Professional Thesis - Nicolas Pierre - Can We Predict Liquidity Shocks on Emerging Fixed Income Markets

ADVANCED MASTER IN FINANCIAL TECHNIQUES

Subject: Can we predict liquidity shocks on emerging fixed income markets?

Presented by

Nicolas Pierre Promotion 2012/2013

Professional Thesis Director

Field Project Director

Carmen Stefanescu

Patrick Temfack

Assistant Professor

Head of

Finance Department

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Risk Department

Nicolas Pierre-Carmignac Gestion

Contents Acknowledgment ...............................................................................4 Executive Summary ............................................................................5 Résumé..............................................................................................5 I Liquidity ...........................................................................................6 I-1 Liquidity Definition ........................................................................................ 6 I-1-A Definition ........................................................................................................................ 6 I-1-B Market Liquidity Stakeholders ........................................................................................ 6

I-2 Liquidity Measures......................................................................................... 7 I-2-A Depth............................................................................................................................... 7 I-2-B Breadth............................................................................................................................ 7 I-2-C Resilience ........................................................................................................................ 8 I-2-D Credit Rating Agency....................................................................................................... 8 I-2-E Other Determinants ...................................................................................................... 10

I-3 Liquidity Management ................................................................................. 10 I-3-A Liquidity Model Establishment ..................................................................................... 10 I-3-B Value at Risk and Stress Test......................................................................................... 11 I-3-C Optimal Position Liquidation......................................................................................... 12 I-3-D Liquidity Management Instrument ............................................................................... 13 I-3-E Liquidity Indicators ........................................................................................................ 13

I-4 Liquidity Case studies................................................................................... 13 I-4-A Amaranth Advisors LLC - 2006 ...................................................................................... 13 I-4-B Northern Rock - 2007 .................................................................................................... 14 I-4-C LTCM - 1998 .................................................................................................................. 14

II Emerging Markets ......................................................................... 14 II-1 Emerging Markets Overview ....................................................................... 14 II-1-A Emerging Markets Definition ....................................................................................... 14

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II-1-B Emerging Markets Integration ..................................................................................... 15

II-2 Previous Liquidity Crisis in Emerging Markets ............................................. 18 II-3 Current global Liquidity Analysis in Emerging Markets ............................... 21

III Emerging Fixed Income Markets ................................................... 23 III-1 Emerging Bond Markets ............................................................................ 23 III-1-A Macroeconomic fundamentals ................................................................................... 23 III-1-B Price discovery ............................................................................................................ 24 III-1-C Volatility dynamics ...................................................................................................... 24

III-2 Emerging market yield spreads (Domestic and external determinants) ..... 24 III-3 Warning indicators of asset price boom bust cycles in emerging markets . 28

IV Liquidity evaluation Tools implemented at Carmignac Gestion ..... 31 IV-1 Evaluation of the necessary time to liquidate an equity portfolio considering level of acceptable losses .............................................................. 31 IV-1-A Under no constraints .................................................................................................. 31 IV-1-B Keeping the same liquidity profile .............................................................................. 32 IV-1-C Liquidate the portfolio in a given number of days ..................................................... 33 IV-1-D Stress Scenario............................................................................................................ 34 IV-1-E Results ......................................................................................................................... 34

IV-2 Liquidity Evaluation of each fixed income assets in portfolio..................... 34 IV-3 Emerging Liquidity Bonds Model ............................................................... 36 IV-4 Future Liquidity project ............................................................................. 39 IV-4-A Market impact model ................................................................................................. 39

Bibliography .................................................................................... 40

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Acknowledgment

I would like to thank all the teams I have been working with for the last 6 months at Carmignac Gestion, who always were ready to spare me some time to help, support and teach and who welcome me very warmly.I would like to particularly thank the risk management team, Mathieu Decrop, Jean-Etienne Gadret, Jean Yves Lassaut, Patrick Temfack for giving me the opportunity to develop my technical and soft skills, perform during this first and outstanding experience in asset management and of course for hiring me at the end of the internship.

However without suitable preparation and theoretical knowledge and skills developed while following the Financial Techniques master degree, I would have had more difficulties to prove myself as a reliable member of my team. I can today say that this master degree set me on the path of fulfilling my personnel objectives. I would then like to thank all faculty members of the master who have participated in my education. Of course I would like to more specifically thank Carmen Stefanescu for assisting me on the following professional thesis.

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Executive Summary Before 2008, liquidity issues were considered to be of secondary importance in most asset management firms. But since the subprime crisis, liquidity has become a very important stake in this industry. Indeed when an asset becomes illiquid, portfolio managers often prefer to take their loss rather than dealing with bigger and bigger spread arising with liquidity shocks. Moreover Regulators keep on specifying new liquidity requirements. More specifically, emerging liquidity risk has become a key question for market finance academics. One of their focuses of research is to find relevant indicators of emerging liquidity crisis. Following the recent liquidity shocks we observed on emerging fixed income markets in June 2013, finding those relevant indicators became also even more critical for asset managers. In the first part of this professional thesis, I will discuss how liquidity can be defined and managed. Then in a second part what are the main characteristics of emerging economies. I will then focus more specifically on the specificity of emerging fixed income markets and notably use the research of market finance academics to determine a list of emerging liquidity shocks indicators. Finally using this list, I will show the models I implemented at Carmignac Gestion to monitor the risk of an occurrence of such liquidity shock. Keywords: Liquidity, Emerging Markets, Fixed Income Markets, Asset Management, Stress Test, Portfolio Liquidation, Liquidity Black Hole Indicators

Résumé Avant 2008 les problématiques de liquidité étaient considérées comme d’importance secondaire par la plupart des sociétés de gestion d’actif. Mais depuis la crise des subprimes, la liquidité est devenue un enjeu prépondérant dans cette industrie. En effet quand un actif devient illiquide, les gérants de portefeuille préfèrent souvent prendre des pertes plutôt que de devoir gérer des spreads de plus en plus grand en conséquence de chocs de liquidité. De plus les régulateurs ne cessent d’imposer de nouvelles contraintes de suivi de liquidité. Plus spécifiquement, le risque de liquidité des économies émergentes est devenu une question clé pour la recherche académique. L’un des axes de cette recherche est de déterminer des indicateurs fiables de risque de choc de liquidité sur les marchés émergents. A la suite du choc de liquidité observé sur les marchés émergents en juin 2013, la détermination de ces indicateurs est de plus devenue encore plus critique pour les gérants de portefeuille. Dans la première partie de cette thèse professionnelle, je m’attacherai à définir la liquidité et comment celle-ci peut être gérée. Puis dans une seconde partie, je définirai les principales caractéristiques des marchés émergents. Ensuite je m’intéresserai plus particulièrement aux spécificités des marchés de fixed income émergents et utiliserai notamment la recherche académique en finance pour dresser une liste pertinente d’indicateurs de risque de choc sur la liquidité émergente. Enfin j’expliquerai les modèles de surveillance de liquidité que j’ai implémentés chez Carmignac Gestion à partir de cette liste d’indicateurs. Mots clés: Liquidité, Marchés Emergents, Marchés de taux, Gestion d’actif, Stress Test, Liquidation de Portefeuille, Indicateurs de Trous Noirs de Liquidité

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I Liquidity I-1 Liquidity Definition I-1-A Definition Liquidity from a market point of view represents the capacity to buy or sell an asset rapidly without having an effect on its price and its volatility. The more liquid a market is, the easier, the faster and the cheaper it is to do the transaction. Assets would be transparent so that their market prices reflect their intrinsic value. Moreover this characteristic is an evidence of whether a market is efficient or not. A perfectly liquid market would then give a single bid-ask spread for every assets traded on, no matter when and irrespective of the volume traded. Cash is supposed to be the most liquid asset since it can be used to perform immediate economic actions such as buying or selling. Nevertheless even cash in major currencies can be subject to liquidity crisis. A stress scenario of a dump of US dollar bonds by China, Saudi Arabia or Japan could lead to a black hole of liquidity (According to Persaud, a liquidity black hole is a situation where liquidity disappears on a market because every market participant want to sell or nobody wants to buy: a decline in price leads more counterparty to want to sell, driving prices way below their equilibrium prices). Liquidity black holes happen when all traders are on the same side of the market at the same time. This behavior can come from the fact that they all hold similar position and manage risk the same way. It is also possible that they are irrational and over-exposed to some risks. In a range of most liquid instruments to less liquid instruments come money market and government bonds, then stocks, corporate bonds and mutual funds (relatively easy to sell but prices are not always guaranteed) and finally structured product and Over-The-Counter Derivatives. The last category of asset are often considered as illiquid : those assets generally do not have a consensus about its value, have important transaction cost, see few transactions per day or even lack of a market where it can be traded (mortgage-related assets are example of illiquid assets despite being secured by real assets such as real estate). When those assets are not quoted, they are evaluated by mathematical models which generally take into account volatility and return but induce a model risk. Covering illiquid Asset with contract requiring margin calls may be problematic. For instance Ashanti Goldfields, a gold mining company covered his production with future contracts. When the gold price rose Ashanti Goldfields had to respond to important margin calls. The losses on those contracts were compensated by the gain on the physical gold but since this asset was too illiquid, Ashanti Goldfiels had to be restructured.

I-1-B Market Liquidity Stakeholders There are different stakeholders which have an impact on the liquidity of a market: 

Speculators and market makers contribute in some ways to the liquidity of an asset or more generally of a market by providing some capital while trying to benefit from anticipated price movement. Their impact on the liquidity depends if they are contrarian or momentum. Contrarian traders buy assets when their price is judged too low creating demand to meet the offer (it is however difficult to follow this strategy for a fund which manages his risk with VaR). Whereas Momentum traders sells assets when their prices are dropping making them unstable and the market illiquid. Several reasons explain the existence of momentum traders: stop-loss rules (automatic sell after a given level of loss), option dynamic cover (long position on put or

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

 

short position on call implies to sell the underlying asset after a drop of its price), synthetic option creation, margin call (if traders have a high leverage and cannot honor their margin call, they have to unbind their position, increasing the price drop), predator trading (Traders short a company that they know in financial difficulty and by doing so strengthen the price drop). Financial institutions and asset managers which are subject to two different kind of liquidity risk: the funding of portfolio assets during the normal course of business and the necessity to find additional funds under stressed market conditions. Indeed even if a market is liquid most of the time, it can become illiquid as soon as a major crisis arises. Generally the stress begins by a drop in price, the bid-ask spread then increases and generally a panic movement follows, leading most investors to want to sell but since books become empty from the buying part, demand does not meet offer anymore. Central Banks can also influence the global liquidity by supplying money (quantitative easing notably). Financial regulation assesses the liquidity of financial institution in order to make sure that the financial institutions would be able to survive those liquidity shocks. It can also determine the market organization (stock exchange or over the counter).

I-2 Liquidity Measures Improving the liquidity of a market aims to reduce the transaction costs, both the explicit one (brokerage commission, bid-ask spread) and the implicit one (unsuitable technology, regulation, price discovery, information, participation…). Those costs depend on many factors such as the number of counterparty, the volume, the bid-ask spread, the price dispersion among the contributors but are difficult to evaluate. Nevertheless liquidity can be measured by several indicators:

I-2-A Depth The market depth which can be seen as the number of units that can be sold or bought for a given price impact or as the price impact for a number of units sold or bought. The market depth shows the capacity of market to absorb sell or buy orders of large amount. On organized market it can be evaluated by the order books size and by the number of investors which may execute on the asset.

I-2-B Breadth The market breadth which is linked to the tightness of the bid-ask spread. This spread compensates the market-maker for taking the risk of holding the position, the asset volatility and the information asymmetry (the counterparty may have privileged information regarding the asset real value). Bid-asks spreads vary with market capitalization and turnover but as shown below small, mid and large caps evolve in a similar manner. Nevertheless small cap bid-ask spreads are wider than mid cap bid-ask spreads, which in turn are larger than larger cap bid-ask spreads. This property results from competition between market markers, which is more intense on more liquid stocks.

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It can be highlighted the fact that the largest bid-ask spreads were observed during the subprime crisis. Market volatility and market bid-ask spreads evolve with very similar patterns since market makers will ask for a larger margin when they face greater market risk (for which volatility can be considered a good proxy).

I-2-C Resilience The Market Resilience which corresponds to how fast prices come back to their equilibrium price after a perturbation. Market Resilience can be analyzed by examining the variance of prices over a given period of time. In practice, liquidity crisis leads to a prompt expansion of the bid-ask spread and then an increase of the short-term volatility. All the liquidity determinants presented above are summarized in the figure below based on the publication of Ranaldo.

I-2-D Credit Rating Agency Credit Rating agencies aim to solve one of the primary problems of financial markets which is the asymmetric information and notably about the credit risk (risk that the debt issuer will not fulfill in time payments of principal and interest over the life of a debt instrument). The cost of producing such information is collected through fees on the issuers of rated securities. This implies that borrowers with relatively small shares can spare the costs which would arise from their own investigation. Since

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international investors generally prefer rated securities than non-rated of apparently similar credit risk profile, credit rating agencies makes easier their access to global debt capital markets. The request for sovereign credit ratings has increased considerably over the last 20 years as shown below for Moody’s. By reducing investors’ information asymmetry, sovereign credit ratings have enabled many governments, even those who had already made default, to gain access to international bond markets.

The improvements in sovereign ratings during the first half of the 1990s accelerated capital inflows while the severe adjustments of sovereign ratings for many emerging market economies throughout the Asian financial crisis of 1997-1998 contributed to the collapse of these inflows. Ratings have especially important weight in emerging markets since investors’ confidence is relatively not very strong. By evaluating the credit quality of some market contributors, Credit Rating agencies give creditworthy entity the ability to enter in a transaction more easily. This is especially important in emerging debt markets where the issuers are not always well known or where international investors may not be familiar with the issuers’ business culture, language or accounting standards. By doing so credit rating agency influence the liquidity of a market. Some assets need in order to be eligible as collateral to achieve a minimum grade, grade which determines amongst other things if the asset is liquid enough. Then an announcement of a downgrade or a grade review can influence directly the market by stressing the assets price on one hand and the asset liquidity on the other hand. Indeed institutional investors have strict policy (either official regulations or internal risk management practices) about the rating of their investment. If one of their investments is downgraded, they may be forced to sell and as they hold generally pretty big position, this will stress as well the liquidity of the asset. The previous induces then more volatility in investors’ behavior. On another hand if the sovereign rating is upgraded to investment grade, it opens a much wider investor base which will result in more stable demand for bonds of that particular emerging market. Until recently, the sovereign rating set a ceiling on the rating that could be achieved by other domestic entities. The idea behind this was that governments have the first claim on foreign exchange reserves to meet their obligations. This is no longer an absolute truth but it still has a major influence on the ratings of corporate bonds. Reisen and Von Maltzan study concludes that sovereign rating assignments intensify boom-bust cycles in emerging markets lending. Their study also points out the fact that the largest rating effects are for emerging market bond spreads, which are usually of lower credit quality (ie rated below investment grade). Kräussl empirical study demonstrates that credit rating agencies have a substantial influence on

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the size and volatility of emerging markets lending. This influence is moreover significantly stronger for government’s downgrade or negative rating watch than for positive adjustments. This impact on financial markets in emerging economies is also more important when it was not anticipated.

I-2-E Other Determinants The number, frequency and volume of each asset transaction per day and the number of market participant are other liquidity determinants. The more the asset is exchanged, the more liquid it is. Indeed if an asset is exchanged very often, it can be turned into cash rapidly. Market turnover in cash is greatly influenced by price levels, that’s why volumes (in number of shares) are used to measure trading activity independently of price effects. Compared with the pre-crisis year of 2007, the period following the crisis experienced a drop in turnover as shown below. This drop was mainly driven by prices as shown by the Stoxx 600 Index while volumes expressed in number of shares were much less volatile.

However despite a predictable phenomenon of volume seasonality whose two typical characteristics are a large drop in the last two weeks of December and another in the second week of August, volumes remain quite volatile. This fact should then be taken into account in order to compute liquidity risk measures.

I-3 Liquidity Management I-3-A Liquidity Model Establishment Liquidity Monitoring took since 2008 a more and more important place in the investment strategies, especially in the emerging environment. It is furthermore particularly difficult to invest big amount of money in small capitalization without making prices move. There is then an arbitrage to make between liquidity and expected return. Some asset evaluation models take the liquidity component into account. Hamon and Jacquillat proposed an extension of the Capital Asset Pricing Model adding a liquidity factor:

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Where:     

E (Rj) is the expected return of the asset, Rf is the risk-free rate Beta is the sensibility of the asset to the market E(Rm) is the market expected return LIQ is the liquidity factor and can be measured by the bid-ask spread, the volume or the number of contributors

The intuition behind this model is that illiquidity can be measured as costs of immediate execution. Then an investor willing to transact at a favorable price faces a trade-off: he can choose to wait to transact at the price he considers fair enough or choose to be executed immediately but at the current bid or ask spread. Transaction costs represent then potentially a future negative cash flow that reduces the asset returns and then investors must be compensated for it and for the exposure to liquidity shocks. Moreover Brennan and Subrahmanyam pointed out the fact that the main cause of illiquidity in financial markets is the adverse selection which arises from the presence of informed traders (phenomenon even more important in emerging markets). Then if an investor is less informed, he should demand higher rates of return as the adverse selection increases (and then the illiquidity). Fama and French proposed a 3-factors model with a liquidity premium

Where:  

SMB corresponds to the size factor (ie the return difference between small and large capitalization) HML connects to the Book to Market Factor (ie the return difference between growth and value capitalization)

This model suggests that stocks with greater persistence in illiquidity have higher average returns. Akbas, Armstrong and Petkova think that the positive correlation between liquidity volatility and expected returns would be caused by investors’ risk aversion. Indeed if there is high liquidity variation, there is then higher probability that the asset becomes illiquid at a time when it must be traded and hence risk adverse investors should require a risk premium for holding stocks whose liquidity is volatile. Furthermore if liquidity is persistent then higher liquidity today predicts higher liquidity on the near future and then a higher required rate of return.

I-3-B Value at Risk and Stress Test Liquidity Stress Test can be made, evaluating the potential losses in case of an adverse shift of one or several market variable. For instance a parallel shift in long term interest rate, downgrade of the US credit rating, abnormal increase of the volatility, a systemic crisis (increase of the correlation between the return of all assets), a bank run,… Value at risk (risk of loss on a portfolio with a given interval confidence and a given interval of time) can also be adapted in order to integrate the liquidity risk by increasing the volatility and then potential losses of illiquid assets and by considering not a normal distribution but for instance a distribution having a negative skew and a kurtosis superior to 3.

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We can also determine the time necessary to liquidate a given percentage of the portfolio. If we take S as

and

the position value, in normal market condition, the cost to liquidate a

portfolio is with n the number of positions. Diversification reduces the market risk but does not automatically reduce the market liquidity risk. Nevertheless the cost of unbinding a large position is greater than for a small position. Holding several little positions rather than one big enables then to reduce the liquidity risk. In stressed market conditions, let’s denote as the average spread and the spread standard deviation. Then the cost of portfolio liquidation becomes: ) * where gives the confidence interval for the spread. For instance if we consider a 99% scenario and a normal distribution, i = 2.33 for each i. This equation implies that all assets are perfectly correlated. This can be considered as conservative but empirically when liquidity is low, Spreads tend to widen for every instrument. Liquidity Value at Risk can be defined as a normal VAR with another component: the exogenous liquidity cost (worst expected spread with a given confidence interval). This exogenous liquidity component is equal in normal market conditions to and to ) * in stressed market conditions.

I-3-C Optimal Position Liquidation A trader who wishes to liquidate a large position must arbitrate between a fast liquidation with large spread or a slower liquidation with tighter spread but bigger risk of losses. Almgren and Chriss worked on the optimization of position liquidation: Let’s assume the size of a position is of V units and the trader decides to liquidate it in n days. We define a function P(Q) which corresponds to the spread when the trader sells a quantity q of the position. Qi refers to the units exchanged on day i and Xi the position remaining at the end of day i with 1 i n. X(i) = X(i-1) - Qi Each transaction cost half the spread. Then the total cost is equal to Assuming that the variation of the middle spread price follows a normal distribution with a standard deviation , the variance of the position value variation on day i is . The total variance of the position value variation during the all liquidation is then The trader wishes to minimize his losses which correspond to minimize its Liquidity Value at Risk:

Under the constraint

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I-3-D Liquidity Management Instrument Some Derivatives have been structured to hedge liquidity risk. Bhaduri, Meissner and Youn quote notably:    

Withdrawal option: A put on an illiquid asset with a strike at the current market price Return swap: swap the underlying’s return for LIBOR paid periodically Return swaption: Option to enter in the previous swap Liquidity option: “Knock-in” barrier option, where the barrier depends on liquidity level

Paradoxically, due to the complexity of some of those instruments and due to the fact that they are often tailor-made, most of them are quite illiquid.

I-3-E Liquidity Indicators Euribor (Euro interbank offered rate), Libor (London interbank offered rate) and Eonia (Euro Overnight Index Average) can be good indicators of market liquidity. Indeed those rates reflect if there is a stress on the interbank market and this stress is very likely to spread to the overall market. Besides those rates depend directly on the BCE refinancing rate, refinancing rate that directly influence the liquidity supply.

The graph above shows the spread between the 3-month libor and the 3-month OIS (overnight indexed swap). It can be highlighted the fact that this spread was especially wide during the crisis on the interbank market (2008) reflecting the liquidity stress that occurred on this market.

I-4 Liquidity Case studies I-4-A Amaranth Advisors LLC - 2006 Amaranth Advisors LLC was an American multi-strategy hedge fund which had 9 billion dollars under management. They built, using leverage, a very large position on the natural gas futures market (around 10%). The concentration of their position was so high that were not enough counterparty to unwind the positions. They turned out to be unable to sell their futures contract at or near the latest quoted price

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and lost around 5 billion on those natural gas futures. According to Chincarini, part of their losses can be attributed to asset illiquidity.

I-4-B Northern Rock - 2007 Northern Rock was in 2007 one of the five first mortgage loan company in the United Kingdom. A great part of Northern Rock financing came from debt instruments sold on the markets. Following the subprimes crisis in August 2007, the bank experienced great difficulties to refinance itself since institutional investors became very suspicious towards Bank that were highly involved on the mortgage market. Northern Rock Assets were sufficient to cover its debt so that the bank remained solvent. Indeed the FSA (UK financial services authority) judges that Northern Rock had the necessary regulatory capital and a good credit portfolio. Since Northern Rock could not access the short-term money markets anymore, it asked the Bank of England for a refinancing of 3 billion pound. On the 13th September a BBC journalist announced that Northern Rock had asked for the help of the Bank of England which created a bank run during which 2 billion pound were withdrawn. The English government had then to guarantee Northern Rock deposit to stop this bank run. Despite this, the bank had to be nationalized in February 2008. In response to this event, the FSA now places greater supervisory focus on liquidity risk.

I-4-C LTCM - 1998 LTCM was a hedge fund created in 1994, fund in which Myron Scholes and Robert Merton participated. Their strategy involved to go short on liquid bonds and to go long on illiquid bonds and then wait for the price convergence of the two bond types. After Russian default in 1998, illiquid bond prices dropped. LTCM and other funds which followed the same strategy with high leverage were then unable to face up margin calls. They had then to unbind their position by buying liquid bond and selling illiquid one. This strengthened the flight towards quality and increased the price spread between those two kinds of assets. LTCM was then bailed out by a consortium of 14 banks in 1998. LTCM strategy was essentially a massive, unhedged exposure to a single risk factor. However LTCM was aware of its liquidity risk. Indeed they estimated that in case of severe liquidity stress, haircuts on bonds would increase from 2% to 10%. They had then negotiated long-term financing with margins fixed on most of their collateralized loans. Nevertheless due to the liquidity black hole that happened, LTCM was ultimately not able to fund its position despite its numerous measures to control liquidity risk.

II Emerging Markets II-1 Emerging Markets Overview II-1-A Emerging Markets Definition Emerging countries are countries whose GDP per resident is inferior to the one of developed countries but which experience a fast economical growth and whose standard of living and economical structures converge to the one of developed countries. The emerging theme corresponds to the appearance of new characteristics, breaking with the former model: increase in production activities and economy diversification. Among those new characteristics, we can quote: legal and institutional renovation, passing from agrarian to an industrial organization, opening market for products, services and capital, an expanding middle-class, social stability and tolerance, innovation. Those countries invest more and more

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abroad: 117 billion dollars in 2005 (17% of the worldwide investment) against 10% in 1982. Taiwan is for instance the first investor in China. At the beginning of 2010, around 60 countries could have been considered as emerging countries. Together they represent around 50% of the wealth created in the world and two thirds of its population. Among them the BRICS (Brazil, Russia, India, China and South Africa) are the main actors but Indonesia, Mexico and Turkey are also very important actors. The four Asian dragons (South Korea, Hong Kong, Singapore and Taiwan) former emerging countries are now considered as developed country. The emerging economies can be gathered in function of its GDP per resident: PIB/resident in $

Middle East and North Africa

Asia

Eastern Europe

> 50 % Euro Zone

Kuwait (31640)

Singapore (32470), South Korea(19700), Taiwan (17930)

Slovenia (21000)

> 20 % Euro Zone

Saudi Arabia (15440)

Turkey (8020)

South Africa (5930), Algeria (5000), Morocco (4320), Tunisia (5320), Kenya (1820)

Malaysia (5620), Thailand (3050)

Egypt (1580)

China (2360), Indonesia (1420), India (820)

LATAM

Mexico (7830)

Argentina > 8 % Euro Zone (6050), Brazil (4710)

> à 2 % Euro Zone

Bolivia (1260)

Bulgaria (11800),Hungary (11570), Slovakia (9610), Poland (8210),

Russia (5770), Albania (2930)

This table shows us that there are a lot of different realities behind the term emerging markets.

II-1-B Emerging Markets Integration Market integration relates to low barrier to trading in goods and services as well as to free access of foreigners to local capital markets and reciprocally. However regulatory liberalization does not necessarily result in market integration especially if foreign investors do not believe the reforms will last

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long or if other market imperfections persist. A strong drop in dividends yields reflecting a permanent price increase, an increase in equity capital flows by foreign investors or the quality of investor protection and accounting standards could be seen as a sign of effective market liberalization. Market integration is a gradual process and depends on the barriers to market investment (information asymmetry, liquidity, political, economic policy and currency risk). And it is rather unlikely that those barriers will disappear at the same time.

Average annualized S.D. Data through April 2002.

The chart above is given by Bekaert and Harvey. Out of the 19 other countries, 9 experienced a decrease in volatility and 10 an increase. For instance in Argentina, the sharp decrease in volatility was due to economic policies that fought hyperinflation. So this cannot prove significant changes in volatility going from a segmented to an integrated capital market. In theory, market integration does not necessarily imply higher correlations with the world, for instance if the country has an industrial structure different from the world’s average structure. But empirically:

Correlation with the MSCI world market return. Data through April 2002.

The chart above shows that on average, market integration leads to higher correlation with the world. Empirical evidence also shows that correlation among emerging markets and beta with respect to the world market increases as well. The theory tells us that increased correlations reduce diversification

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benefits. Nevertheless correlations of emerging asset returns remain low enough to keep providing diversification gains. According to Harvey, both post and pre-liberalization return are not normally distributed. They are indeed skewed and have fat tails. Portfolio decisions must then take into account the third and fourth moment.

Emerging markets are relatively inefficient due to relatively low volume of transactions, returns being less impacted by company announcements and insider trading existence. The traditional Capital Asset Pricing Model gives for emerging markets a yield that is generally too low. This yield can be modified adding the spread between US treasury yield and US dollar denominated government bond yields of the same maturity. Arouri, Nguyen and Pukthuanthong found that most of the following countries: Brazil Chile, Malaysia, Mexico and the Philippines have seen their market more integrated following liberalization and structural reforms. They also showed the dominant role of emerging markets risk premium. In fact the more the markets integrate themselves into the worldwide economy, the smaller the risk premium and then the lower the international cost of capital. Bekart and Harvey showed that after market liberalization, the trade balance worsens showing that a more important part of investment is financed by foreign capital. There is however no evidence that this will imply an increase in local consumption. But still the real GDP increases (on average by 1% per year over the five years following the liberalization). This growth depends on many factors. Among them we can quote, the level of school enrollment, the state of development of country’s markets.

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II-2 Previous Liquidity Crisis in Emerging Markets Principal emerging liquidity crisis happened in Mexico in 1994-1995, East Asia 1997-1998, Russia 1998, Brazil 2000 and Argentina in 2002. In all cases, these crises were associated with big negative movement in both asset prices and macroeconomic fundamentals in the affected economies. For instance, as shown below, between December 1994 and March 1995, Mexico’s stock market fell by 26% in terms of pesos, the peso depreciated by around 50% (according to Aguiar, Fernando and Broner, this was due to loosening of monetary policy since the monetary authorities had been trying to defend the peg by raising interest rates and due to worsening in credit market conditions) and peso interest rates rose to more than 70% in annualized terms (this dramatic rise in interest rates had also for aim to deter speculation and to reduce excessive depreciation and inflationary pass-through). The same pattern has been observed during the Asian, Russian and Brazilian crisis.

There has been observed high correlation between markets during those crises. In fact Forbes and Rigobon have demonstrated that it can be expected higher correlation when volatility increases. Kenourgios and Padhi study shows that the Russian default and the subprime crisis hit emerging financial markets independently of their market integration since cross-market correlation dynamics are driven by investors risk aversion whose changes had impact on emerging countries financial structures. Another government default event, Argentine crisis was due to weak macroeconomic fundamental and financial vulnerability of emerging economies to shocks. A situation of international illiquidity arises when the country’s consolidated financial system has shortterm obligations in excess of short-term accessible foreign currency. There were several reasons invoked to explain emerging liquidity crisis : financial liberalization, increased inflows of foreign capital, corruption, lack of transparency, imperfect democracy, misguided investment and loan guarantees, exchange rates policy (pegged for too long or not long enough), external deficits (too large or too small), poor financial regulation… However an emerging crisis does not necessarily need illiquidity to happen. The troubles of early 1990s were more linked to government wishes to fight unemployment than to the access to short-term liquidity. However illiquidity is generally a sufficient element to trigger an emerging crisis because as soon as creditors lose confidence, they stop lending money to the emerging private sector or to the government, putting them in great financing difficulties (same phenomenon observed as in a bank run). Policy distortions are also likely to amplify the effects of adverse shocks.

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Kaminsky and Reinhart showed high correlation between currency collapses and liquidity banking crisis. Indeed in Latam countries in the years 1980s, Scandinavia in 1990s, Mexico in 1995 and Asia in 1998, the exchange rates crashed along with the financial system (domestic bank, other domestic financial entities that perform banking operations, central banks) due to the fact that the financial system play an even more important role in emerging than in developed countries. Emerging markets having less access to world capital markets, they can run out of liquidity a lot faster as soon as local banks face liquidity issues. Asian countries had in 1997-1998 high and strongly ratios of currency short-term liabilities (external debt notably) to liquid assets. They were highly vulnerable to liquidity crisis: indeed capital outflows (which occurred in Asia in the second half of 1997) lead to bankruptcies, payments default and collapse in asset prices (both exchange rates and domestic money price). Financial panics began, amplifying the illiquidity of domestic financial institutions causing another wave of asset liquidation and thus price devaluation. To counter it, Malaysia imposed capital controls that aimed at eliminating the possibility of foreign capital flight. The table below from the BIS data shows that the international liquidity position of Korea, Malaysia, Philippines and Thailand deteriorated before the crisis:

The ratio displayed above has decreased in Philippines between 1990 and 1994 (Philippine Brady’s plan debt restructuring). But the ratio is above one for Korea, Indonesia and Thailand which reflects the fact that international reserves would not have been enough to repay short-term debt in case of liquidity stress. Latin countries however appear to have been in a less vulnerable position in 1997. The behavior of domestic deposits relatively to international reserves is represented in the table below. The high level of the ratio confirms for the Asian countries what we state before (high illiquidity risk in 1996).

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It can be highlighted that this multiple was for Mexico in 1994 of 12.63 which coincide with Mexico own crisis. In fact Mexican government’s inability to fund its large stock of short-term debt (the tesobonos notably) was the catalyst of the financial crisis in December 1994 (foreign investors reduced their holdings in Mexico but were preceded by local investors that had information in advance). However this ratio overestimates international liquidity risk for countries which adopted flexible exchange rates system (Peru for instance). Indeed in case of a liquidity stress, central banks can always, under this exchange rate system, print domestic currency to honor the deposits in domestic currency. Aizenman and Hutchinson state that before the 2008 financial crisis, emerging countries were the source of most of the worldwide growth and countries where most of the global population lives. Furthermore before the crisis, the speed at which emerging countries connected financially to OECD countries was increasing dramatically relatively to less developing countries. While OECD countries had access to large dollar swap lines by the FED and thus could meet excess demand for dollar liquidity and then deleveraging pressures, most emerging markets did not have access to those swap lines and were then made to adjust abruptly to the excess demand for dollar liquidity. The table below shows changes in the exchange rate depreciation and in loss of international reserves during two phases of the subprime crisis. Change in exchange market pressure is the sum of those two previous changes.

It can be highlighted how important were exchange market pressures on most emerging markets during July 2008 to February 2009, especially on Poland and Russia. With the exception of Venezuela, all of the country above with positive change in exchange market pressures experienced FX depreciation on this period (Poland by 79%, Brazil, Korea, Mexico and Russia by around 50%). The pressure was however lower for countries with relatively higher income. The financial crisis was global but hit more Eastern Europe and Central Asia since the crisis originated in US was rapidly transmitted to Western Europe through financial institutions and then to economy whose banking system was the most linked to western European banking institutions (ie Eastern Europe and Central Asia).

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II-3 Current global Liquidity Analysis in Emerging Markets The size of the emerging corporate bond has reached as of end of 2012, 1300 billion dollars, exceeding the size of emerging government bonds (680 billion USD). And this size for emerging corporate bonds keeps on growing each year since their financing needs keeps on increasing along with their country development. This effect is amplified by the fact that most banks reduce their balance sheets, inciting companies to seek financing towards financial markets (especially since 2009). The diagram below compares the number of corporate debt emission in 2011 and 2012 and show the increasing number of debt emission.

Moreover the number of contributors to emerging markets has increased as well and has even enabled some funds invested on emerging markets to offer a daily liquidity to their investors. The overall credit quality of emerging bonds has also improved. All of those elements contribute to improve the liquidity of those bonds. Beyond the economic fundamentals, it is the liquidity that determined emerging debt short-term performance whether it is issued in local currency or dollar. At the beginning of 2013, we were on relatively high level of liquidity (as shown in the table below) driven by the quantitative easing program of the FED.

But on the 19th June, Ben Bernanke announced that the FED would progressively reduce the liquidity supply. Ben Bernanke wants to clean up the bond markets (speculative bubbles in the high yield) before

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letting the economic recovery make the task any harder. Following the same idea, the Chinese central bank chose to correct the credit excessive expansion before it becomes impossible to regulate the imbalances (poor asset allocation notably) created without troubles. The reserves that US banks hold within the FED have multiplied its balance sheet by 5 since 2008. Nevertheless this did not lead to inflation pressure since the banking system did not transform this liquidity flow into loan to the economy. This will however be the case once the banking system will have recovered, leading to inflation and then to a bond crash which the FED wants to avoid. This explains why it took the decision to slow down its quantitative easing program. Moreover the decrease of available liquidities to invest in the emerging world led to capital repatriation which paired with the Chinese slowdown, strongly affected emerging currency, equity and bonds prices and created a liquidity stress on those assets even if Japan central bank explicitly plans on keeping on buying foreign assets. And all of the above despite the fact that contrary to the situation in 1997 when the emerging world was short-term financed by foreign investment, was mostly under a fixed currency system and had a weak amount of foreign reserves, today emerging countries have globally better monitored their foreign liabilities exposition (around 30% of the GDP for south-east Asia), most of them have a floating currency system which enable them to devaluate in order to handle exchange rate pressures and most of them have relatively important foreign reserves (16,46 and 47% of the GDP for India, Malaysia and Thailand against 7, 19 and 18% in 1997). According to Natixis economic research department, following the events described above, two countries are more specifically highly exposed to a liquidity crisis: Ukraine and Turkey. On one hand Ukrainian government have dangerously weakened the currency reserves (2.7 months of import) and kept opposing itself to do the necessary reforms in order to preserve the household cheap cost of energy. On another hand Turkey has more currency reserves (6 months of import) and has committed to structural reforms aiming at improving the country potential growth (retirement plans reform and new capital markets regulation notably). Moreover Turkey financing needs reduced while Ukraine’s increased. However those two countries have a high ratio of short-term debt to currency reserves (graph below).

Those ratios suggest that in case of liquidity stress, Ukrainian currency reserves would not be enough to cover the short-term debt. Moreover both Ukraine and Turkey face increasing external trade balance deficits. In Ukraine, despite a relatively constant flow of foreign investment, those deficits are likely to increase since Ukraine lacks of exportation competitiveness and has a high demand for imported high tech products. Ukraine Central Bank would then spend its currency reserves, increasing its ratio of short-term debt to currency

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reserves. Turkey trade balance situation is even worse since its deficits are increasing while the foreign investments decrease. Before the subprime crisis, there were relatively strong economic indicators in the Europe and MiddleEast Region (high growth rates, important exchange reserves, balanced budgets), but it was not enough to immune those regions from the crisis. Turkey and Ukraine present then an important liquidity risk and are highly exposed to the future end of the FED quantitative easing program (capital leak) and will have to make a lot more effort to keep attracting foreign capitals. However Turkey is adjusting better to this new environment than Ukraine and presents then a relatively smaller liquidity risk than Ukraine.

III Emerging Fixed Income Markets III-1 Emerging Bond Markets Emerging bond markets regroup bonds issued by emerging countries. Emerging bonds issuance has especially increased since the advent of the Brady Plan in the early 1990s. This market was mainly constituted of government bonds since emerging corporate tends to borrow more from banks than from financial markets which requires developed markets and relatively large borrowing needs but recently as we showed before the tendency is reversing. Government bonds have historically been issued in foreign currencies (Dollars or Euros) but recently the development of pension funds in BRICS countries have led to increasing issuance in local currencies. Emerging debt has on average a lower credit rating than mature economies debt because takes into account the economic and political risk, information quality and liquidity. In fact most emerging debt is rated below investment grade. However some countries have seen their grade upgraded to BBB or A. Moreover during the 2010 European sovereign debt crisis, credit spread seemed to show that some emerging countries presented less default probability than some European countries due among others to the fact that emerging countries debt to GDP ratio is equal on average to only 34% and is falling. Haque and Hal investigated the economic determinants of emerging countries creditworthiness for sixty developing countries and found out that economic fundamentals such as the ratio of non gold foreign exchange reserves to imports, current account to GDP and inflation explain a great part of variation in credit rating. Moreover an increase in international interest rates affects adversely their ratings, independently of their domestic economic fundamentals. While stronger growth typically hurts the performance of bonds in the developed markets, the opposite is true in the emerging bond markets (for emerging countries, higher expected real GDP growth and forecasted improvements in the current account balance reduce the spread).

III-1-A Macroeconomic fundamentals Macroeconomics fundamentals include exchange rates, interest rates, exports, inflation, oil prices, foreign exchange reserves, foreign direct investment, the degree of financial development, current account….According to Fleming and Remonola, since macroeconomic fundamentals are the main factors of the benchmark curve, government bonds are the asset class that is the most expected to adjust to macroeconomic news. This is confirmed empirically along with the foreign exchange and this is more pronounced than for other asset classes such as equity. The impact of the exchange rate is consistent with the view that exchange rate pegs raise spreads. Indeed this reflects the fact that this kind of regime is not likely to last long and once it will be demised, a rise in inflation will follow. Romer furthermore demonstrated the fact that more open economies are more prone to inflation.

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Robitaille and Roush showed effects of US inflation and interest rate news on Brazilian bonds spread for up to two hours after their release. And the price impact on the Brazilian market is close to the one that the news has on the US bonds market. Nowak proved that for Brazilian, Mexican, Russian and Turkish Bonds, the immediate response to macroeconomic news is close to the one of mature markets in the matter that it is almost instantaneous. News is indeed absorbed within five to 10 minutes by the markets. Global and regional news tends to be as important as local news for emerging bonds markets showing that macroeconomic fundamentals explains a lot about the performance of foreign-currency denominated emerging market assets.

III-1-B Price discovery There is two types of market reactions to announcement : the repricing which refers to a shift in asset prices due to the consequences of the news in the evaluation of the asset fair value (the price would have already moved before the announcement following investors expectations but will be rebalanced if there is a surprise component in the news) and the repositioning which corresponds to the fact that following the news, investors rebalance their portfolio accordingly to their risk profile, increasing trading activity (for emerging market bonds, this process is however more drawn-out). But since there is an information asymmetry between investors and then differences in the news interpretation, there is also volatility at a relatively strong level for a consequent period after price discovery. However it seems that market reaction weakens when news are released at random times during the day or are unexpected as shown by the Russian bond markets.

III-1-C Volatility dynamics As we state before, price volatility is generally found to remain high for quite a long period in response to news and according to Nowak, it is about two times longer in emerging markets than in mature markets. This is due to the fact that emerging markets absorb new information more slowly than developed markets. There are several explanations to this fact. One is that the lower liquidity makes dealers concentrate their trades. Another is that there is more information asymmetry in emerging markets leading to more different interpretation of the news and then more volatility for a longer time. There is moreover an asymmetry in the news effect since markets react stronger to bad news than to good news and this effect is even larger when the news is surprising (true for US macroeconomic data notably rather than local news).

III-2 Emerging market yield spreads (Domestic and external determinants) The yield spread between emerging market bonds over U.S. Treasuries is supposed to compensate investors among others for assuming a greater default risk. Hong G. Min found out that strong macroeconomic fundamental of an emerging country such as low domestic inflation rates, improved terms of trade are associated with lower yield spreads whereas higher yield spreads are linked to weak liquidity variables (high debt-to-GDP ratio, low ratio of foreign reserves to GDP, deteriorating trade balance…). The volatility of bond spreads is affected both by liquidity and macroeconomic factors and is highly correlated with the debt to GDP ratio, the foreign currency reserves to GDP ratio and the domestic inflation rate (also confirmed by Siklos study). We have an inverted yield curve when an increase in demand for short-term drives up short-term interest rates while long-term interest rates does not increase as fast. This happened in the 1990s with a surge in short-term borrowing in Korea,

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Mexico and Thailand. Ratios of short maturity debt to total debt were much higher than those of developed countries back then. And this led to a negative yield curve in the international bond market in the 1990s. A default is whether due to short-term liquidity issue or long-term insolvency which then affects the yield spread determination. Assuming a lender with a neutral aversion to risk, Edwards determined the following model for bonds spread determination:

S is the spread on bonds, I* the risk free rate, Xi economic variables of the default probability, βi the relative coefficient and εi the error terms. Xi variables measures the domestic and external economic performance of a country as well as exogenous shocks that affect liquidity and solvency of this country. Those variables can be classified into three groups: 

Liquidity and solvency variables

Lower export income or higher import expenses decreases the foreign currency reserves and then increases the probability of short-term liquidity issue whereas a growth decrease can lead to long-term insolvency and then lower creditworthiness ratings. Current account deficit is equal to the increase in a country’s liabilities, degrading the debt ratio and increasing the probability of default. Thus investors ask more return to compensate for the increased risk which results in higher spreads. 

Macroeconomics fundamentals

Yields generally impact the long-term insolvency of a country. Yields are the results of the monetary policy and inflation is an indicator of the effectiveness of this policy. An increase in the inflation rate results in a decrease of the yield spread. The real exchange rate can be used to measure the trade competitiveness of a country. In fact Cline showed that this was inappropriate exchange rate policies that lead to the most important debt crisis. If the currency appreciates, the country becomes less competitive, its trade balance worsens, leading to over borrowing and then affecting adversely the yield spread. In the case of Latam countries, this was an overvalued currency that led to capital flight and then short-term liquidity crisis. 

External shocks

International interest rates have been key factors in the explanation of capital allocation in the 1990s. An increase in interest rate affects the cost of new financing but also the existing debt that has a floating rate. Globally it should have a strong impact on the borrower capacity to repay its debt and even to find the necessary funding. The two oil shocks were oil price increase and caused world recession as well as an increase in the capital demand of oil importing countries, implying higher yield spreads. But for emerging countries, rising oil prices can also be seen as a consequence of rising economic activity which would reduce bond spreads and narrowing the gap relatively to US yields. However Hong G. Min concludes his analysis by saying that those two kinds of external shocks are found to be insignificant for the bond spreads determination. External shocks with no economic cause such as natural disasters or

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terrorist attacks cannot be neglected. Indeed in the aftermath of such events, global economic imbalances or downturns can arise as demonstrated by the history of the last decade. The subprime crisis was characterized by three types of shocks: a collapse of global trade which caused a major shock to demand for companies that wanted to benefit from participating in expanding global trade and production, a credit supply contraction which restricted the company’s access to funding and decreased their effective debt capacity, and selling pressures in equity markets as investors scrambled to meet margin calls and made redemptions to make up for losses in the US market. The results of Siklos’ study states that this crisis raised yield spreads globally with the exception of Asia and Africa (this suggests that emerging markets cannot be treated as only one entity). However there are a few common determinants of emerging yield spread such as the central bank transparency (While interest rates on the long end of the yield curve are driven by the forecasters expectations about the future state of the economy, interest rates on the short end of the yield curve are driven by the influence of central bank’s monetary policy as a reaction to present and anticipated developments of the economy), changes in risk aversion due to onset of the worldwide financial crisis (which leads to increased emerging bond spreads even if the emerging countries showed improved economic performance), volatility which can be proxied by the VIX Index (Average of implied volatility of call and put on the S&P500)). Improved central bank transparency has proven for every emerging market except Asian to lead to higher spreads. The intuition would be that it would result in the opposite effect along with greater clarity and improvements in monetary policy but it can actually be explained by the fact that spreads were underestimated when monetary authorities were more secretive. The graphic below plots the VIX index. It is generally considered that values below 20 is consistent with investors’ appetites for risk while values superior to 30 are considered as a signal for turbulence which will probably raise investors’ aversion to risk. Over the period 2002-2007 investors were relatively risklover but by early 2008 the VIX jumped to 80 and the markets became turbulent ever since despite the decline of the index afterwards.

Increasing foreign direct investment lead to increasing exchange rate pressure (risk of a FX adjustment) reflected in higher spreads while those same pressures decrease as the VIX increases (except for Asia). Indeed according to Bernanke, Asia is known for having large amount of foreign exchange reserves as well as being one of the main sources for worldwide savings. As the VIX reflects risk in US markets, an increase would be expected to make emerging debt more attractive thereby reducing spreads. Emerging markets have large sensitivities to US shocks and US monetary policy was found to impact mainly LATAM and Asian economies. Moreover trade and financial links between countries as well as proximity to the source crisis is determinant in the crisis contagion (according to Forbes and Rigobon:

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significant increase in cross-market asset linkages following a shock to an individual country, or groups of countries). In the case of Egypt, Jordan, Morocco, Kuwait and the UAE’s capital markets, the subprime crisis has shown that greater global financial integration coupled with the formation of a real estate bubble can increase vulnerabilities and create systemic risk. Before this crisis, those economies were growing at a fast rate with a strong will to financially integrate themselves both regionally and globally. Complex financial instruments were designed and traded, innovative securitization techniques were adopted and inappropriate incentive structures were introduced, leading to the emergence of an environment of excessive risk taking which combined with high liquidity and credit growth resulted in real estate bubble and enabled the crisis spread into those economies. However if an emerging country has strong economical fundamentals, it can be protected from the crisis contagion despite the fact that there is strong evidence that a crisis originating in a developed country has generally spillover into emerging markets. Moreover according to Kenourgios and Padhi, contagion implies that diversification benefits which are sought by investing in international markets, are likely to be lower in case of a crisis. The graphics below plot yield spreads for LATAM (top left), European (top right) and African and Asian Countries (bottom left) while bottom left graph shows CDS data. The shaded area (yellow and purple) reflects period during which some of the countries spreads displayed were affected by a financial crisis (subprimes, russian, argentinan crisis). It can be highlighted than the subprime crisis had relatively important impacts on LATAM and Europe (strong spread increase).

Siklos study demonstrates that yield spreads are narrower in less corrupt emerging economy with the exception of Asian and Latam markets where the improvement in corruption indices actually produced higher spreads. Moreover the same study concludes that improvement on fiscal independence reduces slightly the spread (except for LATAM countries).

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III-3 Warning indicators of asset price boom bust cycles in emerging markets An important number of emerging market economies have been prone to shocks such as financial crisis, since the fast rise of emerging asset prices have significantly contributed to the pre-crisis overheating of these economies. Policy makers have then been seeking to implement a system of warning indicators that would help to identify imbalances in emerging asset markets. The challenge is that macroeconomic variables used as warning indicators should be different for the developed and emerging markets. For instance an excessive credit growth that would result in an asset price bubble in a developed economy could correspond to the convergence of an emerging underdeveloped banking sector to a level concordant with the one of industrialized countries. Joyce and Nab study shows on one hand that in the absence of a bank crisis, a sudden stop of inflows would not by itself cause investment to decline. On another hand the more open an economy is to capital flows, the more severe is the impact of banking crises (notably due to short duration capital flight) on investment. Impacts of a sudden decline in the financial account of the balance of payments appear to happen through the bank crisis channel. This implies that a strong banking sector which can withstand any international capital outflows is very important for countries that wish to liberalize financially speaking and policy makers must fix this strength accordingly to the degree of liberalization. Indeed many banking crises have been preceded by financial liberalization. Kaminsky and Reinhart notably found that in 18 of 26 emerging banking crises they study, the financial sector had recently been liberalized. This fact has mainly been attributed to poorly designed banking systems. In fact following the financial liberalization, the capital stock is relatively low so that the marginal product of capital is high even if productivity is low. The local banks foreign competitors do not know the relative emerging market and then only lend at high interest. As a result, expected returns on debt instrument are high. Recently liberalized banks enjoy at first high profits and see their net worth rise steadily. But as the time goes, capital stock grows as well and the marginal product of capital decreases. And to this phenomenon can be added the fact that debt interest rates lowers as foreign lenders gains experience in the emerging bond market, leading local banks to progressively lose their competitive advantage. At that point risky behavior and banking crises become more likely if local banks cannot retain sufficient competitive advantage. This is confirmed by Daniel and Jones model which reveals that even if an emerging banking system has good long-term properties, most emerging countries will enjoy after liberalization an initial period of fast, low-risk growth and then enter a time of high risk of banking crisis. This transition period length depends on the degree of foreign competition, the marginal product of capital and the bank own net worth evolution. Some similarities are generally observed in the way currency crises unfold in emerging economies: a loss of foreign exchange reserves (which can constitute a good indicator since it is detectable some quarters before the crisis), capital outflow and a sudden depreciation of the currency. Emerging currency crises may happen without a worsening of fundamentals and without inconsistent policies. However Burkart and Coudert study shows that overvaluation caused by long periods of pegs increases the currency crisis likelihood, the same being true for high short-term debt and inflation. Currency crisis literature also identified a drop in credit and balance sheet effects of large devaluations as channels through which those crises affect real economic activity. Indeed Hale and Arteta found a decline in foreign credit to emerging market private firms of about 25% in the first year following large currency depreciations. Around 25% of this decline can be attributed to “credit crunch” (firms are interested in obtaining credit, but are either unable to do so or find the cost of credit prohibitively high) while the rest results to contracting demand. After 6 months however the credit decline can be linked to supply effects. Currency crises may lead foreign investors to be less willing to lend, which is likely to result in an increased risk

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premium that all the companies in the affected country are charged by foreign investors. In order to deal with those increased costs, companies are likely to choose to borrow less from foreign creditors and switch to other types of financing. Komulainen and Lukkarila study shows that an increase in private sector liabilities, public debt, foreign liabilities of banks, unemployment or inflation raises the probability of a currency crisis. Moreover fluctuations in the US interest rates influence also the probability of currency crisis. Indicators of indebtedness become stronger in predicting emerging crisis in the phase following capital account liberalization since issuance by the governments of large amount of financial liabilities can easily lead to sudden capital outflow and thus currency crisis. If this liberalization is coupled with an intermediate exchange rate system (nor totally pegged, nor totally floating), the emerging country will be even more vulnerable to currency crisis. According to Komulainen and Lukkarila, currency crisis tends to occur in emerging market around 2 years after domestic financial sector liberalization and 4 years and a half after capital flows liberalization. Overheating growth rates of real sector variables are also difficult to identify since the emerging economy experience substantial transformation and relatively strong volatility of their asset prices. Posedel and Visek found for some economies a strong relation between house price movements and other macroeconomic fundamentals (especially in Eastern Europe) and notably that an asymmetric house price adjustment to macroeconomic fundamentals has led to house price boom in some emerging economy. The graph below identifies boom/bust phases in the Russian housing prices. It can be highlighted that the Russian financial crisis of 1998 and the subprime crisis coincide with real estate bust phase.

On one hand, according to Ponomarenko study, credit growth, investment and capital flow ratio have proven to be reliable indicators for forecasting asset price cycles. On another hand Frankel and Saravelos have found real exchange rate appreciation and international reserve growth to be the most efficient indicators of financial crisis. Aizenman and Hutchinson found for their part that emerging economies with higher ratio of total foreign liabilities to GDP were more vulnerable to financial shocks. They furthermore pointed out the fact that higher short-term foreign debt to international reserves is significantly correlated to currency depreciation as the international reserves are spent to handle exchange market pressures associated

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with those shocks. During liquidity crisis, if most of an emerging country’s assets are illiquid or subject to valuation effects, those assets will have limited capacity to cover exposure to short and intermediate term foreign debt. Then liquidity mismatches will result in deeper exposure and adjustments possibly leading to a prolonged and major crisis. Even if emerging markets had quite large amount of foreign reserves during the period preceding the subprime crisis, this would not have been enough to cover their entire external portfolio liabilities. Then those markets relied first on exchange rate depreciation rather than reserve losses to absorb exchange market pressure. The fear of losing reserves became then stronger than the fear of floating since losing reserves too fast may result in a run on the remaining reserves while uncertainty about crisis duration implies the need to save reserves for dealing with future exchange market pressures. Moreover in time of crisis, a currency depreciation which would result in higher inflation is mitigated by the global recession and would enable to gain competitiveness at times of collapsing export demand. Aizenman and Hutchinson found out that emerging markets having an important part of commodities in their export trade balance are more prone to use foreign reserves and to try to limit exchange rate depreciation when dealing with exchange rate pressures. This results from the fact that commodities are priced globally and then commodities exporters do not benefit from currency depreciation. They also found out that countries with lower pre-crisis real exchange rate appreciation, lower ratio of external debt to GDP and higher ratio of foreign reserves to GDP were submitted to lower exchange rates pressures when the crisis reaches their economy. The policy rule by Taylor displayed below, where r corresponds to the federal funds rate, p the rate inflation over the previous year and y the percent deviation of real GDP form the expected one, gives indication about which economic variable are taken into account for determining the monetary policy.

This formula suggests that policy makers do not only take into account backward looking data in order to determine key interest rates but also their expectations about inflation and growth rates. However in time of financial crisis, forecasting the future monetary policy of central banks becomes a lot harder as shown by the fact that according to Kunze,Kramer and Rudschuck the spread between the minimum and maximum forecasted rate widens. Hence the widening of this spread can be used as an indicator of a coming financial crisis as it also reflect distrust among market participants on the interbank market. The opposite is true, Kunze,Kramer and Rudschuck found that expectations of better economic conditions leads this spread to narrow. Steven Block interested himself to the link between political conditions and currency crises in emerging markets. His study notably concludes that right-wing governments are relatively less vulnerable to currency crises. Strong governments (the one who have large legislative majorities and who face fragmented legislative opposition) are also less likely to endure a currency crisis (this benefit may come at the cost of considering competing policy objectives favored by minority parties). Democracy also makes emerging markets more resilient to currency crises since either greater public accountability leading to better policies or greater political stability increases the likelihood of a good outcome. Then emerging markets perceived as weak (small majority facing a unified opposition) or being left-wing or less democratic face greater currency market risk and are moreover more vulnerable to speculative attacks. Yet negative market expectations based on the latest factors could be mitigated by other signals sent by government officials such as communication on intentions to defend exchange rate pegs and fostering of the credibility of those intentions by implementing responsible fiscal and monetary policies..

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IV Liquidity evaluation Tools implemented at Carmignac Gestion Following the run towards quality which began in May 2013 and the liquidity shocks that followed on the emerging assets, fund managers but also regulation asked the risk department to focus more our quantitative research on liquidity monitoring and forecasting of the assets in portfolio. In fact in May 2013 the ESMA (European Securities and Markets Authority) issued a consultation paper on reporting obligations for alternative funds. These guidelines should be applied in 2014 by investment bank and it was assessed to be very likely that those guidelines will be required for UCITS funds in the next regulation review. The liquidity reporting will need to: “report the percentage of the fund’s portfolio that is capable of being liquidated within each of the liquidity periods specified. Each investment should be assigned to one period only and such assignment should be based on the shortest period during which such a position could reasonably be liquidated at or near its carrying value”. This reporting frequency depends on the fund’s AUM (quarterly, bi-annual or annual). I more specifically worked on three models which were dedicated to the two main classes of assets we hold in portfolio: bond and equity.

IV-1 Evaluation of the necessary time to liquidate an equity portfolio considering level of acceptable losses The primary aim of this model was to implement different scenario of portfolio liquidation under different constraints. The three main inputs are the position (P), the market values (MV) and the three month average volume of transaction (V). Private equities, for which average volumes are not available, are considered to be immediately liquidated but with a haircut of its market value (defined by the model user). I had to make some assumptions such as considering the volume of the underlying for participatory note and taking the absolute value for the inputs of short positions. I then calculated the time to liquidate (T) the position with the following formula:

Where F corresponds to a percent of the average daily volume we will be able to use when liquidating our portfolios (defined by the model user). I then calculate the weights (W) of each asset in terms of markets value (MV) in the portfolio as well as the percentage of a position that can be liquidated in one day (1/T). Once this work is done, we can simulate the liquidation of the portfolio:

IV-1-A Under no constraints Under no constraints, the remaining percentage at a given day (d) of each asset in portfolio is simply:

W(0) =1 ) * W(d-1), 0]

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IV-1-B Keeping the same liquidity profile The problem which arises (as shown below on the blue line) is that without any constraints, as the liquidation goes, some assets (the illiquid one) begin to have a more and more important weight in the remaining portfolio.

In order to keep the same liquidity profile during the portfolio liquidation, I added the two following constraints:  

Not liquidating more than a percentage (L) of a position each day and after a day of liquidation, Not having more than a percentage (V) of a position in terms of market value in the remaining portfolio.

We then have

W(0) =1 ) )* W(d-1), 0]

W(d) Max Max = Weight(asset) MaxLine = EquityLine End If Next Equity ‘We liquidate every other asset more than the one with the highest weight in order to meet our constraints: While Max > V And Diminution > 0 For each equity in portfolio except the one where we had the equity maximum weight ) )* W(d-1)* Diminution, 0] Diminution = Diminution - 0.1 Wend Next Liquidation day

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There was then an arbitrage to be made between the step of the diminution and the speed of the problem solving (the smaller, the slower but the more precise). To determinate the optimal step for the diminution I calculated the tracking error (TE) of each asset weights (n assets) relatively to their original weight in portfolio:

With a step of 0.1 I get the following error profile (graph below, purple curve) and a relatively fast time to execute the algorithm.

It can be highlighted that under those constraints we keep approximately the same liquidity profile during the liquidation, which is not the case when we were under no liquidity policy.

IV-1-C Liquidate the portfolio in a given number of days This constraint has been implemented considering that if an equity cannot be liquidated in the number of days specified by the user (t), we liquidate at the beginning a percentage of the position with a haircut (also specified by the user, H) so that at the end of the specified period of liquidation we will not have this equity anymore in portfolio. This is simply achieved by multiplying the position and the associated market value by And the associated haircut costs (HC) are then:

We then launched the same algorithm as the one used under no constraints.

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Nicolas Pierre-Carmignac Gestion

IV-1-D Stress Scenario Several stress scenarios can be implemented by modifying the model inputs and notably the 3 month average volume of transaction. For now I only implemented a general decrease of transaction volume.

IV-1-E Results The results of the model are the following:

This can be produced not only for each fund but also for an aggregation of different fund and even for all the equity of all Carmignac Gestion portfolios. It will be further enhanced by implementing different stress scenarios depending on the assets geographical area, industrial sectors…. It can also take more inputs into account such as the width or the dispersion of the average bid-ask spread, the average number of contributors on this asset,…

IV-2 Liquidity Evaluation of each fixed income assets in portfolio As the bond markets is mainly over the counter, evaluating the liquidity is much more tricky since taking only the 3 month average volume into account is not enough.

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Nicolas Pierre-Carmignac Gestion

This Model takes then more parameters into inputs thant the previous one and focus on fixed income assets:    

The number of contributors who gives a price at a given day on each bonds (the more contributors, the more liquid the bond) The number of contributors who are ready to be executed on a particular bond The bid-ask spread dispersion (The more scattered, the less liquid otherwise there would be arbitrage opportunity) The bid-ask spread (the wider, the less liquid)

There were then created a scoring map for those assets which were classified into four categories : Very Liquid,Liquid,Not Really Liquid and Illiquid. In a first time the contributors are divided into three categories : the trusted contributors which we can use for the pricing policy, exchange contributors whose price cannot really be trusted but who can be executed and the fake contributors. Once this has been done we can delete the quote from the fake contributors and the contributos who do not propose a bid. We then calculate the average BidAsk spread for each bonds with the following formula :

The Ask and Bid Dispersion : = =

The algorithm works that way (blue line when the condition is satisfied, red when it is not) :

Very Liquid If Number of contributors >=10

Not Really Liquid ElseIf Number of Contributors < 3

If DispBid =10

Illiquid If AvgBidAsk >=0.01

Illiquid

ElseIf DispAsk = 10 If DispBid