Abstract
This article evaluates the predictive performance of variance risk premiums (VRPs) in Japan on the Nikkei 225 returns, credit spreads, and the composite index of coincident indicators. Different monthly VRPs, such as expected and ex-post VRPs, are measured by using model-free implied and realized variances from option prices and high-frequency (HF) data, and their predictive ability is compared with that of VRPs using a realized measure based on coarser frequency return observations. The empirical results show that the VRPs in Japan with HF data are useful in predicting credit spreads and the composite index of coincident indicators, but lose their predictive ability for the Nikkei 225 returns. Such significant predictive power tends to be greater for the expected VRPs with HF data relative to the ex-post VRP with HF data and VRPs with daily data as well as for lower investment grade credit spreads.
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Notes
The literature on market microstructure provides some insights from early studies, including Roll (1984), who derives a simple estimator of the bid-ask spread based on the negative autocovariance of returns. In the literature on microstructure noise, Hansen and Lunde (2006) examine the variance of microstructure noise as well as the correlation between the microstructure noise and the frictionless equilibrium price. Ubukata and Oya (2009) propose test statistics for the dependence of macrostructure noise processes and capture the various dependence patterns.
We also calculate other realized variance measures such as realized variance with 15-min returns and the bias-corrected two-scale estimator introduced in Zhang et al. (2005) and Bandi and Russell (2011). We confirm that the predictive ability of VRPs is robust to different realized variance measures taking account of microstructure noise.
We confirm that the results for predictability do not change substantially when we employ another model-free implied variance that is robust to the presence of large jumps proposed by Du and Kapadia (2012).
VRP is often defined as \(\mathrm{{VRP}}_t \equiv E^\mathbb{P }_t [\mathcal{V }_{t,t+1}] - E^\mathbb{Q }_t [\mathcal{V }_{t,t+1}]\) in the finance literature. For convenience, we use this expression in (7).
We use a covariance matrix with a Bartlett kernel and a lag length determined by \(h+4((T-h)/100)^{2/9}\) where \(T\) is the sample size in the regression.
Such different predictability patterns for credit spreads are not found in the previous studies of Zhou (2010) and Wang et al. (2013). The stronger predictability for lower investment grade credit spreads is consistent with the evidence reported in Wang et al. (2013), who use firm-level VRP in the US market.
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Acknowledgments
The authors are grateful to the associate editor and two anonymous referees for their helpful comments and suggestions. The authors also thank Torben Andersen, Tim Bollerslev, Peter Hansen, and the participants in the second international conference “High Frequency Data Analysis in Financial Markets” for valuable comments. Financial support from the Ministry of Education, Culture, Sports, Science and Technology of the Japanese Government through Grant-in-Aid for Scientific Research (Nos. 18203901; 21243018; 22243021; 23730301; 25780154), the Global COE program “Research Unit for Statistical and Empirical Analysis in Social Sciences” at Hitotsubashi University and the Joint Usage and Research Center, Institute of Economic Research, Hitotsubashi University (IERPK1109) is gratefully acknowledged.
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Ubukata, M., Watanabe, T. Market variance risk premiums in Japan for asset predictability. Empir Econ 47, 169–198 (2014). https://doi.org/10.1007/s00181-013-0741-2
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DOI: https://doi.org/10.1007/s00181-013-0741-2