market uncertainty and volatility of the investment instruments. Thus, the prediction of the uncertainty and volatilities of … to identify the best fit model that can predict the volatility of return of Bitcoin, which is in high demand as an … the residuals of the average equation model selected have ARCH effect. Volatility of Bitcoin return series after detection …

Due to the high relevance of 1-day volatility forecasts and the increasing demand for zero-day-to-expiration (0DTE …) options on the S&P 500, the Cboe recently introduced the 1-Day Volatility Index (VIX1D). Compared to the longer …-term volatility indices of the VIX family, it is overall lower and more volatile, shows a weaker negative correlation with the S&P 500 …

instrumental-variables based tests which involve enforcement or partial enforcement of the null hypothesis in variance estimation …

outperforms the other models in model estimation and daily out-of-sample volatility forecasting of the two indices. This study is … for forecasting the volatility of both the TASI and the TIPISI in the context of petrochemical industries, as this model …

-step state-space estimation approach the four-factor model produces accurate forecasts and outperforms competitor models across …

Realized Volatility and the Variance Risk Premium. The approach is innovative along two different dimensions, namely: (1) we …-type models and (2) we price equity volatility risk using factors which go beyond the equity class. These are volatility factors …

leverage effect and maintains a mathematical structure that facilitates volatility estimation. A class of bivariate models that … volatility information improves the day volatility estimation. The results indicate a forecasting improvement using bivariate …We propose a methodology to include night volatility estimates in the day volatility modeling problem with high …

-varying parameter vector autoregression with stochastic volatility developed by Cogley and Sargent (2005) and Primiceri (2005), CSP …