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Large volatility matrices are involved in many finance practices, and estimating large volatility matrices based on high-frequency financial data encounters the \"curse of dimensionality\". It is a common approach to impose a sparsity assumption on the large volatility matrices to produce consistent volatility matrix estimators. However, due to the existence of common factors, assets are highly correlated with each other, and it is not reasonable to assume the volatility matrices are sparse in financial applications. This paper incorporates factor influence in the asset pricing model and investigates large volatility matrix estimation under the factor price model together with some sparsity assumption. We propose to model asset prices by assuming that asset prices are governed by common factors and that the assets with similar characteristics share the same association with the factors. We then impose some reasonable sparsity condition on the part of the volatility matrices after accounting for the factor contribution. Under the proposed factor-based model and sparsity assumption, we develop an estimation scheme called \"blocking and regularizing\". Asymptotic properties of the proposed estimator are studied, and its finite sample performance is tested via extensive numerical studies to support theoretical results.

This study clarifies how risks spread across economic sectors and indicates the sectors that are the most affected to help investors with asset allocation and to support them in risk management. Although the Japanese stock market is one of the relatively large global stock markets, no studies have explored volatility spillovers among its sectors. Using the forecast error variance decomposition of the vector autoregressive model, this study examines the volatility spillovers among sectors classified on the Tokyo Stock Exchange. Our findings show that the pattern of volatility spillovers across sectors in the Japanese stock market differs between a few years preceding the coronavirus disease 2019 (pre-COVID-19), from 2014 to 2019, and during the COVID-19 period, in 2020. Although the energy resources and bank sectors are risk receivers in the pre-COVID-19 period, these sectors are risk transmitters during the COVID-19 period. We also find that volatility spillovers in the Japanese stock market are mainly driven by negative realized semivariance. These results are useful for asset allocation and risk management.

This study investigates the relationship between the volatility risk premia (VRP) of stock and oil returns. Using daily data on VRP from 10 May 2007 to 16 May 2017, VAR analyses on the stock and oil VRP are conducted, and it is found that the effects of the stock VRP on the oil VRP are limited and, if any, short-lived. In contrast, the VRP of oil has significantly positive and long-lasting effects on the stock VRP after the financial crisis. These results suggest that investors’ sentiments (measured by VRP) are transmitted from the oil to the stock market over time, but not vice versa. This is unexpected because the financialization of commodities means a massive increase in investment in commodities by investors in the traditional stock and bond markets; hence, the direction of effects is thought to be from the stock to the commodity market.

Despite the growing interest in realized stochastic volatility models, their estimation techniques, such as simulated maximum likelihood (SML), are computationally intensive. Based on the realized volatility equation, this study demonstrates that, in a finite sample, the quasi-maximum likelihood estimator based on the Kalman filter is competitive with the two-step SML estimator, which is less efficient than the SML estimator. Regarding empirical results for the S&P 500 index, the quasi-likelihood ratio tests favored the two-factor realized asymmetric stochastic volatility model with the standardized t distribution among alternative specifications, and an analysis on out-of-sample forecasts prefers the realized stochastic volatility models, rejecting the model without the realized volatility measure. Furthermore, the forecasts of alternative RSV models are statistically equivalent for the data covering the global financial crisis.

We examine the predictive power of a daily newspaper-based index of uncertainty associated with infectious diseases (EMVID) for oil-market volatility. Using the heterogeneous autoregressive realized volatility (HAR-RV) model, we document a positive effect of the EMVID index on the realized volatility of crude oil prices at the highest level of statistical significance, within-sample. Importantly, we show that incorporating EMVID into a forecasting setting significantly improves the forecast accuracy of oil realized volatility at short-, medium-, and long-run horizons. Our findings comprise important implications for investors and risk managers during the unprecedented episode of high uncertainty resulting from the COVID-19 pandemic.

In this paper, we examine extreme spillovers among the realized volatility of various energy, metals, and agricultural commodities over the period from September 23, 2008, to June 1, 2020. Using high-frequency (5-min) price data on commodity futures, we compute daily realized volatility and then apply quantile-based connectedness measures. The results show that the connectedness measures estimated at the lower and upper quantiles are much higher than those estimated at the median, implying that realized volatility shocks circulate more intensely during extreme events relative to normal periods, which endangers the stability of the system of volatility connectedness under extreme events such as the COVID19 outbreak. There is evidence of a strong asymmetry between the behaviour of volatility spillovers in lower and upper quantiles, given that the connectedness measures estimated at the upper quantile are the highest. The main results are robust to rolling window size and other alternative choices. Our analyses matter to investors and policy makers who are concerned with the stability of commodity markets.

This study aims to examine the issue of cryptocurrency volatility modelling and forecasting based on high-frequency data. More specifically, this study assesses whether crisis periods, particularly the coronavirus disease pandemic, influence the dynamic of cryptocurrency volatility. We investigate the four main cryptocurrency markets (Bitcoin, Ethereum Classic, Ethereum, and Ripple) from April 2018 to June 2020. The realized volatility measure is computed and decomposed to various components (continuous versus discontinuous, positive and negative semi-variances, and signed jumps). A variety of heterogeneous autoregressive (HAR) models are developed including these components, thereby enabling assessment of different assumptions (including persistence and asymmetric dynamic) of modelling and volatility forecasting based on in-sample and out-of-sample forecasting strategies, respectively. Our results reveal three main findings. First, the extended HAR model that includes the positive and negative jumps appears to be the best model for predicting future volatility for both crisis and non-crisis periods. Second, during the crisis period, only the negative jump component is statistically significant. Third, in terms of volatility forecasting, the results show that the extended HAR model that includes positive and negative semi-variances outperform the other models.

This paper examines the dynamics of connectedness among the realized volatility indices of 16 clean energy stocks belonging to the SPGCE and the implied volatility indices of two important stock markets—the S&P 500 and the STOXX50—and two commodities markets—the crude oil and gold markets. The empirical results show a unidirectional connectedness from the implied volatility indices to the clean energy stocks. Our analysis further reveals similar volatility connectedness behaviors among companies in the same energy production subsector. However, there exists heterogeneous behavior between different energy production subsectors over time. Further, we identify pairwise directional connectedness clusters among related companies, indicating that there are few possibilities for portfolio diversification within the energy production subsectors. Finally, through an impulse–response analysis, we confirm that the expectation of future market volatility of the S&P 500 index and the gold price plays a leading role in volatility connectedness with clean energy stocks.

The high energy consumption of cryptocurrency transactions has raised concerns about the environment and sustainability among green investors and regulatory authorities. The current study examines the connectedness among clean energy, Bitcoin, the stock market, and crude oil empirically. The time-varying parameter vector autoregression (TVP-VAR) is used to estimate the dynamics of connectedness in a daily dataset spanning the period November 11, 2013 to September 30, 2021. We find that the clean energy and traditional stock markets transmit shocks to Bitcoin and oil in terms of return, and they receive shocks in terms of volatility from Bitcoin and oil. Additionally, Bitcoin and other financial markets are only tenuously linked during non-crisis periods. Nonetheless, their connection strengthens substantially during times of crisis, such as the great cryptocurrency crash of 2018 and the COVID-19 pandemic of 2020. We believe that these findings can help explain how clean energy and cryptocurrency markets are linked during times of crisis.

We consider microstructure as an arbitrary contamination of the underlying latent securities price, through a Markov kernel Q. Special cases include additive error, rounding and combinations thereof. Our main result is that, subject to smoothness conditions, the two scales realized volatility is robust to the form of contamination Q. To push the limits of our result, we show what happens for some models that involve rounding (which is not, of course, smooth) and see in this situation how the robustness deteriorates with decreasing smoothness. Our conclusion is that under reasonable smoothness, one does not need to consider too closely how the microstructure is formed, while if severe non-smoothness is suspected, one needs to pay attention to the precise structure and also the use to which the estimator of volatility will be put.