financial ratios. Stock market volatility is non-Gaussian distributed. It can be approximated by an inverse Gaussian (IG … indicators to help us forecast stock market volatility. Via simulation, we validated the use of four models, i.e., a univariate … us forecast stock market volatility. These are the credit spread between the U.S. Aaa corporate bond yield and the 10 …

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

Precise modeling and forecasting of the volatility of energy futures is vital to structuring trading strategies in spot … measurement of risk. This paper is an attempt to model volatility of energy futures under different distributions. In empirical … analysis, we estimate the volatility of Natural Gas Futures, Brent Oil Futures and Heating Oil Futures through GARCH and APARCH …

options prices under Heston's stochastic volatility (SV) model. We demonstrate that under a particular reparametrization, this … volatility 'smile', which indicates a likely distortion in the Black-Scholes modeling of such option data. Reflective of entirely … different market expectations, this distortion in the volatility 'smile' appears not to exist in the TLT option data. We provide …

one-month variance swap rate, i.e., the CBOE Volatility Index (VIX) accurately. Our research suggests that one should use …

timelines were used to carry out stressed value at risks, and it was seen that during periods of crisis, the volatility of asset …