difficulties for the use of Fourier inversion methodologies in volatility surface calibration. Continuous time Markov chain …

memory in stochastic volatility (SV) components in order to develop the General Long Memory SV (GLMSV) model. We examine the …

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 …

asset price volatility. We show how the squared low-frequency returns can be expressed in terms of the temporal aggregation … properties of the spectral density function of realized volatility series, constructed from squared returns with different … new features of volatility in financial market indices. The theoretical findings are illustrated via the analysis of both …

The paper examines the relative performance of Stochastic Volatility (SV) and GARCH(1,1) models fitted to twenty plus … years of daily data for three indices. As a benchmark, I use the realized volatility (RV) for the S&P 500, DOW JONES and … volatility models, if the simple expedient of using lagged squared demeaned daily returns provides a better RV predictor, at …

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 …

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

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