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122
A multiple indicators model for volatility using intradaily data
 Journal of Econometrics
, 2006
"... Many ways exist to measure and model financial asset volatility. In principle, as the frequency of the data increases, the quality of forecasts should improve. Yet, there is no consensus about a “true ” or "best " measure of volatility. In this paper we propose to jointly consider absolute ..."
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Cited by 74 (9 self)
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Many ways exist to measure and model financial asset volatility. In principle, as the frequency of the data increases, the quality of forecasts should improve. Yet, there is no consensus about a “true ” or "best " measure of volatility. In this paper we propose to jointly consider absolute daily returns, daily highlow range and daily realized volatility to develop a forecasting model based on their conditional dynamics. As all are nonnegative series, we develop a multiplicative error model that is consistent and asymptotically normal under a wide range of specifications for the error density function. The estimation results show significant interactions between the indicators. We also show that onemonthahead forecasts match well (both in and out of sample) the marketbased volatility measure provided by an average of implied volatilities of index options as measured by VIX.
The empirical riskreturn relation: a factor analysis approach
, 2007
"... Existing empirical literature on the riskreturn relation uses a relatively small amount of conditioning information to model the conditional mean and conditional volatility of excess stock market returns. We use dynamic factor analysis for large datasets to summarize a large amount of economic info ..."
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Cited by 46 (7 self)
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Existing empirical literature on the riskreturn relation uses a relatively small amount of conditioning information to model the conditional mean and conditional volatility of excess stock market returns. We use dynamic factor analysis for large datasets to summarize a large amount of economic information by few estimated factors, and find that three new factors termed “volatility,” “risk premium,” and “real” factors contain important information about onequarterahead excess returns and volatility not contained in commonly used predictor variables. Our specifications predict 1620 % of the onequarterahead variation in excess stock market returns, and exhibit stable and statistically significant outofsample forecasting power. We also find a positive conditional riskreturn correlation.
Volatility Forecast Comparison Using Imperfect Volatility Proxies, Quantitative Finance Research
, 2006
"... The use of a conditionally unbiased, but imperfect, volatility proxy can lead to undesirable outcomes in standard methods for comparing conditional variance forecasts. We derive necessary and sufficient conditions on functional form of the loss function for the ranking of competing volatility foreca ..."
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Cited by 41 (6 self)
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The use of a conditionally unbiased, but imperfect, volatility proxy can lead to undesirable outcomes in standard methods for comparing conditional variance forecasts. We derive necessary and sufficient conditions on functional form of the loss function for the ranking of competing volatility forecasts to be robust to the presence of noise in the volatility proxy, and derive some interesting special cases of this class of “robust ” loss functions. We motivate the theory with analytical results on the distortions caused by some widelyused loss functions, when used with standard volatility proxies such as squared returns, the intradaily range or realised volatility. The methods are illustrated with an application to the volatility of returns on IBM over the period 1993 to 2003.
Weather Forecasting for Weather Derivatives
 Journal of the American Statistical Association
, 2000
"... We take a nonstructural timeseries approach to modeling and forecasting daily average temperature in ten U.S. cities, and we inquire systematically as to whether it may prove useful from the vantage point of participants in the weather derivatives market. The answer is, perhaps surprisingly, yes. T ..."
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Cited by 35 (1 self)
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We take a nonstructural timeseries approach to modeling and forecasting daily average temperature in ten U.S. cities, and we inquire systematically as to whether it may prove useful from the vantage point of participants in the weather derivatives market. The answer is, perhaps surprisingly, yes. Time series modeling reveals both strong conditional mean dynamics and conditional variance dynamics in daily average temperature, and it reveals sharp differences between the distribution of temperature and the distribution of temperature surprises. Most importantly, it adapts readily to produce the longhorizon forecasts of relevance in weather derivatives contexts. We produce and evaluate both point and distributional forecasts of average temperature, with some success. We conclude that additional inquiry into nonstructural weather forecasting methods, as relevant for weather derivatives, will likely prove useful. Key Words: Risk management; hedging; insurance; seasonality; average temperature; financial derivatives; density forecasting JEL Codes: G0, C1 Acknowledgments: For financial support we thank the National Science Foundation, the Wharton Financial Institutions Center, and the Wharton Risk Management and Decision Process Center. For helpful comments we thank Marshall Blume, Larry Brown, Jeff Considine, John Dutton, Ren Garcia, Stephen Jewson, Vince Kaminski, Paul Kleindorfer, Howard Kunreuther, Yu Li, Bob Livezey, Cliff Mass, Don McIsaac, Nour Meddahi, David Pozo, Matt Pritsker, S.T. Rao, Claudio Riberio, Til Schuermann and Yihong Xia. We are also grateful for comments by participants at the American Meteorological Society's Policy Forum on Weather, Climate and Energy. None of those thanked, of course, are responsible in any way for the outcome. Address corresponde...
A NoArbitrage Approach to RangeBased Estimation of Return Covariances and Correlations
, 2003
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MCMC methods for continuoustime financial econometrics

, 2003
"... This chapter develops Markov Chain Monte Carlo (MCMC) methods for Bayesian inference in continuoustime asset pricing models. The Bayesian solution to the inference problem is the distribution of parameters and latent variables conditional on observed data, and MCMC methods provide a tool for explor ..."
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Cited by 28 (1 self)
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This chapter develops Markov Chain Monte Carlo (MCMC) methods for Bayesian inference in continuoustime asset pricing models. The Bayesian solution to the inference problem is the distribution of parameters and latent variables conditional on observed data, and MCMC methods provide a tool for exploring these highdimensional, complex distributions. We first provide a description of the foundations and mechanics of MCMC algorithms. This includes a discussion of the CliffordHammersley theorem, the Gibbs sampler, the MetropolisHastings algorithm, and theoretical convergence properties of MCMC algorithms. We next provide a tutorial on building MCMC algorithms for a range of continuoustime asset pricing models. We include detailed examples for equity price models, option pricing models, term structure models, and regimeswitching models. Finally, we discuss the issue of sequential Bayesian inference, both for parameters and state variables.
Optimal filtering of jump diffusions: extracting latent states from asset prices
, 2007
"... This paper provides a methodology for computing optimal filtering distributions in discretely observed continuoustime jumpdiffusion models. Although it has received little attention, the filtering distribution is useful for estimating latent states, forecasting volatility and returns, computing mo ..."
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Cited by 25 (5 self)
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This paper provides a methodology for computing optimal filtering distributions in discretely observed continuoustime jumpdiffusion models. Although it has received little attention, the filtering distribution is useful for estimating latent states, forecasting volatility and returns, computing model diagnostics such as likelihood ratios, and parameter estimation. Our approach combines timediscretization schemes with Monte Carlo methods to compute the optimal filtering distribution. Our approach is very general, applying in multivariate jumpdiffusion models with nonlinear characteristics and even nonanalytic observation equations, such as those that arise when option prices are available. We provide a detailed analysis of the performance of the filter, and analyze four applications: disentangling jumps from stochastic volatility, forecasting realized volatility, likelihood based model comparison, and filtering using both option prices and underlying returns.
Financial asset returns, directionofchange forecasting and volatility dynamics
, 2003
"... informs doi 10.1287/mnsc.1060.0520 ..."
Realized RangeBased Estimation of Integrated Variance
 Journal of Financial Econometrics (Forthcoming
, 2005
"... We provide a set of probabilistic laws for estimating quadratic variation of continuous semimartingales with the realized rangebased variance; a statistic that replaces every squared return of realized variance with a normalized squared range. If the entire sample path of the process is available ..."
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Cited by 19 (1 self)
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We provide a set of probabilistic laws for estimating quadratic variation of continuous semimartingales with the realized rangebased variance; a statistic that replaces every squared return of realized variance with a normalized squared range. If the entire sample path of the process is available and given weak conditions our statistic is consistent and has a mixed Gaussian limit with five times the precision of realized variance. In practice, of course, inference is drawn from discrete data and true ranges are unobserved, leading to downward bias. We solve this problem to give a consistent, mixed normal estimator, irrespective of nontrading. It has varying degrees of efficiency over realized variance, depending on how many observations that are used to construct the highlow. The methodology is applied to TAQ data and compared with realized variance. Our findings suggest the empirical path of quadratic variation is also estimated better with the intraday highlow statistic.
Jump robust volatility estimation using nearest neighbor truncation
, 2009
"... We propose two new jumprobust estimators of integrated variance based on highfrequency return observations. These MinRV and MedRV estimators provide an attractive alternative to the prevailing bipower and multipower variation measures. Specifically, the MedRV estimator has better theoretical effic ..."
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Cited by 16 (3 self)
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We propose two new jumprobust estimators of integrated variance based on highfrequency return observations. These MinRV and MedRV estimators provide an attractive alternative to the prevailing bipower and multipower variation measures. Specifically, the MedRV estimator has better theoretical efficiency properties than the tripower variation measure and displays better finitesample robustness to both jumps and the occurrence of “zero” returns in the sample. Unlike the bipower variation measure the new estimator allows for the development of an asymptotic limit theory in the presence of jumps. Finally, it retains the local nature associated with the low order multipower variation measures. This proves essential for alleviating finite sample biases arising from the pronounced intraday volatility pattern which afflict alternative jumprobust estimators based on longer blocks of returns. An empirical investigation of the Dow Jones 30 stocks and an extensive simulation study corroborate the robustness and efficiency properties of the new estimators.