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81
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 36 (6 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.
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 24 (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.
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 22 (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...
Optimal filtering of jump diffusions: extracting latent states from asset prices”, Working Paper, http://wwwstat.wharton.upenn.edu/ stroud/pubs.html
, 2006
"... 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 20 (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. 2 1
A NoArbitrage Approach to RangeBased Estimation of Return Covariances and Correlations
, 2003
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Financial asset returns, directionofchange forecasting and volatility dynamics
, 2003
"... informs doi 10.1287/mnsc.1060.0520 ..."
2009, “What Ties Return Volatilities to Price Valuations and Fundamentals?,” Working paper
"... The relation between the volatility of stocks and bonds and their price valuations is strongly timevarying, both in magnitude and direction, defying traditional asset pricing models and conventional wisdom. We construct and estimate a model in which investors ’ learning about regular and unusual fu ..."
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Cited by 13 (1 self)
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The relation between the volatility of stocks and bonds and their price valuations is strongly timevarying, both in magnitude and direction, defying traditional asset pricing models and conventional wisdom. We construct and estimate a model in which investors ’ learning about regular and unusual fundamental states leads to a nonmonotonic V −shaped relation between volatilities and prices. Structural forecasts from our model predict future return volatility and covariances with R2 ranging between 40 % and 60 % at the 1year horizon. The model’s success stems largely from backing out the endogenous and timevarying pro (counter) cyclical weights that investors assign to earnings (inflation) news. While it is intuitive that the volatilities and comovements of stocks and bonds are strongly related to the state of economic fundamentals, it is surprising that the financial literature has been unable to empirically demonstrate such a strong link between them, as evidenced in the following quote from a recent paper by Nobel prize laureate Robert Engle.
The Econometrics of Option Pricing
"... The growth of the option pricing literature parallels the spectacular developments of derivative securities and the rapid expansion of markets for derivatives in the last three decades. Writing a survey of option pricing models appears therefore like a formidable task. To delimit our focus we will ..."
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Cited by 12 (1 self)
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The growth of the option pricing literature parallels the spectacular developments of derivative securities and the rapid expansion of markets for derivatives in the last three decades. Writing a survey of option pricing models appears therefore like a formidable task. To delimit our focus we will put emphasis on the more recent contributions since there are
2004), “A Discrete Sine Transform Approach for Realized Volatility Measurement,” Working
, 2004
"... Realized volatility affords the expost empirical measurement of the latent notional volatility. However, the timevarying returns autocorrelation induced by microstructure effects represents a challenging problem for standard volatility measures. In this study, a new nonparametric volatility measur ..."
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Cited by 10 (0 self)
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Realized volatility affords the expost empirical measurement of the latent notional volatility. However, the timevarying returns autocorrelation induced by microstructure effects represents a challenging problem for standard volatility measures. In this study, a new nonparametric volatility measures based on the Discrete Sine Transform (DST) is proposed. We show that the DST exactly diagonalizes the covariance matrix of MA(1) process. This original result provides us an orthonomal basis decomposition of the return process which permits to optimally disentangle the underlying efficient price signal from the timevarying nuisance component contained in tickbytick return series. As a result, two nonparametric volatility estimators which fully exploit all the available information contained in high frequency data are constructed. Moreover the DST orthogonalization allow us to analytically compute the score and the Fischer information matrix of MA(1) processes. In discussing efficient numerical procedures for the likelihood maximizations we also suggest that DST estimator would represent the most valid starting point for the numerical maximization of the likelihood. Monte Carlo simulations based on a realistic model for microstructure effects show the superiority of DST estimators, compared to alternative local volatility proxies for every level of the noise to signal ratio and a large class of noise contaminations. These properties make the DST approach a nonparametric method able to cope with timevarying autocorrelation, in a simple and efficient way, providing robust and accurate volatility estimates under a wide set of realistic conditions. Moreover, its computational efficiency makes it well suitable for realtime analysis of high frequency data.