A general estimation approach combining the attractive features of method of moments with the efficiency of ML is proposed. The moment conditions are computed via the characteristic function. The two major difficulties with the implementation is that one needs to use an infinite set of moment...

We present a general class of nonlinear time series Markov regime-switching models for seasonal data which may exhibit periodic features in the hidden Markov process as well as in the laws of motion in each of the regimes. This class of models allows for nontrivial dependencies between seasonal,...

Asset returns exhibit clustering of volatility throughout the year. This paper proposes a class of models featuring periodicity in conditional heteroskedasticity. The periodic structures in GARCH models share many properties with periodic ARMA processes studied by Gladyshev (1961), Tiao and...

To enhance our understanding of emerging markets we study a data set containing all the transaction records over a long span. The market, which was included in 1996 in the International Finance Corporation (IFC) data base roughly three years after important market reforms, operated under a dual...

Subordinated stochastic processes, also called time deformed stochastic processes, have been proposed in a variety of contexts to describe asset price behavior. They are used when the movement of prices is tied to the number of market transactions, trading volume or the more illusive concept of...

This paper, prepared for the Handbook of Statistics, vol.14, Statistical Methods in Finance, surveys the subject of Stochastic Volatility. The following subjects are covered : volatility in financial markets (instantaneous volatility of asset returns, implied volatilities in option prices and...

In this paper, we study stochastic volatility models with time deformation. Such processes relate to early work by Mandelbrot and Taylor (1967), Clark (1973), Tauchen and Pitts (1983), among others. In our setup, the latent process of stochastic volatility evolves in a operational time which...

We consider various MIDAS (Mixed Data Sampling) regression models to predict volatility. The models differ in the specification of regressors (squared returns, absolute returns, realized volatility, realized power, and return ranges), in the use of daily or intra-daily (5-minute) data, and in...

Globalization of trading in foreign exchange markets is a principal source of the daily and weekly seasonability in market volatility. One way to model such phenomena is to adopt a framework where market volatility is tied to the intensity of (world) trading through a subordinated stochastic...

We examine several simulation-based estimators for the parameters of a moving average process, including the one initially proposed by Gourieroux, Monfort and Renault (1993) as well as several extensions based on Gallant and Tauchen (1994). The estimators are also compared and related to...