degree of volatility risk in stock and index returns, where we characterize volatility risk by the extent to which … forecasting errors in realized volatility are substantive. Even though returns standardized by ex post quadratic variation … distribution of returns. Explicitly modeling this volatility risk is fundamental. We propose a dually asymmetric realized …

One of the notable features of bitcoin is its extreme volatility. The modeling and forecasting of bitcoin volatility … volatility were founded on econometric models. Research on bitcoin volatility forecasting using machine learning algorithms is … bitcoin's return volatility and Value at Risk. The objective of this study is to compare their out-of-sample performance in …

This paper investigates the benefits of jointly using several realized measures in predicting daily price volatility … significantly increases the accuracy of volatility forecasts, while in forecasting Value-at-Risk and Expected Shortfall at different …

Developments in the world of finance have led the authors to assess the adequacy of using the normal distribution … timelines were used to carry out stressed value at risks, and it was seen that during periods of crisis, the volatility of asset …

(MRS-MNTS-GARCH) to accommodate fat tails, volatility clustering and regime switch. The volatility of each asset …

markets. We use filtered historical simulations, GARCH models, and stochastic volatility models. The out-of-sample performance …-at-Risk forecasts, which appears to stem from several econometric properties of the return distributions. With stochastic volatility …

This research examines the correlations between the return volatility of cryptocurrencies, global stock market indices …, and the spillover effects of the COVID-19 pandemic. For this purpose, we employed a two-stage multivariate volatility … and respond well to previous shocks. As a result, financial assets have low unconditional volatility and the lowest risk …