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Discrete-parameter time-series models for financial data have received, and continue to receive, a great deal of attention in the literature. Stochastic volatility models, ARCH and GARCH models and their many generalizations, designed to account for the so-called stylized features of financial time series, have been under development and refinement now for some thirty years. At the same time there...
This paper surveys a class of Generalised Ornstein-Uhlenbeck (GOU) processes associated with Lévy processes, which has been recently much analysed in view of its applications in the financial modelling area, among others. We motivate the Lévy GOU by reviewing the framework already well understood for the “ordinary” (Gaussian) Ornstein-Uhlenbeck process, driven by Brownian motion; thus, defining it...
Lévy processes are developed in the more general framework of semimartingale theory with a focus on purely discontinuous processes. The fundamental exponential Lévy model is given, which allows us to describe stock prices or indices in a more realistic way than classical diffusion models. A number of standard examples including generalized hyperbolic and CGMY Lévy processes are considered in detail.
Gaussian ARMA processes with continuous time parameter, otherwise known as stationary continuous-time Gaussian processes with rational spectral density, have been of interest for many years. (See for example the papers of Doob (1944), Bartlett (1946), Phillips (1959), Durbin (1961), Dzhapararidze (1970,1971), Pham-Din-Tuan (1977) and the monograph of Arató (1982).) In the last twenty years there has...
We collect some continuous time GARCH models and report on how they approximate discrete time GARCH processes. Similarly, certain continuous time volatility models are viewed as approximations to discrete time volatility models.
This paper overviews maximum likelihood and Gaussian methods of estimating continuous time models used in finance. Since the exact likelihood can be constructed only in special cases, much attention has been devoted to the development of methods designed to approximate the likelihood. These approaches range from crude Euler-type approximations and higher order stochastic Taylor series expansions to...
A review is given of parametric estimation methods for discretely sampled multivariate diffusion processes. The main focus is on estimating functions and asymptotic results. Maximum likelihood estimation is briefly considered, but the emphasis is on computationally less demanding martingale estimating functions. Particular attention is given to explicit estimating functions. Results on both fixed...
Realized volatility is a nonparametric ex-post estimate of the return variation. The most obvious realized volatility measure is the sum of finely-sampled squared return realizations over a fixed time interval. In a frictionless market the estimate achieves consistency for the underlying quadratic return variation when returns are sampled at increasingly higher frequency. We begin with an account...
This chapter reviews our recent work on disentangling high frequency volatility estimators from market microstructure noise, based on maximum-likelihood in the parametric case and two (or more) scales realized volatility (TSRV) in the nonparametric case. We discuss the basic theory, its extensions and the practical implementation of the estimators.
This chapter reviews basic concepts of derivative pricing in financial mathematics.We distinguish market prices and individual values of a potential seller. We focus mainly on arbitrage theory. In addition, two hedgingbased valuation approaches are discussed. The first relies on quadratic hedging whereas the second involves a first-order approximation to utility indifference prices.
In this paper we give a short overview of some basic topics in interest rate theory, from the point of view of arbitrage free pricing. We cover short rate models, affine term structure models, inversion of the yield curve, the Musiela parameterization, and the potential approach to positive interest rates. The text is essentially self contained.
In this paper we present a review on the extremal behavior of stationary continuous-time processes with emphasis on generalized Ornstein-Uhlenbeck processes. We restrict our attention to heavy-tailed models like heavy-tailed Ornstein-Uhlenbeck processes or continuous-time GARCH processes. The survey includes the tail behavior of the stationary distribution, the tail behavior of the sample maximum...
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