In this article, we develop one- and two-component Markov regime-switching conditional volatility models based on the intraday range evaluate their performance in forecasting daily of S&P 500 Index. We compare with that several well-established return- range-based models, namely EWMA, GARCH, FIGARCH GARCH model, hybrid EWMA CARR model. in-sample goodness fit out-of-sample forecast using a compr...

In this paper, a general autoregressive model with Markov switching is considered, where the autoregression may be of an infinite order. The consistency of the maximum likelihood estimators for this model is obtained under regular assumptions. Examples of finite and infinite order Markov switching AR models are discussed. The simulation study with these examples illustrates the consistency and ...

T he objective of this study is using the Markov Switching Vector Autoregressive method and regime dependent impulse response functions to measure the pass-through of world food prices to consumer price index in Iran from 1990 to 2013. With respect to information criteria and the log-likelihood ratio statistic, MSIA(2)-VAR(1) model has a better fit to data than other models. The magn...

This paper proposes a discrete-state stochastic volatility model with duration-dependent mixing. The latter is directed by a high-order Markov chain with a sparse transition matrix. Ž . As in the standard first-order Markov switching MS model, this structure can capture turning points and shifts in volatility, due for example, to policy changes or news events. Ž . However, the duration-dependen...

A trivariate vector autoregression time series process, based on a present-value land price model, is used to decompose Iowa farmland prices into fundamental and non-fundamental components. A recent study, by Falk and Lee (1998), found that non-fundamental shocks are an important source of volatility in farmland prices and it was interpreted that these price movements were due to fads not specu...