The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type models with artificial neural networks to benefit from the universal approximation properties to achieve improved forecasting accuracy. Therefore, the ...

We use high-frequency intra-day realized volatility to evaluate the relative forecasting performance of several models for the volatility of crude oil daily spot returns. Our objective is to evaluate the predictive ability of time-invariant and Markov switching GARCH models over different horizons. Using Carasco, Hu and Ploberger (2014) test for regime switching in the mean and variance of the ...

We develop a Markov-switching GARCH model (MS-GARCH) wherein the conditional mean and variance switch in time from one GARCH process to another. The switching is governed by a hidden Markov chain. We provide sufficient conditions for geometric ergodicity and existence of moments of the process. Because of path dependence, maximum likelihood estimation is not feasible. By enlarging the parameter...

We develop a Markov-switching GARCH model (MS-GARCH) wherein the conditional mean and variance switch in time from one GARCH process to another. The switching is governed by a hidden Markov chain. We provide sufficient conditions for geometric ergodicity and existence of moments of the process. Because of path dependence, maximum likelihood estimation is not feasible. By enlarging the parameter...

The volatility observed in securities markets has an important influence on the decision making processes of stock market stakeholders. In this study, volatilities BIST100 index which represents Borsa Istanbul was analyzed. For purpose, closing data for period 03.01.1988-20.04.2018 used study. analyzed by Markov regime switching GARCH (MS-GARCH) with three regimes, standard, high and low regime...

In the present paper, we test use of Markov-Switching (MS) models with time-fixed or Generalized Autoregressive Conditional Heteroskedasticity (GARCH) variances. This, to enhance performance a U.S. dollar-based portfolio that invest in S&P 500 (SP500) stock index, 3-month Treasury-bill (T-BILL) 1-month volatility index (VIX) futures. For investment algorithm, propose two and three-regime, G...