Stochastic volatility is the unpredictable nature of asset price volatility over time. It's a flexible alternative to the Black Scholes' constant volatility assumption.

Stochastic volatility represents an essential framework for understanding the dynamic uncertainty inherent in financial markets. This approach extends traditional models by recognising that volatility ...

A new way of estimating stochastic volatility models is developed. The method is based on the existence of autoregressive moving average (ARMA) representations for powers of the log-squared ...

This article empirically compares the Markov-switching and stochastic volatility diffusion models of the short rate. The evidence supports the Markov-switching diffusion model. Estimates of the ...

Citations: Todorov, Viktor. 2009. Estimation of Continuous-Time Stochastic Volatility Models with Jumps Using High-Frequency Data. Journal of Econometrics. 131-148.

A stochastic volatility model where volatility was driven solely by a latent variable called news was estimated for three stock indices. A Markov chain Monte Carlo algorithm was used for estimating ...

Citations: Andersen, Torben Gustav, Hyung-Jin Chung, Bent Sorensen. 1999. Efficient Method of Moments Estimation of a Stochastic Volatility Model: A Monte Carlo Study. Journal of Econometrics.

Unspanned stochastic volatility (USV) refers to the inability of bonds to replicate volatility-sensitive derivative securities. Affine term structure models require special restrictions on the ...

Volatility modeling is no longer just about pricing derivatives—it's the foundation for modern trading strategies, hedging precision, and portfolio optimization. Whether you're trading gold futures, ...