Structural Change and Asset Pricing in Emerging Markets

This paper documents the importance of testing for structural change in the context of emerging markets. Typically, asset pricing factor models for emerging markets are conditioned on world financial market factors such as world equity excess returns, risk and maturity spreads as well as other variables designed to capture world business cycle fluctuations. We show that for many countries, while we cannot reject the model according to one usual chi-square test for overidentifying restrictions, we reject it on the basis of structural change tests, especially when international factors are considered. Much better support and greater stability are found when a local CAPM is tested with size-ranked portfolios. Some evidence of a small-size effect persists for some countries. Dans cet article, nous montrons l'importance d'utiliser des tests de changement structurel dans le contexte des marchés boursiers en émergence. Les modèles de valorisation das actifs financiers utilisés dans ce contexte sont en général des modèles conditionnels à facteurs fondés sur des facteurs à caractère international tels les rendements excédentaires sur le marché mondial des actions, les écarts de taux captant la prime de risque et la prime de terme, ainsi que d'autres variables visant à mesurer les fluctuations du cycle économique mondial. Nous montrons que dans de nombreux pays, bien que nous ne puissions pas rejeter les modèles en fonction des tests de suridentification habituels de distribution chi-carré, nous les rejetons en fonction des tests de changement structurel, notamment lorsque nous utilisons des factuers internationaux. Nous trouvons des résultats beaucoup plus favorables aux modèles et une plus grande stabilité lorsque nous testons un CAPM local avec des portefeuilles ordonnés selon la taille. Un effet de taille persiste toutefois dans certains pays.Conditional Factor Models, Time-Varying Risk and Returns, Emerging Markets, Structural Stability, Modèles à facteurs conditionnels, marchés émergents, changements structurels

The Impact of Sampling Frequency and Volatility Estimators on Change-Point Tests

The paper evaluates the performance of several recently proposed change-point tests applied to conditional variance dynamics and conditional distributions of asset returns. These are CUSUM-type tests for beta-mixing processes and EDF-based tests for the residuals of such nonlinear dependent processes. Hence the tests apply to the class of ARCH and SV type processes as well as data-driven volatility estimators using high-frequency data. It is shown that some of the high-frequency volatility estimators substantially improve the power of the structural breaks tests especially for detecting changes in the tail of the conditional distribution. Similarly, certain types of filtering and transformation of the returns process can improve the power of CUSUM statistics. We also explore the impact of sampling frequency on each of the test statistics. Ce papier évalue la performance de plusieurs tests de changement structurel CUSUM et EDF pour la structure dynamique de la variance conditionelle et de la distribution conditionnelle. Nous étudions l'impact 1) de la fréquence des observations, 2) de l'utilisation des données de haute fréquence pour le calcul des variances conditionnelles et 3) de transformation des séries pour améliorer la puissance des tests.Change-point tests, CUSUM, Kolmogorov-Smirnov, GARCH, quadratic variation, power variation, high-frequency data, location-scale distribution family, tests de changement structurel, CUSUM, Kolmogov-Smirnov, GARCH, variation quadratique, 'power variation', données de haute fréquence

On Portfolio Choice, Liquidity, and Short Selling: A Nonparametric Investigation

This paper studies the time series effect of changes in liquidity on optimal portfolio allocations. Using a nonparametric approach, we are able to handle models that are analytically intractable. Specifically, we directly estimate optimal portfolio weights for a CRRA investor as functions of liquidity. Liquidity is measured by turnover, dollar volume, or price impact. We consider three different investment horizons: daily, weekly, and monthly. Using a sample of NYSE stocks from 1963-2000, we document a very interesting temporal dimension to the effects of changes in liquidity: whereas optimal weights are strongly increasing functions of liquidity at the very short daily and weekly horizons, they become decreasing functions of liquidity at longer monthly horizons. Overall, the dependence of optimal weights on liquidity is most noticeable for small stocks at short investment horizons. Finally, the optimal conditional portfolio weights documented in this paper are never negative, which may help explain the low level of short selling observed in the US stock market. Nous estimons des décisions de choix de portefeuille en fonction de mesures de liquidité à l'aide de méthodes non paramétriques. Nous trouvons que les parts optimales de portefeuilles sont surtout influencées par la liquidité pour des horizons à court-terme. Par ailleurs, ces parts optimales sont toujours positives, ce qui pourrait expliquer le peu de vente à découvert observé sur le marché américain.portfolio choice, liquidity, short-selling, choix de portefeuille, liquidité, vente à découvert

Quality Control for Structural Credit Risk Models

Over the last four decades, a large number of structural models have been developed to estimate and price credit risk. The focus of the paper is on a neglected issue pertaining to fundamental shifts in the structural parameters governing default. We propose formal quality control procedures that allow risk managers to monitor fundamental shifts in the structural parameters of credit risk models. The procedures are sequential - hence apply in real time. The basic ingredients are the key processes used in credit risk analysis, such as most prominently the Merton distance to default process as well as financial returns. Moreover, while we propose different monitoring processes, we also show that one particular process is optimal in terms of minimal detection time of a break in the drift process and relates to the Radon-Nikodym derivative for a change of measure.

A Semi-Parametric Factor Model for Interest Rates

Understanding the dynamics of interest rates and the term structure has important implications for issues as diverse as real economic activity, monetary policy, pricing of interest rate derivative securities and public debt financing. Our paper follows a longstanding tradition of using factor models of interest rates but proposes a semi-parametric procedure to model interest rates. In a semi-parametric approach one typically parameterizes the object of interest while leaving unspecified the rest of the model. We construct factors as linear functionals of key economic time series involving unknown parameters, but treat the response of interest rates to the factors in a nonparametric way. The Average Derivating Estimator, which is a semi-parametric procedure proposed by Hardle and Stoker (1989) and Powell, Stock and Stoker (1989), allows us to proceed in two steps, namely we first identify factors without assuming knowledge of the response function of interest rates to the factors. Once the factors are identified, we proceed with estimating the response function using nonparametric methods. We can view our semi-parametric approach as a prelude to a fullblown parametric formulation for a factor term structure model. Indeed, our empirical results suggest a short term rate specification which deviates from standard parametric models often considered in the literature. Dans cette étude nous proposons des modèles à facteurs semi-paramétriques pour taux d'intérêts. Nous construisons les facteurs comme des fonctions linéaires de variables clés normales et réelles. L'estimateur de dérivée moyenne, proposé par Hardle et Stoker (1989) et Powell, Stock et Stoker (1989) nous permet d'estimer ces facteurs comme fonctions linéaires sans connaître leurs relations avec les taux d'intérêts. Une fois les facteurs identifiés et estimés nous estimons dans une deuxième étape cette dernière relation par méthodes non-paramétriques.Interest Rates, Term Structure, Factor Models, Semi Parametric Models, Average Derivative Estimator, Taux d'intérêts, Structure par terme, Modèles à facteurs, Méthodes semi-paramétriques, Estimateur de dérivée moyenne

Test for Breaks in the Conditional Co-Movements of Asset Returns

We propose procedures designed to uncover structural breaks in the co-movements of financial markets. A reduced form approach is introduced that can be considered as a two stage method for reducing dimensionality of multivariate heteroskedastic conditional volatility models through marginalization. The main advantage is that one can use returns normalized by volatility filters that are purely data-driven and construct general conditional covariance dynamic specifications. The main thrust of our procedure is to examine change-points in the co-movements of normalized returns. We document, using a ten year period of two representative high frequency FX series, that regression models with non-Gaussian errors describe adequately their co-movements. Change-points are detected in the conditional covariance of the DM/US$and YN/US$ normalized returns over the decade 1986-1996.change-point tests, conditional covariance, high-frequency financial data, multivariate GARCH models

An Empirical Analysis of the Canadian Budget Process

This paper provides a statistical analysis of the forecasts of significant number of expenditure and revenue components of the Federal budget provided each year by the Department of Finance. The sample available for such an investigation is limited and we describe an easily-applied nonparametric testing methodology which is more appropriate than the usual regression-based approach in small samples. The reliability and relative power of the various nonparametric tests are illustrated in a series of simulations. Applying these tests to the fiscal forecasts, we find that there is little cause to be concerned with the forecast performance of the Department of Finance over the last seventeen years. Dans cette étude nous examinons les erreurs de prévisions pour les comptes de dépenses et recettes du budget canadien. Nous appliquons des méthodes non-paramétriques à cause des petites tailles d'échantillons. Nous trouvons peu d'erreurs systématiques dans les prévisions budgétaires.Budget forecast; Nonparametric methods, Prévisions budgétaires ; Méthodes non-paramétriques

Monitoring for Disruptions in Financial Markets

Historical and sequential CUSUM change-point tests for strongly dependent nonlinear processes are studied. These tests are used to monitor the conditional variance of asset returns and to provide early information regarding instabilities or disruptions in financial risk. Data-driven monitoring schemes are investigated. Since the processes are strongly dependent several novel issues require special attention. One such issue is the sampling frequency. We study the power of detection as sampling frequencies vary. Analytical local power results are obtained for historical CUSUM tests and simulation evidence is presented for sequential tests. Finally, a prediction-based statistic is introduced that reduces the detection delay considerably. The prediction based formula is based on a local Brownian bridge approximation argument and provides an assessment of the likelihood of change-points. Nous étudions les tests CUSUM historiques et séquentiels pour des séries dépendantes avec des applications en finance. Pour les processus temporels, une nouvelle dimension se présente : l'effet du choix de la fréquence des observations. Un nouveau test est également proposé. Ce test est basé sur une formule de prévision locale d'un pont brownien.structural change, CUSUM, GARCH, quadratic variation, power variation, high frequency data, Brownian bridge, boundary crossing, sequential tests, local power, changement structurel, CUSUM, GARCH, variation quadratique, 'power variation', données de haute fréquence, pont Brownien, puissance locale, tests séquentiels

Detecting Multiple Breaks in Financial Market Volatility Dynamics

The paper evaluates the performance of several recently proposed tests for structural breaks in conditional variance dynamics of asset returns. The tests apply to the class of ARCH and SV type processes as well as data-driven volatility estimators using high-frequency data. In addition to testing for the presence of breaks, the statistics identify the number and location of multiple breaks. We study the size and power of the new test for detecting breaks in the second conditional variance under various realistic univariate heteroskedastic models, change-point hypotheses and sampling schemes. The paper concludes with an empirical analysis using data from the stock and FX markets for which we find multiple breaks associated with the Asian and Russian financial crises. These events resulted in changes in the dynamics of volatility of asset returns in the samples prior and post the breaks.change-point, break dates, ARCH, high-frequency data.

On Portfolio Separation Theorems with Heterogeneous Beliefs and Attitudes towards Risk

The early work of Tobin (1958) showed that portfolio allocation decisions can be reduced to a two stage process: first decide the relative allocation of assets across the risky assets, and second decide how to divide total wealth between the risky assets and the safe asset. This so called twofund separation relies on special assumptions on either returns or preferences. Tobin (1958) analyzed portfolio demand in a mean-variance setting. We revisit the fund separation in settings that allow not only for heterogeneity of preferences for higher order moments, but also for heterogeneity of beliefs among agents. To handle the various sources of heterogeneity, beliefs, and preferences, we follow the framework of Samuelson (1970) and its recent generalization by Chabi-Yo, Leisen, and Renault (2006). This generic approach allows us to derive, for risks that are infinitely small, optimal shares of wealth invested in each security that coincide with those of a Mean-Variance-Skewness-Kurtosis optimizing agent. Besides the standard Sharpe-Lintner CAPM mutual fund separation we obtain additional mutual funds called beliefs portfolios, pertaining to heterogeneity of beliefs, a skewness portfolio similar to Kraus and Litzenberger (1976), beliefs about skewness portfolios with design quite similar to beliefs portfolios, a kurtosis portfolio, and finally portfolio heterogeneity of the preferences for skewness across investors in the economy as well as its covariation with heterogeneity of beliefs. These last two mutual funds are called cross-co-skewness portfolio and cross-co-skewness-beliefs portfolios. Under various circumstances related to return distribution characteristics, cross-agent heterogeneity and market incompleteness, some of these portfolios disappear.Financial markets; Market structure and pricing