scholarly journals Modeling and Forecasting S&P 500 Volatility: Long Memory, Structural Breaks and Nonlinearity

2004 ◽  
Author(s):  
Martin P.E. Martens ◽  
Michiel De Pooter ◽  
Dick J. C. van Dijk
Keyword(s):  
Long Memory ◽  
Structural Breaks ◽  
Modeling And Forecasting

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.

scholarly journals The long memory HEAVY process: modeling and forecasting financial volatility

2020 ◽  
Author(s):  
M. Karanasos ◽  
S. Yfanti ◽  
A. Christopoulos
Keyword(s):  
Long Memory ◽  
Structural Breaks ◽  
Global Financial Crisis ◽  
Fractional Integration ◽  
Capital Budgeting ◽  
Regression Equations ◽  
Forecasting Accuracy ◽  
Modeling And Forecasting ◽  
The Global Financial Crisis ◽  
Heavy System

AbstractThis paper studies the bivariate HEAVY system of volatility regression equations and its various extensions that are directly applicable to the day-to-day business treasury operations of trading in foreign exchange and commodities, investing in bond and stock markets, hedging out market risk, and capital budgeting. We enrich the HEAVY framework with powers, asymmetries, and long memory that improve its forecasting accuracy significantly. Other findings are as follows. First, hyperbolic memory fits the realized measure better, whereas fractional integration is more suitable for the powered returns. Second, the structural breaks applied to the bivariate system capture the time-varying behavior of the parameters, in particular during and after the global financial crisis of 2007/2008.

scholarly journals Modelling long memory volatility in the Bitcoin market: Evidence of persistence and structural breaks

2018 ◽  
Vol 24 (1) ◽  
pp. 412-426 ◽  
Author(s):  
Elie Bouri ◽  
Luis A. Gil-Alana ◽  
Rangan Gupta ◽  
David Roubaud
Keyword(s):  
Long Memory ◽  
Structural Breaks

Modeling and forecasting daily average PM10 concentrations by a seasonal long-memory model with volatility

2014 ◽  
Vol 51 ◽  
pp. 286-295 ◽  
Author(s):  
Valdério Anselmo Reisen ◽  
Alessandro José Queiroz Sarnaglia ◽  
Neyval Costa Reis ◽  
Céline Lévy-Leduc ◽  
Jane Méri Santos
Keyword(s):  
Long Memory ◽  
Memory Model ◽  
Daily Average ◽  
Modeling And Forecasting

scholarly journals Structural break or long memory: an empirical survey on daily rainfall data sets across Malaysia

2013 ◽  
Vol 17 (4) ◽  
pp. 1311-1318 ◽  
Author(s):  
F. Yusof ◽  
I. L. Kane ◽  
Z. Yusop
Keyword(s):  
Long Memory ◽  
Structural Breaks ◽  
Structural Break ◽  
Daily Rainfall ◽  
Least Square ◽  
Rainfall Data ◽  
Data Sets ◽  
Original Series ◽  
Empirical Survey ◽  
Daily Rainfall Data

Abstract. A short memory process that encounters occasional structural breaks in mean can show a slower rate of decay in the autocorrelation function and other properties of fractional integrated I (d) processes. In this paper we employed a procedure for estimating the fractional differencing parameter in semiparametric contexts proposed by Geweke and Porter-Hudak (1983) to analyse nine daily rainfall data sets across Malaysia. The results indicate that all the data sets exhibit long memory. Furthermore, an empirical fluctuation process using the ordinary least square (OLS)-based cumulative sum (CUSUM) test for the break date was applied. Break dates were detected in all data sets. The data sets were partitioned according to their respective break date, and a further test for long memory was applied for all subseries. Results show that all subseries follows the same pattern as the original series. The estimate of the fractional parameters d1 and d2 on the subseries obtained by splitting the original series at the break date confirms that there is a long memory in the data generating process (DGP). Therefore this evidence shows a true long memory not due to structural break.

scholarly journals Multifractal Analysis of Market Efficiency across Structural Breaks: Implications for the Adaptive Market Hypothesis

2020 ◽  
Vol 13 (10) ◽  
pp. 248
Author(s):  
Ashok Chanabasangouda Patil ◽  
Shailesh Rastogi
Keyword(s):  
Long Memory ◽  
Cross Correlation ◽  
Structural Breaks ◽  
Primary Objective ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Price Series ◽  
Cross Correlation Analysis ◽  
Large Fluctuations ◽  
Secondary Objective

The primary objective of this paper is to assess the behavior of long memory in price, volume, and price-volume cross-correlation series across structural breaks. The secondary objective is to find the appropriate structural breaks in the price series. The structural breaks in the series are identified using the Bai and Perron procedure, and in each segment, Multifractal Detrended Fluctuation Analysis (MFDFA) and Multifractal Detrended Cross-Correlation Analysis (MFDCCA) are conducted to capture the long memory in each series. The price series is persistent in small fluctuations and anti-persistent in large fluctuations across all the structural segments. This confirms that long memory in the series is not affected by the structural breaks. Both volume and price-volume cross-correlation are anti-persistent in all the structural segments. In other words, volume acts as a carrier of the information only in the non-volatile (normal) market. The varying Hurst exponent across the structural segments indicates the varying levels of persistence and signifies the volatile market. The findings of the study are useful for understanding the practical implications of the Adaptive Market Hypothesis (AMH).

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