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 ...

This paper examines the application of various stochastic volatility models to real data and demonstrates their effectiveness in calibrating a wide range of options, including those with short-term ...

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, ...

As stablecoins become more deeply integrated into cross-border payments and compliant financial systems, 2026 is widely seen ...

We provide a simple, yet highly effective framework for forecasting return volatility by combining exponential generalized autoregressive conditional heteroscedasticity models with data on the range.

It was surely only a matter of time before someone applied the principles of Einstein’s general theory of relativity to options trading. Lyudmil Zyapkov, a senior quantitative analyst at Bank of ...

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 ...

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, ...

In today's rapidly changing economic landscape, resilience has become a cornerstone for businesses aiming to thrive amid volatility. I have found that the ability to quickly adapt to market changes, ...