Modeling and Forecasting Realized Volatilities of Korean Financial Assets Featuring Long Memory and Asymmetry

2014 ◽  
Vol 43 (1) ◽  
pp. 31-58 ◽  
Author(s):  
Soyoung Park ◽  
Dong W. Shin
Keyword(s):  
Long Memory ◽  
Financial Assets ◽  
Modeling And Forecasting

Long memory or structural break? Empirical evidences from index volatility in stock market

2019 ◽  
Vol 9 (3) ◽  
pp. 324-337 ◽  
Author(s):  
Yi Luo ◽  
Yirong Huang
Keyword(s):  
Long Memory ◽  
Structural Break ◽  
Reference Value ◽  
Investment Strategy ◽  
Stock Index ◽  
Financial Assets ◽  
Policy Makers ◽  
Content Type ◽  
Market Participants ◽  
Modeling And Forecasting

Purpose The purpose of this paper is to explore whether stock index volatility series exhibit real long memory. Design/methodology/approach The authors employ sequential procedure to test structural break in volatility series, and use DFA and 2ELW to estimate long memory parameter for the whole samples and subsamples, and further apply adaptive FIGARCH (AFIGARCH) to describe long memory and structural break. Findings The empirical results show that stock index volatility series are characterized by long memory and structural break, and therefore it is appropriate to use AFIGARCH to model stock index volatility process. Originality/value This study empirically investigates the properties of long memory and structural break in stock index volatility series. The conclusion has a certain reference value for understanding the properties of long memory and structural break in volatility series for academic researchers, market participants and policy makers, and for modeling and forecasting future volatility, testing market efficiency, pricing financial assets, constructing quantitative investment strategy and measuring market risk.

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

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 Long-memory modeling and forecasting: evidence from the U.S. historical series of inflation

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Heni Boubaker ◽  
Giorgio Canarella ◽  
Rangan Gupta ◽  
Stephen M. Miller
Keyword(s):  
Long Memory ◽  
Inflation Targeting ◽  
Inflation Rate ◽  
Inflation Persistence ◽  
Long Memory Process ◽  
Historical Series ◽  
Forecasting Performance ◽  
Modeling And Forecasting ◽  
Out Of Sample ◽  
Floating Exchange Rates

AbstractWe report the results of applying several long-memory models to the historical monthly U.S. inflation rate series and analyze their out-of-sample forecasting performance over different horizons. We find that the time-varying approach to estimating inflation persistence outperforms the models that assume a constant long-memory process. In addition, we examine the link between inflation persistence and exchange rate regimes. Our results support the hypothesis that floating exchange rates associate with increased inflation persistence. This finding, however, is less pronounced during the era of the Great Moderation and the Federal Reserve System’s commitment to inflation targeting.

Yield Curve Modeling and Forecasting

2013 ◽  
Author(s):  
Francis X. Diebold ◽  
Glenn D. Rudebusch
Keyword(s):  
Financial Risk ◽  
Yield Curve ◽  
Efficient Markets ◽  
Financial Assets ◽  
Unified Approach ◽  
Modeling And Forecasting ◽  
Curve Model ◽  
Curve Modeling ◽  
Dynamic Yield ◽  
Dynamic Version

Understanding the dynamic evolution of the yield curve is critical to many financial tasks, including pricing financial assets and their derivatives, managing financial risk, allocating portfolios, structuring fiscal debt, conducting monetary policy, and valuing capital goods. Unfortunately, most yield curve models tend to be theoretically rigorous but empirically disappointing, or empirically successful but theoretically lacking. This book proposes two extensions of the classic yield curve model of Nelson and Siegel that are both theoretically rigorous and empirically successful. The first extension is the dynamic Nelson–Siegel model (DNS), while the second takes this dynamic version and makes it arbitrage-free (AFNS). The book shows how these two models are just slightly different implementations of a single unified approach to dynamic yield curve modeling and forecasting. They emphasize both descriptive and efficient-markets aspects, they pay special attention to the links between the yield curve and macroeconomic fundamentals, and they show why DNS and AFNS are likely to remain of lasting appeal even as alternative arbitrage-free models are developed. Based on the Econometric and Tinbergen Institutes Lectures, the book contains essential tools with enhanced utility for academics, central banks, governments, and industry.

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