Generalized pricing formulas for stochastic volatility jump diffusion models applied to the exponential Vasicek model

Path integral techniques for the pricing of financial options are mostly based on models that can be recast in terms of a Fokker-Planck differential equation and that, consequently, neglect jumps and only describe drift and diffusion. We present a method to adapt formulas for both the path-integral propagators and the option prices themselves, so that jump processes are taken into account in conjunction with the usual drift and diffusion terms. In particular, we focus on stochastic volatility models, such as the exponential Vasicek model, and extend the pricing formulas and propagator of this model to incorporate jump diffusion with a given jump size distribution. This model is of importance to include non-Gaussian fluctuations beyond the Black-Scholes model, and moreover yields a lognormal distribution of the volatilities, in agreement with results from superstatistical analysis. The results obtained in the present formalism are checked with Monte Carlo simulations.Comment: 9 pages, 2 figures, 1 tabl

Incomplete financial markets and jumps in asset prices

For incomplete financial markets, jumps in both prices and consumption can be unavoidable. We consider pure-exchange economies with infinite horizon, discrete time, uncertainty with a continuum of possible shocks at every date. The evolution of shocks follows a Markov process, and fundamentals depend continuously on shocks. It is shown that: (1) equilibria exist; (2) for effectively complete financial markets, asset prices depend continuously on shocks; and (3) for incomplete financial markets, there is an open set of economies U such that for every equilibrium of every economy in U, asset prices at every date depend discontinuously on the shock at that date

Discrete-time volatility forecasting with persistent leverage effect and the link with continuous-time volatility modeling

We first propose a reduced-form model in discrete time for S&P 500 volatility showing that the forecasting performance can be significantly improved by introducing a persistent leverage effect with a long-range dependence similar to that of volatility itself. We also find a strongly significant positive impact of lagged jumps on volatility, which however is absorbed more quickly. We then estimate continuous-time stochastic volatility models that are able to reproduce the statistical features captured by the discrete-time model. We show that a single-factor model driven by a fractional Brownian motion is unable to reproduce the volatility dynamics observed in the data, while a multifactor Markovian model fully replicates the persistence of both volatility and leverage effect. The impact of jumps can be associated with a common jump component in price and volatility