Market variance risk premiums in Japan for asset predictability

Ubukata, Masato; Watanabe, Toshiaki

doi:10.1007/s00181-013-0741-2

Cited by 10 publications

(7 citation statements)

References 36 publications

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“…The parameters of the AHAR-CJ-MFIV model can be estimated by simple linear regression. For the realized variance forecasting, we assume the normality assumption on the error term because the distribution of ln𝑅𝑉 𝑡 is much closer to the normal distribution than that of 𝑅𝑉 𝑡 (e.g., Andersen et al, 2001;Ubukata & Watanabe, 2014). Under this assumption, the forecasts of 𝑅𝑉 𝑡 , which is log-normal distributed, can be obtained from the forecasts of ln𝑅𝑉 𝑡 and the estimates of 𝜎 𝜈 2 .…”

Section: Definition and Calculation Of Variance Risk Premiummentioning

confidence: 99%

“…The VRP is a forward-looking variable, so if one faces high VRP, one has a high degree of risk aversion to the future economy, meaning firms may refrain from new investments and pause hiring new workers. Therefore, it is interesting to investigate whether the VRP components affect real economic activity (Oya, 2011;Ubukata & Watanabe, 2014;Dierkes et al, 2021). GDP data for output growth are available only quarterly or annually.…”

Section: Predicting the Composite Index Of Coincident Indicatorsmentioning

confidence: 99%

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Variance Risk Premium Components in Japan for Predictability: Evidence from the COVID-19 Pandemic

Ubukata¹

2023

IJEF

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The literature on asset predictability suggests the usefulness of the variance risk premium (VRP) and its diffusive and jump risk components as predictors that can yield an improved forecast power. This study investigates whether there is a robust and statistically significant relation between the VRP components and the future Japanese composite index of coincident indicators (CI) and credit spreads (CS), including the outbreak of the COVID-19 pandemic which has caused economic conditions and financial markets to become unstable. The main empirical results are as follows: (i) our rolling window predictive regressions indicate the stability of the significantly negative relation between the diffusive risk component of the VRP and the future CI; (ii) the significantly positive relation of the jump risk component of the VRP and the future lower-rated CS is hampered by the inclusion of the COVID-19 period when the Bank of Japan purchased large-scale corporate bonds under the continuing Japanese expansionary monetary policy; and (iii) the diffusive risk component is partly affected by the impact of the COVID-19 pandemic, but remains significantly positive relation with the future higher- and lower-rated CS.

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Section: Definition and Calculation Of Variance Risk Premiummentioning

confidence: 99%

Section: Predicting the Composite Index Of Coincident Indicatorsmentioning

confidence: 99%

Variance Risk Premium Components in Japan for Predictability: Evidence from the COVID-19 Pandemic

Ubukata¹

2023

IJEF

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“…Bollerslev et al (2016) introduced the HAR quarticity (HARQ) model, which can handle the time-varying coefficients of the HAR model. In terms of asymmetric modeling, Ubukata and Watanabe (2014) and Bekaert and Hoerova (2014) proposed an asymmetric HAR model. Watanabe (2020) proposed an asymmetric HAR-CJ model and an asymmetric HARQ model.…”

Section: Literature Review Of Volatility Forecasting Modelsmentioning

confidence: 99%

“…We use the NEEDS Tick Data File provided by NIKKEI Media Marketing (NIKKEI Media Marketing NEEDS Tick Data n.d.) for RV calculation (Section 3.1.1) and stock full-board dataset preprocessing (Section 3.1.2). Before calculation and preprocessing, we thin out every 5 min according to previous studies (Ubukata and Watanabe 2014). Most previous researchers reported that the smaller the interval, the larger the market microstructure noise that may be contained during the RV calculation.…”

Section: Needs Tick Data File Preprocessingmentioning

confidence: 99%

New Dataset for Forecasting Realized Volatility: Is the Tokyo Stock Exchange Co-Location Dataset Helpful for Expansion of the Heterogeneous Autoregressive Model in the Japanese Stock Market?

Higashide

Tanaka

Kinkyo

et al. 2021

JRFM

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This study analyzes the importance of the Tokyo Stock Exchange Co-Location dataset (TSE Co-Location dataset) to forecast the realized volatility (RV) of Tokyo stock price index futures. The heterogeneous autoregressive (HAR) model is a popular linear regression model used to forecast RV. This study expands the HAR model using the TSE Co-Location dataset, stock full-board dataset and market volume dataset based on the random forest method, which is a popular machine learning algorithm and a nonlinear model. The TSE Co-Location dataset is a new dataset. This is the only information that shows the transaction status of high-frequency traders. In contrast, the stock full-board dataset shows the status of buying and selling dominance. The market volume dataset is used as a proxy for liquidity and is recognized as important information in finance. To the best of our knowledge, this study is the first to use the TSE co-location dataset. The experimental results show that our model yields a higher forecast out-of-sample accuracy of RV than the HAR model. Moreover, we find that the TSE Co-Location dataset has become more important in recent years, along with the increasing importance of high-frequency trading.

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“…A test for equity risk premium under some specifications of conditional expectation of the Nikkei 225 returns with realized volatility implies that the data used in our empirical analysis may be insensitive to estimated parameters of the market price of equity risk. Besides equity risk compensation, many studies provide empirical results regarding the volatility risk premium where the risk‐neutral conditional expectation of the forward return variation is larger than the physical one (Bollerslev et al ., , ; Carr and Wu, ; Zhou, ; Ubukata and Watanabe, ). The literature on changing probability measures accounting for both the equity and volatility risk premiums is under development.…”

Section: Introductionmentioning

confidence: 99%

Pricing Nikkei 225 Options Using Realized Volatility

Ubukata

Watanabe

2013

Japanese Economic Review

Self Cite

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This article examines option pricing performance using realized volatilities with or without handling microstructure noise, non-trading hours and large jumps. The dynamics of realized volatility is specified by ARFIMA(X) and HAR(X) models. Main results using put options on the Nikkei 225 index are: (1) ARFIMAX model performs best, (2) the Hansen and Lunde (2005a) adjustment for non-trading hours improves the performance, (3) methods for reducing microstructure noise-induced bias yield better performance, while if the Hansen-Lunde adjustment is used, the other methods are not necessarily needed and (4) the performance is unaffected by removing large jumps from realized volatility. JEL Classification Numbers: C13, C22, C52.

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Market variance risk premiums in Japan for asset predictability

Cited by 10 publications

References 36 publications

Variance Risk Premium Components in Japan for Predictability: Evidence from the COVID-19 Pandemic

Variance Risk Premium Components in Japan for Predictability: Evidence from the COVID-19 Pandemic

New Dataset for Forecasting Realized Volatility: Is the Tokyo Stock Exchange Co-Location Dataset Helpful for Expansion of the Heterogeneous Autoregressive Model in the Japanese Stock Market?

Pricing Nikkei 225 Options Using Realized Volatility

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