Long Memory and Structural Breaks in Realized Volatility: An Irreversible Markov Switching Approach

The study provides a framework to model the unbiased extreme value volatility estimator (The AddRS estimator) in presence of structural breaks. We observe that the structural breaks in the volatility based on the AddRS estimator can partly explain its long memory property. We evaluate the forecasting performance of the proposed framework and compare the results with the corresponding results of the models from the GARCH family. The forecasts evaluation exercises consider the cases when future breaks are known as well as unknown. Our findings indicate that the proposed framework outperform the sophisticated GARCH class of models in forecasting realized volatility. Moreover, we devise a trading strategy based on the forecasts of the variance to highlight the economic significance of the proposed framework. We find that a risk averse investor can make substantial gain using the volatility forecasts based on the proposed frameworks in comparison to the GARCH family of models.

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Long memory versus structural breaks in modeling and forecasting realized volatility

Journal of International Money and Finance ◽

10.1016/j.jimonfin.2009.12.001 ◽

2010 ◽

Vol 29 (5) ◽

pp. 857-875 ◽

Cited By ~ 66

Author(s):

Kyongwook Choi ◽

Wei-Choun Yu ◽

Eric Zivot

Keyword(s):

Long Memory ◽

Structural Breaks ◽

Realized Volatility ◽

Modeling And Forecasting

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Are multifractal processes suited to forecasting electricity price volatility? Evidence from Australian intraday data

Studies in Nonlinear Dynamics & Econometrics ◽

10.1515/snde-2019-0009 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Mawuli Segnon ◽

Chi Keung Lau ◽

Bernd Wilfling ◽

Rangan Gupta

Keyword(s):

Long Memory ◽

Structural Breaks ◽

Moving Average ◽

Markov Switching ◽

Price Volatility ◽

Predictive Ability ◽

Electricity Price ◽

Smooth Transition ◽

Volatility Model ◽

Intraday Data

AbstractWe analyze Australian electricity price returns and find that they exhibit volatility clustering, long memory, structural breaks, and multifractality. Consequently, we let the return mean equation follow two alternative specifications, namely (i) a smooth transition autoregressive fractionally integrated moving average (STARFIMA) process, and (ii) a Markov-switching autoregressive fractionally integrated moving average (MSARFIMA) process. We specify volatility dynamics via a set of (i) short- and long-memory GARCH-type processes, (ii) Markov-switching (MS) GARCH-type processes, and (iii) a Markov-switching multifractal (MSM) process. Based on equal and superior predictive ability tests (using MSE and MAE loss functions), we compare the out-of-sample relative forecasting performance of the models. We find that the (multifractal) MSM volatility model keeps up with the conventional GARCH- and MSGARCH-type specifications. In particular, the MSM model outperforms the alternative specifications, when using the daily squared return as a proxy for latent volatility.

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Regime switching with structural breaks in output convergence

Studies in Nonlinear Dynamics & Econometrics ◽

10.1515/snde-2017-0043 ◽

2018 ◽

Vol 22 (3) ◽

Cited By ~ 1

Author(s):

Fuat C. Beylunioğlu ◽

Thanasis Stengos ◽

M. Ege Yazgan

Keyword(s):

Long Memory ◽

Regime Switching ◽

Structural Breaks ◽

Viterbi Algorithm ◽

Markov Switching ◽

Gap Series ◽

Statistical Computation ◽

The Em Algorithm ◽

Fractional Differencing ◽

Over Time

AbstractIn this paper, we examine empirically GDP per capita convergence using an approach that explicitly allows for regime switching in the long memory parameterdwithin the context of a Markov Switching (MS)–ARFIMA framework. As existing methods used in the estimation of standard MS models, such as the EM algorithm are no longer appropriate, we will make use of the Viterbi algorithm to estimate the long memory MS model used by Tsay and Härdle (Tsay, W.-J., and W. K. Härdle. 2009. “A Generalized Arfima Process with Markov-Switching Fractional Differencing Parameter.”Journal of Statistical Computation and Simulation79: 731–745.). We will classify the output gap series into two regimes, a highdand a lowdregime, where a highdclose to unity would imply persistence and lack of convergence. By examining the path ofdparameter over time which enables us to observe non-convergent behavior in more detail, we find that converging behavior is diminishing over time and divergence is the dominant force.

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