A descriptive method to evaluate the number of regimes in a switching autoregressive model

International audienceThis paper proposes a descriptive method for an open problem in time-series analysis : determining the number of regimes in a switching autoregressive model. We will translate this problem into a classification one and define a criterion for hierarchically clustering different model fittings. Finally, the method will be tested on simulated examples and real-life data

Modeling the trading process on financial markets using the MSACD model

We propose a new framework for modeling time dependence in duration processes. The ACD approach introduced by Engle and Russell (1998) will be extended so that the conditional expectation of the durations depends on an unobservable stochastic process which is modeled via a Markov chain. The Markov switching ACD model (MSACD) is a flexible tool for description of financial duration processes. The introduction of a latent information regime variable can be justified in the light of recent market microstructure theories. In an empirical application we show that the MSACD approach is able to capture specific characteristics of inter trade durations while alternative ACD models fail. JEL classification: C41, C22, C25, C51, G1

Comparison of MSACD models

We propose a new framework for modelling time dependence in duration processes on financial markets. The well known autoregressive conditional duration (ACD) approach introduced by Engle and Russell (1998) will be extended in a way that allows the conditional expectation of the duration process to depend on an unobservable stochastic process which is modelled via a Markov chain. The Markov switching ACD model (MSACD) is a very flexible tool for description and forecasting of financial duration processes. In addition, the introduction of an unobservable, discrete valued regime variable can be justified in the light of recent market microstructure theories. In an empirical application we show that the MSACD approach is able to capture several specific characteristics of inter trade durations while alternative ACD models fail. JEL classification: C22, C25, C41, G1

Forecasting Value-at-Risk Using the Markov-Switching ARCH Model

This paper analyzes the application of the Markov-switching ARCH model (Hamilton and Susmel, 1994) in improving value-at-risk (VaR) forecast. By considering a mixture of normal distributions with varying variances over different time and regimes, we find that the “spurious high persistence†found in the GARCH model is adjusted. Under relative performance and hypothesis-testing evaluations, the VaR forecasts derived from the Markov-switching ARCH model are preferred to alternative parametric and nonparametric VaR models that only consider time-varying volatility. JEL classification: C22, C52, G28. Keywords: Value-at-Risk, Switching-regime ARCH models.Value-at-Risk, Switching-regime ARCH models

A non-linear stochastic asset model for actuarial use

This paper reviews the stochastic asset model described in Wilkie (1995) and previous work on refining this model. The paper then considers the application of non-linear modelling to investment series, considering both ARCH techniques and threshold modelling. The paper suggests a threshold autoregressive (TAR) system as a useful progression from the Wilkie (1995) model. The authors are making available (by email, on request) a collection of spreadsheets, which they have used to simulate the stochastic asset models which are considered in this paper

A multiple regime smooth transition heterogeneous autoregressive model for long memory and asymmetries

In this paper we propose a flexible model to capture nonlinearities and long-range dependence in time series dynamics. The new model is a multiple regime smooth transition extension of the Heterogenous Autoregressive (HAR) model, which is specifically designed to model the behavior of the volatility inherent in financial time series. The model is able to describe simultaneously long memory, as well as sign and size asymmetries. A sequence of tests is developed to determine the number of regimes, and an estimation and testing procedure is presented. Monte Carlo simulations evaluate the finite-sample properties of the proposed tests and estimation procedures. We apply the model to several Dow Jones Industrial Average index stocks using transaction level data from the Trades and Quotes database that covers ten years of data. We find strong support for long memory and both sign and size asymmetries. Furthermore, the new model, when combined with the linear HAR model, is viable and flexible for purposes of forecasting volatility.Realized volatility, smooth transition, heterogeneous autoregression, financial econometrics,leverage, sign and size asymmetries, forecasting, risk management, model combination.