Which is the better forecasting model? A comparison between HAR-RV and multifractality volatility

Abstract In this paper, by taking the 5-min high frequency data of the Shanghai Composite Index as example, we compare the forecasting performance of HAR-RV and Multifractal volatility, Realized volatility, Realized Bipower Variation and their corresponding short memory model with rolling windows forecasting method and the Model Confidence Set which is proved superior to SPA test. The empirical results show that, for six loss functions, HAR-RV outperforms other models. Moreover, to make the conclusions more precise and robust, we use the MCS test to compare the performance of their logarithms form models, and find that the HAR-log(RV) has a better performance in predicting future volatility. Furthermore, by comparing the two models of HAR-RV and HAR-log(RV), we conclude that, in terms of performance forecasting, the HAR-log(RV) model is the best model among models we have discussed in this paper.

[1]  R. Engle Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[2]  B. M. Tabak,et al.  Ranking efficiency for emerging markets , 2004 .

[3]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[4]  S. Koopman,et al.  Forecasting Daily Variability of the S&P 100 Stock Index Using Historical, Realised and Implied Volatility Measurements , 2004 .

[5]  Jan Korbel,et al.  Modeling Financial Time Series , 2013 .

[6]  P. Hansen A Test for Superior Predictive Ability , 2005 .

[7]  K. West,et al.  Asymptotic Inference about Predictive Ability , 1996 .

[8]  H. Stanley,et al.  Multifractal Detrended Fluctuation Analysis of Nonstationary Time Series , 2002, physics/0202070.

[9]  Francis X. Diebold,et al.  Modeling and Forecasting Realized Volatility , 2001 .

[10]  F. Diebold,et al.  Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility , 2005, The Review of Economics and Statistics.

[11]  A. Lo,et al.  Data-Snooping Biases in Tests of Financial Asset Pricing Models , 1989 .

[12]  Yiu Kuen Tse,et al.  The conditional heteroscedasticity of the yen-dollar exchange rate , 1998 .

[13]  Benjamin Miranda Tabak,et al.  Ranking efficiency for emerging equity markets II , 2005 .

[14]  B. Mandelbrot A Multifractal Walk down Wall Street , 1999 .

[15]  Jose A. Lopez Evaluating the Predictive Accuracy of Volatility Models , 2001 .

[16]  F. Diebold,et al.  Comparing Predictive Accuracy , 1994, Business Cycles.

[17]  Yu Wei,et al.  Measuring daily Value-at-Risk of SSEC index: A new approach based on multifractal analysis and extreme value theory , 2013 .

[18]  Wei-Xing Zhou,et al.  The components of empirical multifractality in financial returns , 2009, 0908.1089.

[19]  N. Shephard,et al.  Power and bipower variation with stochastic volatility and jumps , 2003 .

[20]  Yu Wei,et al.  Analysis of the efficiency and multifractality of gold markets based on multifractal detrended fluctuation analysis , 2011 .

[21]  Zhi-Qiang Jiang,et al.  Multifractality in stock indexes: Fact or Fiction? , 2007, 0706.2140.

[22]  Xia Sun,et al.  Predictability of multifractal analysis of Hang Seng stock index in Hong Kong , 2001 .

[23]  Benoit B. Mandelbrot,et al.  Fractals and Scaling in Finance , 1997 .

[24]  Chongfeng Wu,et al.  Forecasting volatility in Shanghai and Shenzhen markets based on multifractal analysis , 2011 .

[25]  James Davidson,et al.  Moment and Memory Properties of Linear Conditional Heteroscedasticity Models, and a New Model , 2004 .

[26]  H. White,et al.  A Reality Check for Data Snooping , 2000 .

[27]  Tomaso Aste,et al.  Scaling behaviors in differently developed markets , 2003 .

[28]  P. Hansen,et al.  A Forecast Comparison of Volatility Models: Does Anything Beat a Garch(1,1)? , 2004 .

[29]  Wei-Xing Zhou,et al.  Finite-size effect and the components of multifractality in financial volatility , 2009, 0912.4782.

[30]  Feng Ma,et al.  Multifractal detrended cross-correlation analysis between the Chinese stock market and surrounding stock markets , 2013 .

[31]  Richard Schmalensee,et al.  Advertising and aggregate consumption: an analysis of causality , 1980 .

[32]  C. Granger,et al.  A long memory property of stock market returns and a new model , 1993 .

[33]  Peter Reinhard Hansen,et al.  The Model Confidence Set , 2010 .

[34]  M. Dacorogna,et al.  Volatilities of different time resolutions — Analyzing the dynamics of market components , 1997 .

[35]  N. Shephard,et al.  Econometric analysis of realized volatility and its use in estimating stochastic volatility models , 2002 .

[36]  Yu Wei,et al.  Forecasting volatility of fuel oil futures in China: GARCH-type, SV or realized volatility models? , 2012 .

[37]  Yu Wei,et al.  Cross-correlations between Chinese A-share and B-share markets , 2010 .

[38]  Fulvio Corsi,et al.  A Simple Approximate Long-Memory Model of Realized Volatility , 2008 .

[39]  Alejandra Figliola,et al.  A multifractal approach for stock market inefficiency , 2008 .

[40]  M. Medeiros,et al.  A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries , 2008 .

[41]  F. Diebold,et al.  The distribution of realized stock return volatility , 2001 .

[42]  N. Shephard,et al.  Econometrics of Testing for Jumps in Financial Economics Using Bipower Variation , 2005 .

[43]  F. Douglas Foster,et al.  Assessing goodness-of-fit of asset pricing models: The distribution of the maximal R2 , 1997 .

[44]  Xia Sun,et al.  Multifractal analysis of Hang Seng index in Hong Kong stock market , 2001 .

[45]  Yu Wei,et al.  Forecasting volatility of SSEC in Chinese stock market using multifractal analysis , 2008 .

[46]  Ladislav Kristoufek,et al.  Measuring capital market efficiency: Global and local correlations structure , 2012, 1208.1298.

[47]  Daniel B. Nelson CONDITIONAL HETEROSKEDASTICITY IN ASSET RETURNS: A NEW APPROACH , 1991 .

[48]  N. Shephard,et al.  Non‐Gaussian Ornstein–Uhlenbeck‐based models and some of their uses in financial economics , 2001 .

[49]  Zhi-Qiang Jiang,et al.  Multifractal analysis of Chinese stock volatilities based on the partition function approach , 2008, 0801.1710.

[50]  T. Bollerslev,et al.  ANSWERING THE SKEPTICS: YES, STANDARD VOLATILITY MODELS DO PROVIDE ACCURATE FORECASTS* , 1998 .

[51]  L. Glosten,et al.  On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks , 1993 .

[52]  Guangxi Cao,et al.  Multifractal detrended cross-correlations between the Chinese exchange market and stock market , 2012 .

[53]  T. Aste,et al.  Understanding the source of multifractality in financial markets , 2012, 1201.1535.

[54]  Rongbao Gu,et al.  Analysis of efficiency for Shenzhen stock market based on multifractal detrended fluctuation analysis , 2009 .