Kevin Sheppard
I currently work at the University of Oxford as a Financial Econometrician. My research focuses on volatility and uncertainty. Measuring and modeling conditional correlation, a key input into portfolio risk models is a cornerstone of my research.
I have produced a large volume of teaching resources, including a complete set of notes in Financial Econometrics, and introductions to both Python and MATLAB. I also maintain a number of widely used toolboxes related to my research. The most broadly used of these are the MFE Toolbox for MATLAB, and the arch (documentation, ) and linearmodels (documentation, ) modules for Python. See my GitHub page for a complete list of projects.
I enjoy hiking and being outdoors, although I don't get out as much as I would like. I have a sweet but rambunctious chocolate labrador, Callie.
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Factor high-frequency-based volatility (heavy) models
January 2019|Journal article|Journal of Financial Econometrics© The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions. We propose a new class of multivariate volatility models utilizing realized measures of asset variance and covariance extracted from high-frequency data. Dimension reduction for estimation of large covariance matrices is achieved by imposing a factor structure with time-varying conditional factor loadings. Statistical properties of the model, including conditions that ensure covariance stationarity of returns, are established. The performance of the model is assessed using a panel of large U.S. financial institutions during the financial crisis, where empirical results show that the new model has both superior in-and out-of-sample properties. We show that the superior performance applies to a wide range of quantities of interest, including volatilities, covariances, betas, and scenario-based risk measures.The model's performance is particularly strong at short forecast horizons. -
Ambiguity and the historical equity premium
August 2018|Journal articleThis paper assesses the quantitative impact of ambiguity on historically observed financial asset returns and growth rates. The single agent, in a dynamic exchange economy, treats the conditional uncertainty about the consumption and dividends next period as ambiguous. We calibrate the agent's ambiguity aversion to match only the first moment of the risk-free rate in data and measure the uncertainty each period on the actual, observed history of (U.S.) macroeconomic growth outcomes. Ambiguity aversion accentuates the conditional uncertainty endogenously in a dynamic way, depending on the history; e.g., it increases during recessions. We show the model implied time series of asset returns substantially match the first and second conditional moments of observed return dynamics. In particular, we find the time-series properties of our model generated equity premium, which may be regarded as an index measure of revealed uncertainty, relates closely to those of the macroeconomic uncertainty index recently developed in Jurado, Ludvigson, and Ng (2013).Ambiguity aversion, Asset pricing, Equity premium puzzle, Time-varying uncertainty, Uncertainty shocks -
Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes
July 2015|Journal article|Journal of Econometrics -
Multivariate rotated ARCH models
January 2013|Journal article|Journal of EconometricsThis paper introduces a new class of multivariate volatility models which is easy to estimate using covariance targeting, even with rich dynamics. We call them rotated ARCH (RARCH) models. The basic structure is to rotate the returns and then to fit them using a BEKK-type parameterization of the time-varying covariance whose long-run covariance is the identity matrix. This yields the rotated BEKK (RBEKK) model. The extension to DCC-type parameterizations is given, introducing the rotated DCC (RDCC) model. Inference for these models is computationally attractive, and the asymptotics are standard. The techniques are illustrated using data on the DJIA stocks. © 2013 Elsevier B.V. All rights reserved.
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Department of Economics Discussion Paper Series
Factor High-Frequency Based Volatility (HEAVY) Models
May 2014|Working paper|Department of Economics Discussion Paper SeriesWe propose a new class of multivariate volatility models utilizing realized measures of asset volatility and covolatility extracted from high-frequency data. Dimension reduction for estimation of large covariance matrices is achieved by imposing a factor structure with time-varying conditional factor loadings. Statistical properties of the model, including conditions that ensure covariance stationary or returns, are established. The model is applied to modeling the conditional covariance data of large U.S. financial institutions during the financial crisis, where empirical results show that the new model has both superior in- and out-of-sample properties. We show that the superior performance applies to a wide range of quantities of interest, including volatilities, covolatilities, betas and scenario-based risk measures, where the model's performance is particularly strong at short forecast horizons.Conditional Beta, Conditional Covariance, Forecasting, HEAVY, Marginal Expected Shortfall, Realized Covariance, Realized Kernel, Systematic Risk -
Department of Economics Discussion Paper Series
Does Anything Beat 5-Minute RV? A Comparison of Realized Measures Across Multiple Asset Classes
February 2013|Working paper|Department of Economics Discussion Paper SeriesWe study the accuracy of a wide variety of estimators of asset price variation constructed from high-frequency data (so-called "realized measures"), and compare them with a simple "realized variance" (RV) estimator. In total, we consider almost 400 different estimators, applied to 11 years of data on 31 different financial assets spanning five asset classes, including equities, equity indices, exchange rates and interest rates. We apply data-based ranking methods to the realized measures and to forecasts based on these measures. When 5-minute RV is taken as the benchmark realized measure, we find little evidence that it is outperformed by any of the other measures. When using inference methods that do not require specifying a benchmark, we find some evidence that more sophisticated realized measures significantly outperform 5-minute RV. In forecasting applications, we find that a low frequency "truncated" RV outperforms most other realized measures. Overall, we conclude that it is difficult to significantly beat 5-minute RV.Realized variance, volatility forecasting, high frequency data -
Department of Economics Discussion Paper Series
Multivariate Rotated ARCH models
February 2012|Working paper|Department of Economics Discussion Paper SeriesThis paper introduces a new class of multivariate volatility models which is easy to estimate using covariance targeting, even with rich dynamics. We call them rotated ARCH (RARCH) models. The basic structure is to rotate the returns and then to fit them using a BEKK-type parameterization of the time-varying covariance whose long-run covariance is the identity matrix. The extension to DCC-type parameterizations is given, introducing the rotated conditional correlation (RCC) model. Inference for these mdoels is computationally attractive, and the asymptotics are standard. The techniques are illustrated using data on some SJIA stocks.RARCH, RCC, Multivariate volatility, Covariance targeting, Common persistence, Empirical Bayes, Predictive likelihood -
Department of Economics Discussion Paper Series
Efficient and feasible inference for the components of financial variation using blocked multipower variation
February 2012|Working paper|Department of Economics Discussion Paper SeriesHigh frequency financial data allows us to learn more about volatility, volatility of volatility and jumps. One of the key techniques developed in the literature in recent years has been bipower variation and its multipower extension, which estimates time-varying volatility robustly to jumps. We improve the scope and efficiency of multipower variation by the use of a more sophisticated exploitation of high frequency data. This suggests very significant improvements in the power of jump tests. It also yields efficiency estimates of the integrated variance of the continuous part of a semimartingale. The paper also shows how to extend the theory to the case where there is microstructure in the observations and derive the first nonparametric high frequency estimator of the volatility of volatility. A fundamental device in the paper is a new type of result showing path-by-path (strong) approximation between multipower and the (unobserved) RV based on the continuous part of the process.Bipower variation, Jumps, Market microstructure noise, Multipower variation, Non-parametric analysis, Quadratic variation, Semimartingale, Volatility, Volatility of volatility -
Department of Economics Discussion Paper Series
Multivariate High-Frequency-Based Volatility (HEAVY) Models
February 2011|Working paper|Department of Economics Discussion Paper SeriesThis paper introduces a new class of multivariate volatility models that utilizes high-frequency data. We discuss the models' dynamics and highlight their differences from multivariate GARCH models. We also discuss their covariance targeting specification and provide closed-form formulas for multi-step forecasts. Estimation and inference strategies are outlined. Empirical results suggest that the HEAVY model outperforms the multivariate GARCH model out-of-sample, with the gains being particularly significant at short forecast horizons. Forecast gains are obtained for both forecast variances and correlations.HEAVY model, GARCH, multivariate volatility, realized covariance, covariance targeting, multi-step forecasting, Wishart distribution