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 models have revolutionised the field of option pricing by allowing the volatility of an asset to vary randomly over time rather than remain constant. These models have ...

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

The traditional approach to stochastic volatility (SV) modelling begins with the specification of an SV process, typically on the grounds of its analytical tractability (see, for example, Heston, 1993 ...

Peter Friz, Paolo Pigato and Jonathan Seibel propose a modification of a given stochastic volatility model ‘backbone’ capable of producing extreme short-dated implied skews, without adding jumps or ...

This article uses a Bayesian unit-root test in stochastic volatility models. The time series of interest is the volatility that is unobservable. The unit-root testing is based on the posterior odds ...

The ability to compute exotic greeks is important in explaining profit and loss statements, but what is the best way to calculate them effectively? In a virtual talk for the Bloomberg Quant (BBQ) ...

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 ...

Alternative multivariate stochastic volatility (MSV) models with leverage have been proposed in the literature. However, the existing MSV with leverage models are unclear about the definition of ...

Whether the financial markets are turbulent or calm, the subject of volatility has been of great interest to quants for decades. Some of the pioneering research was published in the mid-1990s, ...