TY - JOUR
T1 - Probabilistic forecasts of volatility and its risk premia
AU - Maneesoonthorn, Worapree
AU - Martin, Gael Margaret
AU - Forbes, Catherine Scipione
AU - Grose, Simone Deborah
N1 - Funding Information:
This research has been supported by Australian Research Council (ARC) Discovery Grant DP0985234 and ARC Future Fellowship FT0991045. We would like to thank the Editor and two anonymous referees for very detailed, thoughtful and constructive comments on the original draft. We are also grateful to Herman van Dijk and John Geweke, plus various other participants at the: Workshop on Bayesian Econometric Methods (Rimini, 2009); Frontiers in Financial Econometrics Workshop (Princeton, 2009); Econometric Society Australasian Meeting (Canberra, 2009); Bayes on the Beach Workshop (Queensland, 2010); Finance Integrity Research Network Doctoral Tutorial (Melbourne, 2010); European Seminar on Bayesian Econometrics (Rotterdam, 2010), for very helpful comments on earlier versions of the paper.
PY - 2012/12
Y1 - 2012/12
N2 - The object of this paper is to produce distributional forecasts of asset price volatility and its associated risk premia using a non-linear state space approach. Option and spot market information on the latent variance process is captured by using dual 'model-free' variance measures to define a bivariate observation equation in the state space model. The premium for variance diffusive risk is defined as linear in the latent variance (in the usual fashion) whilst the premium for variance jump risk is specified as a conditionally deterministic dynamic process, driven by a function of past measurements. The inferential approach adopted is Bayesian, implemented via a Markov chain Monte Carlo algorithm that caters for the multiple sources of non-linearity in the model and for the bivariate measure. The method is applied to spot and option price data on the S&P500 index from 1999 to 2008, with conclusions drawn about investors' required compensation for variance risk during the recent financial turmoil. The accuracy of the probabilistic forecasts of the observable variance measures is demonstrated, and compared with that of forecasts yielded by alternative methods. To illustrate the benefits of the approach, it is used to produce forecasts of prices of derivatives on volatility itself. In addition, the posterior distribution is augmented by information on daily returns to produce value at risk predictions. Linking the variance risk premia to the risk aversion parameter in a representative agent model, probabilistic forecasts of (approximate) relative risk aversion are also produced.
AB - The object of this paper is to produce distributional forecasts of asset price volatility and its associated risk premia using a non-linear state space approach. Option and spot market information on the latent variance process is captured by using dual 'model-free' variance measures to define a bivariate observation equation in the state space model. The premium for variance diffusive risk is defined as linear in the latent variance (in the usual fashion) whilst the premium for variance jump risk is specified as a conditionally deterministic dynamic process, driven by a function of past measurements. The inferential approach adopted is Bayesian, implemented via a Markov chain Monte Carlo algorithm that caters for the multiple sources of non-linearity in the model and for the bivariate measure. The method is applied to spot and option price data on the S&P500 index from 1999 to 2008, with conclusions drawn about investors' required compensation for variance risk during the recent financial turmoil. The accuracy of the probabilistic forecasts of the observable variance measures is demonstrated, and compared with that of forecasts yielded by alternative methods. To illustrate the benefits of the approach, it is used to produce forecasts of prices of derivatives on volatility itself. In addition, the posterior distribution is augmented by information on daily returns to produce value at risk predictions. Linking the variance risk premia to the risk aversion parameter in a representative agent model, probabilistic forecasts of (approximate) relative risk aversion are also produced.
UR - http://www.scopus.com/inward/record.url?scp=84868207493&partnerID=8YFLogxK
U2 - 10.1016/j.jeconom.2012.06.006
DO - 10.1016/j.jeconom.2012.06.006
M3 - Article
SN - 0304-4076
VL - 171
SP - 217
EP - 236
JO - Journal of Econometrics
JF - Journal of Econometrics
IS - 2
ER -