On the application of Wishart process to the pricing of equity derivatives: the multi-asset case

Given the inherent complexity of financial markets, a wide area of research in the field of mathematical finance is devoted to develop accurate models for the pricing of contingent claims. Focusing on the stochastic volatility approach (i.e. we assume to describe asset volatility as an additional stochastic process), it appears desirable to introduce reliable dynamics in order to take into account the presence of several assets involved in the definition of multi-asset payoffs. In this article we deal with the multi asset Wishart Affine Stochastic Correlation model, that makes use of Wishart process to describe the stochastic variance covariance matrix of assets return. The resulting parametrization turns out to be a genuine multi-asset extension of the Heston model: each asset is exactly described by a single instance of the Heston dynamics while the joint behaviour is enriched by cross-assets and cross-variances stochastic correlation, all wrapped in an affine modeling. In this framework, we propose a fast and accurate calibration procedure, and two Monte Carlo simulation schemes.

PRICING MULTI-ASSET AMERICAN OPTION WITH STOCHASTIC CORRELATION COEFFICIENT UNDER VARIANCE GAMMA ASSET PRICE DYNAMIC

This paper considers a class of Levy process namely the variance gamma (VG) process to offer a more realistic way to model the dynamics of the logarithm of stock prices. Then, we verify the uniqueness and existence of the solution to the stochastic differential equation of the model. We also examine the valuation of multi-asset American options under VG model when the correlation coefficient is governed by the modified Ornstein–Uhlenbeck process. Various simulation experiments are presented and the achieved results are tested empirically for option prices using S&P 500 data.

Stochastic correlation, regression probabilistic-statistical models of laminated composite structures with material-energy-saving polyurethane thermal insulation

After testing according to the developed state methods and procedures for polyurethane foam thermal insulation of laminated composite structures with metal facings, the issues of correlation, probabilistic-statistical regression models between the signs of elasticity in tension Eshear and signs of elasticity in shear Gshear were investigated. It is shown that the test results are random stochastic values. Their variability, depending on the type of tests and the parameter under study, is in the acceptable average values. The presence of a significant number of each of the characteristics, for example, Eshear - Gshift, predetermined the use of correlation tables to establish the fact of a relationship. Modules of elasticity under tension and shear were determined. The correlation coefficient between Eshear and Gshift is 0.659. With the help of a computer program, correlation tables were built and the calculation of the probabilistic-statistical interaction of characteristics with different reference points was made. When considering one-dimensional aggregates, the laws of distribution of positive values of the characteristics of foams can be adopted of various types. The connection between phenomena can be not only linear, but also non-linear. In this case, nonlinear correlation can be realized in the form of parabolic and other equations of a certain degree. The calculation of the interaction is carried out on the basis of second-order parabolic equations. Approximation in the form of second-order equations does not greatly improve the convergence, but the possibilities for extrapolating statistical models are reduced.

Modelling Joint Behaviour of Asset Prices Using Stochastic Correlation

Association or interdependence of two stock prices is analyzed, and selection criteria for a suitable model developed in the present paper. The association is generated by stochastic correlation, given by a stochastic differential equation (SDE), creating interdependent Wiener processes. These, in turn, drive the SDEs in the Heston model for stock prices. To choose from possible stochastic correlation models, two goodness-of-fit procedures are proposed based on the copula of Wiener increments. One uses the confidence domain for the centered Kendall function, and the other relies on strong and weak tail dependence. The constant correlation model and two different stochastic correlation models, given by Jacobi and hyperbolic tangent transformation of Ornstein-Uhlenbeck (HtanOU) processes, are compared by analyzing daily close prices for Apple and Microsoft stocks. The constant correlation, i.e., the Gaussian copula model, is unanimously rejected by the methods, but all other two are acceptable at a 95% confidence level. The analysis also reveals that even for Wiener processes, stochastic correlation can create tail dependence, unlike constant correlation, which results in multivariate normal distributions and hence zero tail dependence. Hence models with stochastic correlation are suitable to describe more dangerous situations in terms of correlation risk.

On the bond pricing partial differential equation in a convergence model of interest rates with stochastic correlation

Convergence models of interest rates are used to model a situation, where a country is going to enter a monetary union and its short rate is affected by the short rate in the monetary union. In addition, Wiener processes which model random shocks in the behaviour of the short rates can be correlated. In this paper we consider a stochastic correlation in a selected convergence model. A stochastic correlation has been already studied in different contexts in financial mathematics, therefore we distinguish differences which come from modelling interest rates by a convergence model. We provide meaningful properties which a correlation model should satisfy and afterwards we study the problem of solving the partial differential equation for the bond prices. We find its solution in a separable form, where the term coming from the stochastic correlation is given in its series expansion for a high value of the correlation.

Realized Semicovariances

We propose a decomposition of the realized covariance matrix into components based on the signs of the underlying high‐frequency returns, and we derive the asymptotic properties of the resulting realized semicovariance measures as the sampling interval goes to zero. The first‐order asymptotic results highlight how the same‐sign and mixed‐sign components load differently on economic information related to stochastic correlation and jumps. The second‐order asymptotic results reveal the structure underlying the same‐sign semicovariances, as manifested in the form of co‐drifting and dynamic “leverage” effects. In line with this anatomy, we use data on a large cross‐section of individual stocks to empirically document distinct dynamic dependencies in the different realized semicovariance components. We show that the accuracy of portfolio return variance forecasts may be significantly improved by exploiting the information in realized semicovariances.