Long memory versus structural breaks in modeling and forecasting realized volatility

2010 ◽  
Vol 29 (5) ◽  
pp. 857-875 ◽  
Author(s):  
Kyongwook Choi ◽  
Wei-Choun Yu ◽  
Eric Zivot
Keyword(s):  
Long Memory ◽  
Structural Breaks ◽  
Realized Volatility ◽  
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 On the predictability of high and low prices: The case of Bitcoin

2019 ◽  
Vol 17 (3) ◽  
pp. 66
Author(s):  
Leandro Dos Santos Maciel ◽  
Rosangela Ballini
Keyword(s):  
Long Memory ◽  
Realized Volatility ◽  
Vector Autoregressive ◽  
Market Capitalization ◽  
Modeling And Forecasting ◽  
High Volatility ◽  
Flexible Framework ◽  
Financial Prices ◽  
Cointegration Relationship

<p>Bitcoin has attracted the attention of investors lately due to its significant market capitalization and high volatility. This work considers the modeling and forecasting of daily high and low Bitcoin prices using a fractionally cointegrated vector autoregressive (FCVAR) model. As a flexible  framework, FCVAR is able to account for two fundamental patterns of high and low financial prices: their cointegrating relationship and the long memory of their difference (i.e., the range), which is a measure of realized volatility. The analysis comprises the period from January 2012 to February 2018. Empirical findings indicate a significant cointegration relationship between daily high and low Bitcoin prices, which are integrated on an order close to the unity, and the evidence of long memory for the range. Results also indicate that high and low Bitcoin prices are predictable, and the fractionally cointegrated approach appears as a potential forecasting tool for<br />cryptocurrencies market practitioners.</p>

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
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