Publications (49)18.26 Total impact
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Article: Financial Risk Measurement for Financial Risk Management
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ABSTRACT: Current practice largely follows restrictive approaches to market risk measurement, such as historical simulation or RiskMetrics. In contrast, we propose flexible methods that exploit recent developments in financial econometrics and are likely to produce more accurate risk assessments, treating both portfolio-level and asset-level analysis. Asset-level analysis is particularly challenging because the demands of real-world risk management in financial institutions - in particular, real-time risk tracking in very high-dimensional situations - impose strict limits on model complexity. Hence we stress powerful yet parsimonious models that are easily estimated. In addition, we emphasize the need for deeper understanding of the links between market risk and macroeconomic fundamentals, focusing primarily on links among equity return volatilities, real growth, and real growth volatilities. Throughout, we strive not only to deepen our scientific understanding of market risk, but also cross-fertilize the academic and practitioner communities, promoting improved market risk measurement technologies that draw on the best of both.Risk Management eJournal. 11/2011; -
Article: Volatility in Equilibrium: Asymmetries and Dynamic Dependencies
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ABSTRACT: Stock market volatility clusters in time, carries a risk premium, is fractionally inte- grated, and exhibits asymmetric leverage effects relative to returns. This paper develops a first internally consistent equilibrium based explanation for these longstanding empirical facts. The model is cast in continuous-time and entirely self-contained, in- volving non-separable recursive preferences. We show that the qualitative theoretical implications from the new model match remarkably well with the distinct shapes and patterns in the sample autocorrelations of the volatility and the volatility risk pre- mium, and the dynamic cross-correlations of the volatility measures with the returns calculated from actual high-frequency intra-day data on the S&P 500 aggregate market and VIX volatility indexes.03/2009; -
Article: Glossary to ARCH (GARCH)
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ABSTRACT: The literature on modeling and forecasting time-varying volatility is ripe with acronyms and abbreviations used to describe the many different parametric models that have been put forth since the original linear ARCH model introduced in the seminal Nobel Prize winning paper by Engle (1982). The present paper provides an easy-to-use encyclopedic reference guide to this long list of ARCH acronyms. In addition to the acronyms associated with specific parametric models, I have also included descriptions of various abbreviations associated with more general statistical procedures and ideas that figure especially prominently in the ARCH literature.10/2008; -
Article: Risk, Jumps, and Diversification
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ABSTRACT: We test for price discontinuities, or jumps, in a panel of high-frequency intraday returns for forty large-cap stocks and an equiweighted index from these same stocks. Jumps are naturally classified into two types: common and idiosyncratic. Common jumps affect all stocks, albeit to varying degrees, while idiosyncratic jumps are stock-specific. Despite the fact that each of the stocks has a b of about unity with respect to the index, common jumps are virtually never detected in the individual stocks. This is truly puzzling, as an index can jump only if one or more of its components jump. To resolve this puzzle, we propose a new test for cojumps. Using this new test we find strong evidence for many modest-sized common jumps that simply pass through the standard jump detection statistic, while they appear highly significant in the cross section based on the new cojump identification scheme. Our results are further corroborated by a striking within-day pattern in the non-diversifiable cojumps.Capital Markets: Asset Pricing & Valuation eJournal. 06/2008; -
Article: Continuous-Time Models, Realized Volatilities, and Testable Distributional Implications for Daily Stock Returns
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ABSTRACT: This paper provides an introduction to alternative models of uncertain commodity prices. A model of commodity price movements is the engine around which any valuation methodology for commodity production projects is built, whether discounted cash flow (DCF) models or the recently developed modern asset pricing (MAP) methods. The accuracy of the valuation is in part dependent on the quality of the engine employed. This paper provides an overview of several basic commodity price models and explains the essential differences among them. We also show how futures prices can be used to discriminate among the models and to estimate better key parameters of the model chosen.Queen's University, Department of Economics, Working Papers. 01/2008; -
Article: A Discrete-Time Model for Daily S&P500 Returns and Realized Variations: Jumps and Leverage Effects
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ABSTRACT: We develop an empirically highly accurate discrete-time daily stochastic volatility model that explicitly distinguishes between the jump and continuoustime components of price movements using nonparametric realized variation and Bipower variation measures constructed from high-frequency intraday data. The model setup allows us to directly assess the structural inter-dependencies among the shocks to returns and the two different volatility components. The model estimates suggest that the leverage effect, or asymmetry between returns and volatility, works primarily through the continuous volatility component. The excellent fit of the model makes it an ideal candidate for an easyto- implement auxiliary model in the context of indirect estimation of empirically more realistic continuous-time jump diffusion and L´evy-driven stochastic volatility models, effectively incorporating the interdaily dependencies inherent in the high-frequency intraday data.09/2007; -
Article: Real-time price discovery in global stock, bond and foreign exchange markets
Journal of International Economics. 02/2007; 73(2):251-277. -
Article: Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility
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ABSTRACT: A growing literature documents important gains in asset return volatility forecasting via use of realized variation measures constructed from high-frequency returns. We progress by using newly developed bipower variation measures and corresponding nonparametric tests for jumps. Our empirical analyses of exchange rates, equity index returns, and bond yields suggest that the volatility jump component is both highly important and distinctly less persistent than the continuous component, and that separating the rough jump moves from the smooth continuous moves results in significant out-of-sample volatility forecast improvements. Moreover, many of the significant jumps are associated with specific macroeconomic news announcements. Copyright by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.Review of Economics and Statistics 02/2007; 89(4):701-720. · 2.66 Impact Factor -
Article: Leverage and Volatility Feedback Effects in High-Frequency Data
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ABSTRACT: We examine the relationship between volatility and past and future returns using high-frequency aggregate equity index data. Consistent with a prolonged "leverage" effect, we find the correlations between absolute high-frequency returns and current and past high-frequency returns to be significantly negative for several days, whereas the reverse cross-correlations are generally negligible. We also find that high-frequency data may be used in more accurately assessing volatility asymmetries over longer daily return horizons. Furthermore, our analysis of several popular continuous-time stochastic volatility models clearly points to the importance of allowing for multiple latent volatility factors for satisfactorily describing the observed volatility asymmetries. Copyright 2006, Oxford University Press.Journal of Financial Econometrics 02/2006; 4(3):353-384. · 1.17 Impact Factor -
Article: Practical Volatility and Correlation Modeling for Financial Market Risk Management
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ABSTRACT: What do academics have to offer market risk management practitioners in financial institutions? Current industry practice largely follows one of two extremely restrictive approaches: historical simulation or RiskMetrics. In contrast, we favor flexible methods based on recent developments in financial econometrics, which are likely to produce more accurate assessments of market risk. Clearly, the demands of real-world risk management in financial institutions – in particular, real-time risk tracking in very high-dimensional situations – impose strict limits on model complexity. Hence we stress parsimonious models that are easily estimated, and we discuss a variety of practical approaches for high-dimensional covariance matrix modeling, along with what we see as some of the pitfalls and problems in current practice. In so doing we hope to encourage further dialog between the academic and practitioner communities, hopefully stimulating the development of improved market risk management technologies that draw on the best of both worlds.02/2005; -
Article: A Framework for Exploring the Macroeconomic Determinants of Systematic Risk
American Economic Review 02/2005; 95(2):398-404. · 2.69 Impact Factor -
Article: Correcting the Errors: Volatility Forecast Evaluation Using High-Frequency Data and Realized Volatilities
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ABSTRACT: We develop general model-free adjustment procedures for the calculation of unbiased volatility loss functions based on practically feasible realized volatility benchmarks. The procedures, which exploit recent nonparametric asymptotic distributional results, are both easy-to-implement and highly accurate in empirically realistic situations. We also illustrate that properly accounting for the measurement errors in the volatility forecast evaluations reported in the existing literature can result in markedly higher estimates for the true degree of return volatility predictability. Copyright The Econometric Society 2005.Econometrica 01/2005; 73(1):279-296. · 2.98 Impact Factor -
Article: A Framework for Exploring the Macroeconomic Determinants of Systematic Risk
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ABSTRACT: This paper presents a stock-flow consistent macroeconomic model in which financial fragility in firm and household sectors evolves endogenously through the interaction between real and financial sectors. Changes in firms' and households' financial practices produce long waves. The Hopf bifurcation theorem is applied to clarify the conditions for the existence of limit cycles, and simulations illustrate stable limit cycles. The long waves are characterized by periodic economic crises following long expansions. Short cycles, generated by the interaction between effective demand and labor market dynamics, fluctuate around the long waves.Center for Financial Studies, CFS Working Paper Series. 01/2005; -
Article: Volatility Forecasting
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ABSTRACT: This paper provides an introduction to alternative models of uncertain commodity prices. A model of commodity price movements is the engine around which any valuation methodology for commodity production projects is built, whether discounted cash flow (DCF) models or the recently developed modern asset pricing (MAP) methods. The accuracy of the valuation is in part dependent on the quality of the engine employed. This paper provides an overview of several basic commodity price models and explains the essential differences among them. We also show how futures prices can be used to discriminate among the models and to estimate better key parameters of the model chosen.Center for Financial Studies, CFS Working Paper Series. 01/2005; -
Article: Torben G. Andersen
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ABSTRACT: We characterize the response of U.S., German and British stock, bond and foreign exchange markets to real-time U.S. macroeconomic news. Our analysis is based on a unique data set of highfrequency futures returns for each of the markets. We find that news surprises produce conditional mean jumps; hence high-frequency stock, bond and exchange rate dynamics are linked to fundamentals. The details of the linkages are particularly intriguing as regards equity markets. We show that equity markets rationalize this by temporal variation in the competing "cash flow" and "discount rate" effects for equity valuation. This finding helps explain the time-varying correlation between stock and bond returns, and the relatively small equity market news effect when averaged across expansions and recessions. Lastly, relying on the pronounced heteroskedasticity in the high-frequency data, we document important contemporaneous linkages across all markets and countries over-and-above the direct news announcement effects. Key Words: Asset Pricing; Macroeconomic News Announcements; Financial Market Linkages; Market Microstructure; High-Frequency Data; Survey Data; Asset Return Volatility; Forecasting.07/2004; -
Article: Realized Beta: Persistence and Predictability
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ABSTRACT: A large literature over several decades reveals both extensive concern with the question of time-varying betas and an emerging consensus that betas are in fact time-varying, leading to the prominence of the conditional CAPM. Set against that background, we assess the dynamics in realized betas, vis-a-vis the dynamics in the underlying realized market variance and individual equity covariances with the market. Working in the recently-popularized framework of realized volatility, we are led to a framework of nonlinear fractional cointegration: although realized variances and covariances are very highly persistent and well approximated as fractionally-integrated, realized betas, which are simple nonlinear functions of those realized variances and covariances, are less persistent and arguably best modeled as stationary I(0) processes. We conclude by drawing implications for asset pricing and portfolio management.Capital Markets: Asset Pricing & Valuation eJournal. 05/2004; -
Article: Real-Time Price Discovery in Stock, Bond and Foreign Exchange Markets
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ABSTRACT: We characterize the response of U.S., German and British stock, bond and foreign exchange markets to real-time U.S. macroeconomic news. Our analysis is based on a unique data set of high-frequency futures returns for each of the markets. We find that news surprises produce conditional mean jumps; hence high-frequency stock, bond and exchange rate dynamics are linked to fundamentals. The details of the linkages are particularly intriguing as regards equity markets. We show that equity markets react differently to the same news depending on the state of the economy, with bad news having a positive impact during expansions and the traditionally-expected negative impact during recessions. We rationalize this by temporal variation in the competing “cash flow” and “discount rate” effects for equity valuation. This finding helps explain the time-varying correlation between stock and bond returns, and the relatively small equity market news effect when averaged across expansions and recessions. Lastly, relying on the pronounced heteroskedasticity in the high-frequency data, we document important contemporaneous linkages across all markets and countries over-and-above the direct news announcement effects.02/2004; -
Article: ANALYTICAL EVALUATION OF VOLATILITY FORECASTS
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ABSTRACT: Estimation and forecasting for realistic continuous-time stochastic volatility models is hampered by the lack of closed-form expressions for the likelihood. In response, Andersen, Bollerslev, Diebold, and Labys ("Econometrica", 71 (2003), 579-625) advocate forecasting integrated volatility via reduced-form models for the realized volatility, constructed by summing high-frequency squared returns. Building on the eigenfunction stochastic volatility models, we present analytical expressions for the forecast efficiency associated with this reduced-form approach as a function of sampling frequency. For popular models like GARCH, multifactor affine, and lognormal diffusions, the reduced form procedures perform remarkably well relative to the optimal (infeasible) forecasts. Copyright 2004 by the Economics Department Of The University Of Pennsylvania And Osaka University Institute Of Social And Economic Research Association.International Economic Review 01/2004; 45(4):1079-1110. · 1.56 Impact Factor -
Article: Correcting the Errors: A Note on Volatility Forecast Evaluation Based on High-Frequency Data and Realized Volatilities
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ABSTRACT: This note develops general model-free adjustment procedures for the calculation of unbiased volatility loss functions based on practically feasible realized volatility benchmarks. The procedures, which exploit the recent asymptotic distributional results in Barndorff-Nielsen and Shephard (2002a), are both easy-to-implement and highly accurate in empirically realistic situations. On properly accounting for the measurement errors in the volatility forecast evaluations reported in Andersen, Bollerslev, Diebold and Labys (2003), the adjustments result in markedly higher estimates for the true degree of return-volatility predictability. Cette note développe des méthodes d'ajustement, sans spécifier le modèle, qui corrigent le biais induit par les erreurs de mesures de la volatilité dans la mesure de performance des méthodes de prévision de la volatilité. Les procédures, qui utilisent la récente théorie asymptotique de Barndorff-Nielsen et Shephard (2002a), sont faciles à mettre en ?uvre et très performantes dans les situations empiriques usuelles. En particulier, la prise en compte des erreurs de mesures dans les procédures de prévision de Andersen, Bollerslev, Diebold et Labys (2003), amène à des performances de prévision de la volatilité très élevées.01/2003; -
Article: Some Like it Smooth, and Some Like it Rough: Untangling Continuous and Jump Components in Measuring, Modeling, and Forecasting Asset Return Volatility
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ABSTRACT: Changes in variance, or volatility, over time can be modeled using the approach based on autoregressive conditional heteroscedasticity. Another approach is to model variance as an unobserved stochastic process. Although it is not easy to obtain the exact likelihood function for such stochastic variance models, they tie in closely with developments in finance theory and have certain statistical attractions. This article sets up a multivariate model, discusses its statistical treatment, and shows how it can be modified to capture common movements in volatility in a very natural way. The model is then fitted to daily observations on exchange rates.Center for Financial Studies, CFS Working Paper Series. 01/2003;
Top co-authors
Top Journals
Institutions
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1997–2011
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Duke University
- Department of Economics
Durham, NC, USA
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2008
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Aarhus University
Aars, Region North Jutland, Denmark
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2005
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University of Pennsylvania
- Department of Economics
Philadelphia, PA, USA
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1998
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University of Virginia
- Department of Economics
Charlottesville, VA, USA
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