Which power variation predicts volatility well?

Ghysels, Éric; Sohn, Bumjean

doi:10.1016/j.jempfin.2009.03.002

Cited by 22 publications

(10 citation statements)

References 30 publications

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“…In the context of high frequency data, Liu and Maheu (2005) and Ghysels and Sohn (GS: 2009) study the predictability of future realized volatility using past absolute power variations and multipower variations. Ghysels and Sohn (2009) find that the optimal value of  is approximately unity. However, their empirical evidence considers the continuous class of models, and does not account for jumps.…”

Section: Introductionmentioning

confidence: 86%

Empirical Evidence on the Importance of Aggregation, Asymmetry, and Jumps for Volatility Prediction

Duong

Swanson

2013

SSRN Journal

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Many recent modelling advances in finance topics ranging from the pricing of volatility-based derivative products to asset management are predicated on the importance of jumps, or discontinuous movements in asset returns. In light of this, a number of recent papers have addressed volatility predictability, some from the perspective of the usefulness (2011), and the references cited therein. In this paper, we review the extant literature and then present new empirical evidence on the predictive content of realized measures of jump power variations (including upside and downside risk, jump asymmetry, and truncated jump variables), constructed using instantaneous returns, i.e., |  |   0 ≤  ≤ 6, in the spirit of Ding, Granger and Engle (1993) and Ding and Granger (1996). We also present new empirical evidence on the predictive content of realized measures of truncated large jump variations, constructed using truncated squared instantaneous return, i.e.,   2 ×  ||  where  is the threshold jump size Our prediction experiments use high frequency price returns constructed using S&P500 futures data as well as stocks in the Dow 30, and our empirical implementation involves estimating linear and nonlinear heterogeneous autoregressive realized volatility (HAR-RV) type models. We find that past "large" jump power variations help less in the prediction of future realized volatility, than past "small" jump power variations. Additionally, we find evidence that past realized signed jump power variations, which have not previously been examined in this literature, are strongly correlated with future volatility, and that past downside jump variations matter in prediction. Finally, incorporation of downside and upside jump power variations does improve predictability, albeit to a limited extent.JEL Classification: C58, C53, C22.

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Section: Introductionmentioning

confidence: 86%

Empirical Evidence on the Importance of Aggregation, Asymmetry, and Jumps for Volatility Prediction

Duong

Swanson

2013

SSRN Journal

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“…Taylor (2002), respectively. 12 Forsberg and Ghysels (2007) and Ghysels and Sohn (2009), note that other power transformations may be used to model the dynamics of the realized volatility. These studies show that for a number of stochastic volatility processes used in the financial literature the absolute value of the realized volatility is a better predictor of the future realized volatility, particularly for longer horizons.…”

Section: Heterogeneous Autoregressive Model Of Realized Volatilitymentioning

confidence: 99%

The Contribution of Jumps to Forecasting the Density of Returns

2017

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The extraction of the jump component in the dynamics of asset prices has witnessed a considerably growing body of literature. Of particular interest is the decomposition of returns' quadratic variation between their continuous and jump components. Recent contributions highlight the importance of this component in forecasting volatility at different horizons. In this article, we extend a methodology developed in Maheu and McCurdy (2011) to exploit the information content of intraday data in forecasting the density of returns at horizons up to sixty days. We follow Boudt et al. (2011) to detect intraday returns that should be considered as jumps. The methodology is robust to intra-week periodicity and further delivers estimates of signed jumps in contrast to the rest of the literature where only the squared jump component can be estimated. Then, we estimate a bivariate model of returns and volatilities where the jump component is independently modeled using a jump distribution that fits the stylized facts of the estimated jumps. Our empirical results for S&P 500 futures, U.S. 10-year Treasury futures, USD/CAD exchange rate and WTI crude oil futures highlight the importance of considering the continuous/jump decomposition for density forecasting while this is not the case for volatility point forecast. In particular, we show that the model considering jumps apart from the continuous component consistently deliver better density forecasts for forecasting horizons ranging from 1 to 30 days.JEL Classification: C15, C32, C53, G1

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“…An enlightened choice of the power transformation of returns can also improve the power of some autocorrelation-based heteroscedasticity tests in Stochastic Volatility (SV) models; see Harvey and Streibel (1998). Moreover, some authors have shown that powers of absolute returns can be used to obtain improved estimators of the parameters of conditional heteroscedastic models and better predictors of future volatilities; see, Deo et al (2006) and Dalla et al (2006) in the context of Long-Memory Stochastic Volatility (LMSV) models, Forsberg and Ghysels (2007) and Ghysels and Sohn (2009) in the context of MIDAS regression models for realized variance and Franq and Zakoian (2009) for GARCH models.…”

Section: Introductionmentioning

confidence: 99%

Maximally Autocorrelated Power Transformations: A Closer Look at the Properties of Stochastic Volatility Models

Ruiz¹,

Pérez²

2012

Studies in Nonlinear Dynamics &Amp; Econometrics

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There has been an increasing interest in the financial econometrics literature on the properties of non-linear transformations of returns. In this paper, we focus on power transformations and analyze which powers of returns provide stronger autocorrelations in the context of stochastic volatility (SV) models. We show that the sample and theoretical autocorrelations implied by short and long memory SV models peak at the same power transformation, which is close to one for realistic values of the model parameters. Consequently, we suggest that the power that maximizes the sample autocorrelations could be used as an additional descriptive tool for the adequacy of a SV model. The results are illustrated with three real series of financial returns. * We are grateful to L. Borge and J. Rodríguez for helping us with Matlab codes. We also thank P.C. Alvarez for his invaluable help in preparing the final version of this paper.

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Which power variation predicts volatility well?

Cited by 22 publications

References 30 publications

Empirical Evidence on the Importance of Aggregation, Asymmetry, and Jumps for Volatility Prediction

Empirical Evidence on the Importance of Aggregation, Asymmetry, and Jumps for Volatility Prediction

The Contribution of Jumps to Forecasting the Density of Returns

Maximally Autocorrelated Power Transformations: A Closer Look at the Properties of Stochastic Volatility Models

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