Can Realized Volatility improve the Accuracy of Value-at-Risk Forecasts?

[1]  Marc S. Paolella,et al.  Value-at-Risk Prediction: A Comparison of Alternative Strategies , 2005 .

[2]  Jose A. Lopez.  Regulatory Evaluation of Value-at-Risk Models , 1996 .

[3]  Chung-Ming Kuan,et al.  Reexamining the Profitability of Technical Analysis with Data Snooping Checks , 2005 .

[4]  D. Cox,et al.  Time series models : in econometrics, finance and other fields , 1997 .

[5]  Susan Thomas,et al.  Selection of Value-at-Risk models , 2003 .

[6]  J. Wooldridge,et al.  Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances , 1992 .

[7]  Stephen L Taylor,et al.  Forecasting Currency Volatility: A Comparison of Implied Volatilities and AR(FI)MA Models , 2003 .

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

[9]  T. Bollerslev,et al.  A Discrete-Time Model for Daily S&P500 Returns and Realized Variations: Jumps and Leverage Effects , 2007 .

[10]  S. Satchell,et al.  Forecasting Volatility in Financial Markets : A Review , 2004 .

[11]  S. Laurent,et al.  Modelling skewness dynamics in series of financial data using skewed location-scale distributions , 2001 .

[12]  N. Shephard.  Statistical aspects of ARCH and stochastic volatility , 1996 .

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

[14]  J. Durbin,et al.  Monte Carlo maximum likelihood estimation for non-Gaussian state space models , 1997 .

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

[16]  Y. Tse,et al.  Residual-Based Diagnostics for Conditional Heteroscedasticity Models , 2002 .

[17]  Campbell R. Harvey,et al.  Current Version : April 6 , 1999 Autoregressive Conditional Skewness , 1999 .

[18]  N. Shephard,et al.  Stochastic Volatility: Likelihood Inference And Comparison With Arch Models , 1996 .

[19]  Yong Bao,et al.  Evaluating Predictive Performance of Value-at-Risk Models in Emerging Markets: A Reality Check , 2006 .

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

[21]  S. Turnbull,et al.  Pricing foreign currency options with stochastic volatility , 1990 .

[22]  Joseph P. Romano,et al.  The stationary bootstrap , 1994 .

[23]  B. M. Hill,et al.  A Simple General Approach to Inference About the Tail of a Distribution , 1975 .

[24]  Marc S. Paolella,et al.  Conditional density and value‐at‐risk prediction of Asian currency exchange rates , 2000 .

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

[27]  R. Koenker,et al.  Regression Quantiles , 2007 .

[28]  Marius Ooms,et al.  A Package for Estimating, Forecasting and Simulating Arfima Models: Arfima package 1.0 for Ox , 1999 .

[29]  N. Shephard,et al.  Estimating quadratic variation using realized variance , 2002 .

[30]  J. Geweke,et al.  THE ESTIMATION AND APPLICATION OF LONG MEMORY TIME SERIES MODELS , 1983 .

[31]  E. Ruiz,et al.  Persistence and Kurtosis in GARCH and Stochastic Volatility Models , 2004 .

[32]  Siem Jan Koopman,et al.  A simple and efficient simulation smoother for state space time series analysis , 2002 .

[33]  Peter Christoffersen,et al.  Série Scientifique Scientific Series 2003 s-05 Backtesting Value-at-Risk : A Duration-Based Approach , 2003 .

[34]  R. Oomen.  Properties of Bias-Corrected Realized Variance Under Alternative Sampling Schemes , 2005 .

[35]  M. Pitt,et al.  Likelihood analysis of non-Gaussian measurement time series , 1997 .

[36]  Giovanni Barone-Adesi,et al.  Backtesting Derivative Portfolios with Filtered Historical Simulation (Fhs) , 2002 .

[37]  Eric R. Ziegel,et al.  Analysis of Financial Time Series , 2002, Technometrics.

[38]  Stavros Degiannakis,et al.  The Use of GARCH Models in VaR Estimation , 2004 .

[39]  Michael P. Clements,et al.  Evaluating interval forecasts of high-frequency financial data , 2003 .

[40]  R. Baillie,et al.  Fractionally integrated generalized autoregressive conditional heteroskedasticity , 1996 .

[41]  Philippe Artzner,et al.  Coherent Measures of Risk , 1999 .

[42]  Matthew Pritsker,et al.  The Hidden Dangers of Historical Simulation , 2001 .

[43]  Norman R. Swanson,et al.  Nonparametric Bootstrap Procedures for Predictive Inference Based on Recursive Estimation Schemes , 2005 .

[44]  J. Corcoran.  Modelling Extremal Events for Insurance and Finance , 2002 .

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

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

[47]  Siem Jan Koopman,et al.  Estimating Stochastic Volatility Models: A Comparison of Two Importance Samplers , 2004 .

[48]  Fulvio Corsi,et al.  A Simple Long Memory Model of Realized Volatility , 2004 .

[49]  Sébastien Laurent,et al.  G@RCH 2.2: An Ox Package for Estimating and Forecasting Various ARCH Models , 2001 .

[50]  P. Robinson,et al.  LONG AND SHORT MEMORY CONDITIONAL HETEROSKEDASTICITY IN ESTIMATING THE MEMORY PARAMETER OF LEVELS , 1999, Econometric Theory.

[51]  Dick J. C. van Dijk,et al.  Modeling and Forecasting S&P 500 Volatility: Long Memory, Structural Breaks and Nonlinearity , 2004 .

[52]  Chris Kirby,et al.  A Closer Look at the Relation between GARCH and Stochastic Autoregressive Volatility , 2003 .

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

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

[55]  Olaf Schoffer.  HY-A-PARCH: A stationary A-PARCH model with long memory , 2003 .

[56]  Richard T. Baillie,et al.  Long memory processes and fractional integration in econometrics , 1996 .

[57]  Peter Christoffersen,et al.  Série Scientifique Scientific Series Estimation Risk in Financial Risk Management , 2022 .

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

[59]  S. Mittnik,et al.  The Volatility of Realized Volatility , 2005 .

[60]  A. I. McLeod,et al.  Preservation of the rescaled adjusted range: 1. A reassessment of the Hurst Phenomenon , 1978 .

[61]  A. McNeil,et al.  Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach , 2000 .

[62]  N. Shephard,et al.  Estimation of an Asymmetric Stochastic Volatility Model for Asset Returns , 1996 .

[63]  Juri Marcucci.  Forecasting Stock Market Volatility with Regime-Switching GARCH Models , 2005 .

[64]  S. Laurent,et al.  Modelling Daily Value-at-Risk Using Realized Volatility and Arch Type Models , 2001 .

[65]  Dominique Guégan,et al.  How can we Define the Concept of Long Memory? An Econometric Survey , 2005 .

[66]  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.

[67]  Jon Danielsson,et al.  Forecasting Extreme Financial Risk: A Critical Analysis of Practical Methods for the Japanese Market , 2000 .

[68]  M. Steel,et al.  On Bayesian Modelling of Fat Tails and Skewness , 1998 .

[69]  Jonathan D. Cryer,et al.  Time Series Analysis , 1986 .

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

[71]  M. McAleer,et al.  Dynamic Asymmetric Leverage in Stochastic Volatility Models , 2005 .

[72]  M. Ivimey.  Annual report , 1958, IRE Transactions on Engineering Writing and Speech.

[73]  F. Diebold,et al.  How Relevant is Volatility Forecasting for Financial Risk Management? , 1997, Review of Economics and Statistics.

[74]  T. Bollerslev,et al.  A CONDITIONALLY HETEROSKEDASTIC TIME SERIES MODEL FOR SPECULATIVE PRICES AND RATES OF RETURN , 1987 .

[75]  A. Gallant,et al.  Using Daily Range Data to Calibrate Volatility Diffusions and Extract the Forward Integrated Variance , 1999, Review of Economics and Statistics.