Range-based Volatility Forecasting: A Multiplicative Component Conditional Autoregressive Range Model

IF 0.3 4区经济学 Q4 BUSINESS, FINANCE

Journal of Risk Pub Date : 2020-06-04 DOI:10.21314/jor.2020.433

Haibin Xie

引用次数: 2

Abstract

To capture the "long-memory" effect in volatility, a multiplicative component conditional autoregressive range (MCCARR) model is proposed. We show theoretically that the MCCARR model can capture the long-memory effect well. An empirical study is performed on the Standard & Poor's 500 index, and the results show that the MCCARR model outperforms both conditional autoregressive range and heterogeneous autoregressive models for in-sample and out-of-sample volatility forecasting.

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基于区间的波动率预测:一个乘法分量条件自回归区间模型

为了捕捉波动性中的“长记忆”效应，提出了一个乘法分量条件自回归范围（MCCARR）模型。我们从理论上证明了MCCARR模型能够很好地捕捉长记忆效应。对标准&；Poor’s 500指数，结果表明，MCCARR模型在样本内和样本外波动率预测方面均优于条件自回归区间和异质自回归模型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Risk BUSINESS, FINANCE-

CiteScore

1.00

自引率

14.30%

发文量

期刊介绍： This international peer-reviewed journal publishes a broad range of original research papers which aim to further develop understanding of financial risk management. As the only publication devoted exclusively to theoretical and empirical studies in financial risk management, The Journal of Risk promotes far-reaching research on the latest innovations in this field, with particular focus on the measurement, management and analysis of financial risk. The Journal of Risk is particularly interested in papers on the following topics: Risk management regulations and their implications, Risk capital allocation and risk budgeting, Efficient evaluation of risk measures under increasingly complex and realistic model assumptions, Impact of risk measurement on portfolio allocation, Theoretical development of alternative risk measures, Hedging (linear and non-linear) under alternative risk measures, Financial market model risk, Estimation of volatility and unanticipated jumps, Capital allocation.