scholarly journals Long Memory and Nonlinearities in Realized Volatility: A Markov Switching Approach

2010 ◽  
Author(s):  
Silvano Bordignon ◽  
Davide Raggi
Keyword(s):  
Long Memory ◽  
Markov Switching ◽  
Realized Volatility

MULTIFRACTALITY AND LONG-RANGE DEPENDENCE OF ASSET RETURNS: THE SCALING BEHAVIOR OF THE MARKOV-SWITCHING MULTIFRACTAL MODEL WITH LOGNORMAL VOLATILITY COMPONENTS

2008 ◽  
Vol 11 (05) ◽  
pp. 669-684 ◽  
Author(s):  
RUIPENG LIU ◽  
T. DI MATTEO ◽  
THOMAS LUX
Keyword(s):  
Exchange Rates ◽  
Foreign Exchange ◽  
Empirical Data ◽  
Long Memory ◽  
Markov Switching ◽  
Asset Returns ◽  
Financial Data ◽  
Long Range Dependence ◽  
Foreign Exchange Rates ◽  
Good Agreement

In this paper, we consider daily financial data from various sources (stock market indices, foreign exchange rates and bonds) and analyze their multiscaling properties by estimating the parameters of a Markov-switching multifractal (MSM) model with Lognormal volatility components. In order to see how well estimated models capture the temporal dependency of the empirical data, we estimate and compare (generalized) Hurst exponents for both empirical data and simulated MSM models. In general, the Lognormal MSM models generate "apparent" long memory in good agreement with empirical scaling provided that one uses sufficiently many volatility components. In comparison with a Binomial MSM specification [11], results are almost identical. This suggests that a parsimonious discrete specification is flexible enough and the gain from adopting the continuous Lognormal distribution is very limited.

Structural breaks in unbiased volatility estimator: Modeling and forecasting

2017 ◽  
Vol 11 (1) ◽  
pp. 27-50 ◽  
Author(s):  
Dilip Kumar
Keyword(s):  
Long Memory ◽  
Structural Breaks ◽  
Realized Volatility ◽  
Extreme Value ◽  
Trading Strategy ◽  
Risk Averse ◽  
Memory Property ◽  
Economic Significance ◽  
Forecasting Performance ◽  
Modeling And Forecasting

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.

Regimes and long memory in realized volatility

2013 ◽  
Vol 17 (5) ◽  
Author(s):  
Elena Goldman ◽  
Jouahn Nam ◽  
Hiroki Tsurumi ◽  
Jun Wang
Keyword(s):  
Long Memory ◽  
Realized Volatility

scholarly journals Can Markov switching model generate long memory?

2014 ◽  
Vol 124 (1) ◽  
pp. 117-121 ◽  
Author(s):  
Changryong Baek ◽  
Natércia Fortuna ◽  
Vladas Pipiras
Keyword(s):  
Long Memory ◽  
Markov Switching ◽  
Markov Switching Model ◽  
Switching Model

scholarly journals Refined Inference on Long Memory in Realized Volatility

2008 ◽  
Vol 27 (1-3) ◽  
pp. 254-267 ◽  
Author(s):  
Offer Lieberman ◽  
Peter C. B. Phillips
Keyword(s):  
Long Memory ◽  
Realized Volatility

scholarly journals Modelando e Prevendo a Volatilidade dos Retornos de Ativos Brasileiros: uma Abordagem da Variância Realizada

2006 ◽  
Vol 4 (1) ◽  
pp. 55
Author(s):  
Marcelo C. Carvalho ◽  
Marco Aurélio S. Freire ◽  
Marcelo Cunha Medeiros ◽  
Leonardo R. Souza
Keyword(s):  
Long Memory ◽  
High Frequency ◽  
Value At Risk ◽  
Realized Volatility ◽  
High Frequency Data ◽  
Garch Models ◽  
Frequency Data ◽  
Normality Assumption ◽  
Out Of Sample ◽  
Management Area

The goal of this paper is twofold. First, using five of the most actively traded stocks in the Brazilian financial market, this paper shows that the normality assumption commonly used in the risk management area to describe the distributions of returns standardized by volatilities is not compatible with volatilities estimated by EWMA or GARCH models. In sharp contrast, when the information contained in high frequency data is used to construct the realized volatility measures, we attain the normality of the standardized returns, giving promise of improvements in Value-at-Risk statistics. We also describe the distributions of volatilities of the Brazilian stocks, showing that they are nearly lognormal. Second, we estimate a simple model of the log of realized volatilities that differs from the ones in other studies. The main difference is that we do not find evidence of long memory. The estimated model is compared with commonly used alternatives in out-of-sample forecasting experiment.

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