International Journal of Theoretical and Applied Finance

Families of explicit solutions are found to a nonlinear Black–Scholes equation which incorporates the feedback-effect of a large trader in case of market illiquidity. The typical solution of these families will have a payoff which approximates a strangle. These solutions were used to test numerical schemes for solving a nonlinear Black–Scholes equation.

We give a simple proof that in applications there is no need to track the branch-cut of the complex logarithm in the Heston model when using the Lewis-Lipton formula in the right way.

In this paper we propose a new finite element method for pricing of bond options under time inhomogeneous one-factor affine models of short interest rates: the Hull–White model and the extended CIR model. The stability and weak convergence are established. Numerical results are presented to examine the method and to compare the calibrated models.

This paper develops bridge sampling path integral algorithms for pricing path-dependent options under a new class of nonlinear state dependent volatility models. Path-dependent option pricing is considered within a new (dual) Bessel family of semimartingale diffusion models, as well as the constant elasticity of variance (CEV) diffusion model, arising as a particular case of these models. The transition p.d.f.s or pricing kernels are mapped onto an underlying simpler squared Bessel process and are expressed analytically in terms of modified Bessel functions. We establish precise links between pricing kernels of such models and the randomized gamma distributions, and thereby demonstrate how a squared Bessel bridge process can be used for exact sampling of the Bessel family of paths. A Bessel bridge algorithm is presented which is based on explicit conditional distributions for the Bessel family of volatility models and is similar in spirit to the Brownian bridge algorithm. A special rearrangement and splitting of the path integral variables allows us to combine the Bessel bridge sampling algorithm with either adaptive Monte Carlo algorithms, or quasi-Monte Carlo techniques for significant numerical efficiency improvement. The algorithms are illustrated by pricing Asian-style and lookback options under the Bessel family of volatility models as well as the CEV diffusion model.

A recent study linked the behavior of buying and selling in financial markets to the phenomenon of phase transitions that occur in physical systems with many interacting components. We urge caution with such findings. In particular, we show that the statistical technique employed in the study referred to above to analyze stock trading data also produces evidence of two-phase behavior when used to analyze a simple sequence of independent and identically distributed random variables.

In this paper we develop a method to find the price of several options whose payoff depends on a volume weighted average price (VWAP). Fixed and floating strike VWAP, together with digital VWAP contracts are considered. Throughout we assume that the stock follows a geometric Brownian motion and the rate of trades evolves as a mean reverting process. It is assumed that the VWAP at the final time has a lognormal distribution. The parameters of the approximating lognormal distribution are obtained by matching the first two moments of the volume weighted average price with a lognormal process. A price is then obtained for the fixed strike and digital options when the market price of risk is a constant. We concentrate on the price for calls, prices for puts can be obtained in an analogous manner.

Using no arbitrage principle, we derive a relation between the drift term of risk-neutral dynamics for instantaneous variance and the term structure of forward variance. We show that the forward variance curve can be derived from options market. Based on the variance term structure, we derive a no arbitrage pricing model for VIX futures pricing. The model is the first no arbitrage model combining options market and VIX futures market. The model can be easily generalized to price other volatility derivatives.

This paper discusses the asymptotic property of estimators for optimal portfolios when the returns are vector-valued locally stationary processes. First, we derive the asymptotic distribution of a nonparametric portfolio estimator based on the kernel method. Optimal bandwidth and kernel function are given by minimizing the mean squares error of it. Next, assuming parametric models for non-Gaussian locally stationary processes, we prove the LAN theorem, and propose a parametric portfolio estimator ĝ based on a quasi-maximum likelihood estimator. Then it is shown that ĝ is asymptotically efficient based on the LAN. Numerical studies are provided to investigate the accuracy of the portfolio estimators parametrically and nonparametrically. They illuminate some interesting features of them.

The defaultable forward rate is modelled as a jump diffusion process within the Schönbucher [26,27] general Heath, Jarrow and Morton [20] framework where jumps in the defaultable term structure f^d(t,T) cause jumps and defaults to the defaultable bond prices P^d(t,T). Within this framework, we investigate an appropriate forward rate volatility structure that results in Markovian defaultable spot rate dynamics. In particular, we consider state dependent Wiener volatility functions and time dependent Poisson volatility functions. The corresponding term structures of interest rates are expressed as finite dimensional affine realizations in terms of benchmark defaultable forward rates. In addition, we extend this model to incorporate stochastic spreads by allowing jump intensities to follow a square-root diffusion process. In that case the dynamics become non-Markovian and to restore path independence we propose either an approximate Markovian scheme or, alternatively, constant Poisson volatility functions. We also conduct some numerical simulations to gauge the effect of the stochastic intensity and the distributional implications of various volatility specifications.