MS-GARCH Models Research Articles

Volatility estimation for cryptocurrencies using Markov-switching GARCH models

In the 21st century, digital currencies have become a disruptive technology that is shaking up both financial markets and academic environment. Investors, politicians, companies, and academics are attempting to improve their understanding of these currencies for future investment possibilities and technological applications. This study aims to evaluate changes in different volatility states of eight digital currencies (BTC, ETH, LTC, XRP, XMR, NEM, LISK, and STEEM) that showed the highest liquidity and market capitalisation from 2013 to 2017. The methodology involved the MSGARCH model, using SGARCH, EGARCH, GJRGARCH, and TGARCH models. Our study demonstrated that two volatility regimes, that is, one with a larger volatility and another with a smaller one, clearly exist for all the analysed cryptocurrencies. What differs between the currencies is the probability of a second regime occurring. Moreover, we concluded that for both the first and second state, the asymmetry coefficient (gamma) is positive for all currencies.

Detection method of financial crisis in Indonesia using MSGARCH models based on banking condition indicators

Financial crisis has hit Indonesia for several times resulting the needs for an early detection system to minimize the impact. One of many methods that can be used to detect the crisis is to model the crisis indicators using combination of volatility and Markov switching models [5]. There are some indicators that can be used to detect financial crisis. Three of them are the difference between interest rate on deposit and lending, the real interest rate on deposit, and the difference between real BI rate and real Fed rate which can be referred as banking condition indicators. Volatility model used to overcome the conditional variance that change over time. Combination of volatility and Markov switching models used to detect condition change on the data. The smoothed probability from the combined models can be used to detect the crisis. This research resulted that the best combined volatility and Markov switching models for the three indicators are MS-GARCH(3,1,1) models with three states assumption. Crises in mid of 1997 until 1998 has successfully detected with a certain range of smoothed probability value for the three indicators.

Improved Forecast Ability of Oil Market Volatility Based on combined Markov Switching and GARCH-class Model

This paper estimates and forecasts West Texas Intermediate (WTI) crude oil market volatility with GARCH, EGARCH models. We find that, in terms of Root Mean Squared Error (RMSE) criteria, EGARCH model outperformances GARCH model in both short term and long term forecast length. Then, we apply Markov Switching model on oil volatility term and find that we can improve forecast accuracy of oil market volatility further with MSGARCH model, when compared with normal GARCH and normal EGARCH model. Besides, empirical results confirm that two regimes consist of regime 1 with low volatility and regime 2 with high volatility exist in WTI crude oil market. Also, the smoothed probabilities indicate that before 2004, WTI crude oil market stays in a low volatility regime most of time, while after 2004, WTI crude oil market switches to high volatility regime and stays in it until present most of time.

Modeling Volatility in Emerging Markets: Comparison between Symmetric Garch Model and Ms-Garch Model

Modeling Volatility in Emerging Markets: Comparison between Symmetric Garch Model and Ms-Garch Model

Empirical Analysis on the USD/all Exchange Rate Volatility in Albanian Market: Preliminary Results

This paper aims to forecast the USD/ALL exchange rate volatility in short term period in Albanian market, being that the American dollar is considered a safe currency independently to the political context in the rest of the world. Furthermore, USD is the second foreign currency after Euro (according to financial and commercial transactions) and it is characterized by a peculiar probabilistic volatility distribution. In particular, USD volatility represents a continuous concern for economic agents exposed to the exchange risk. It follows that the measurement of the USD/ALL exchange rate volatility may help in the assessment and maintenance of capital needed for coverage purposes. The common financial time series dynamic models such as ARMA (1;1), ARCH (1) and GARCH (1;1) can be used to estimate the USD/ALL exchange rate volatility in short term period. Our results suggest that, in the presence of political factors as well as external shocks derived from country’s main trade partners, the best way to estimate and forecast the USD/ALL exchange rate volatility in the short term is the use of the MS-GARCH model.

Efficient Gibbs sampling for Markov switching GARCH models

Efficient simulation techniques for Bayesian inference on Markov-switching (MS) GARCH models are developed. Different multi-move sampling techniques for Markov switching state space models are discussed with particular attention to MS-GARCH models. The multi-move sampling strategy is based on the Forward Filtering Backward Sampling (FFBS) approach applied to auxiliary MS-GARCH models. A unified framework for MS-GARCH approximation is developed and this not only encompasses the considered specifications, but provides an avenue to generate new variants of MS-GARCH auxiliary models. The use of multi-point samplers, such as the multiple-try Metropolis and the multiple-trial metropolized independent sampler, in combination with FFBS, is considered in order to reduce the correlation between successive iterates and to avoid getting trapped by local modes of the target distribution. Antithetic sampling within the FFBS is also suggested to further improve the sampler’s efficiency. The simulation study indicates that the multi-point and multi-move strategies can be more efficient than other MCMC schemes, especially when the MS-GARCH is not strongly persistent. Finally, an empirical application to financial data shows the efficiency and effectiveness of the proposed estimation procedure.

Markov Switching GARCH Models for Bayesian Hedging on Energy Futures Markets

A new Bayesian multi-chain Markov Switching GARCH model for dynamic hedging in energy futures markets is developed by constructing a system of simultaneous equations for the return dynamics on the hedged portfolio and futures. More specifically, both the mean and variance of the hedged portfolio are assumed to be governed by two unobserved discrete state processes, while the futures dynamics is driven by a univariate hidden state process. The noise in both processes are characterized by a MS-GARCH model. This formulation has two main practical and conceptual advantages. First, the different states of the discrete processes can be identified as different volatility regimes. Secondly, the parameters can be easily interpreted as different hedging components. Our formulation also provides an avenue to analyze the contribution of the volatility dynamics and state probabilities to the optimal hedge ratio at each point in time. Moreover, the combination of the expected utility framework with regime-switching models allows the definition of a robust minimum variance hedging strategy to also account for parameter uncertainty. Evidence of changes in the optimal hedging strategies before and after the financial crisis is found when the proposed robust hedging strategy is applied to crude oil spot and futures markets.

Modeling Markov switching ARMA-GARCH neural networks models and an application to forecasting stock returns.

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 proposed Markov-switching MS-ARMA-FIGARCH, APGARCH, and FIAPGARCH processes are further augmented with MLP, Recurrent NN, and Hybrid NN type neural networks. The MS-ARMA-GARCH family and MS-ARMA-GARCH-NN family are utilized for modeling the daily stock returns in an emerging market, the Istanbul Stock Index (ISE100). Forecast accuracy is evaluated in terms of MAE, MSE, and RMSE error criteria and Diebold-Mariano equal forecast accuracy tests. The results suggest that the fractionally integrated and asymmetric power counterparts of Gray's MS-GARCH model provided promising results, while the best results are obtained for their neural network based counterparts. Further, among the models analyzed, the models based on the Hybrid-MLP and Recurrent-NN, the MS-ARMA-FIAPGARCH-HybridMLP, and MS-ARMA-FIAPGARCH-RNN provided the best forecast performances over the baseline single regime GARCH models and further, over the Gray's MS-GARCH model. Therefore, the models are promising for various economic applications.

Efficient Gibbs Sampling for Markov Switching GARCH Models

We develop efficient simulation techniques for Bayesian inference on switching GARCH models. Our contribution to existing literature is manifold. First, we discuss different multi-move sampling techniques for Markov Switching (MS) state space models with particular attention to MS-GARCH models. Our multi-move sampling strategy is based on the Forward Filtering Backward Sampling (FFBS) applied to an approximation of MS-GARCH. Another important contribution is the use of multi-point samplers, such as the Multiple-Try Metropolis (MTM) and the Multiple Trial Metropolize Independent Sampler, in combination with FFBS for the MS-GARCH process. In this sense we extend to the MS state space models the work of So (2006) on efficient MTM sampler for continuous state space models. Finally, we suggest to further improve the sampler efficiency by introducing the antithetic sampling of Craiu and Meng (2005) and Craiu and Lemieux (2007) within the FFBS. Our simulation experiments on MS-GARCH model show that our multi-point and multi-move strategies allow the sampler to gain efficiency when compared with single-move Gibbs sampling.

Optimal Test for Markov Switching GARCH Models

Empirically, the sum of GARCH parameter estimates is found to be close to unity, suggesting that the conditional volatility of most stock return data are likely to follow an integrated GARCH (IGARCH) process. However, such an extremely high persistence in unconditional variance may be overstated because of neglected structural breaks or parameter changes. As a result it is important to distinguish between these two processes, one being a globally stationary process and the other being a nonstationary IGARCH process. Though there are a number of studies modelling asymmetry leverage effects and advancing a battery of specification tests, studies that directly propose specification tests against Markov switching (MS) GARCH models are almost nonexistent. This paper develops such tests against MS-GARCH processes, which is shown to be asymptotically equivalent to the LR test. Furthermore, we consider the case in which the conditional variance follows an IGARCH process under the null whilst it is globally stationary under the alternative. Monte Carlo studies show that our proposed tests have a good finite sample performance. In an application to the weekly stock return data for five East Asian emerging markets, we find strong evidence in favor of MS-GARCH models.