This paper develops a fully-fledged statistical arbitrage strategy based on a mean-reverting jump-diffusion model and … stock selection and trading framework identifies overnight price gaps based on an advanced jump test procedure and exploits …

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 …

This paper proposes a semiparametric realized stochastic volatility model by integrating the parametric stochastic … volatility model utilizing realized volatility information and the Bayesian nonparametric framework. The flexible framework … returns and logarithmic realized volatility but also enables flexible adjustments for estimation bias in realized volatility …

be achieved by adjusting a lattice’s configuration (e.g., grid sizes and jump patterns). Using this framework as a …

This paper proposes the sample path generation method for the stochastic volatility version of the CGMY process. We …

The paper builds a Variance-Gamma (VG) model with five parameters: location (μ), symmetry (δ), volatility (σ), shape (α … five-parameter VG model is a stochastic volatility model with a Γ(α,θ) Ornstein-Uhlenbeck type process; the associated Lévy …

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

use of computational methods and techniques for modelling financial asset prices, returns, and volatility, and on the use …

implied volatility surface (up to 100%) and on two risk measures: value at risk and expected shortfall where an increase of up …

derive the asymptotic hedging error for options under a generalised jump-diffusion model with kernel bias, which nests a …