Affine processes provide a versatile framework for modelling complex financial phenomena, ranging from interest rate dynamics to credit risk and beyond. Their defining characteristic is the affine, or ...

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 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 ...

This paper is devoted to the price–storage dynamics in natural gas markets. A novel stochastic path-dependent volatility model is introduced with path dependence in both price volatility and storage ...

We extend the existing small-time asymptotics for implied volatilities under the Heston stochastic volatility model to the multifactor volatility Heston model, which is also known as the Wishart ...

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) ...

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, ...

Eduardo Abi Jaber introduces a simple, efficient and accurate numerical scheme that preserves non-negativity for simulating the square-root process. The novel idea is to first simulate the integrated ...

Citations: Andersen, Torben Gustav, Tim Bollerslev. 1996. GMM Estimation of a Stochastic Volatility Model: A Monte Carlo Study. Journal of Business & Economic Statistics. (3)328-352.