Multiple-days-ahead value-at-risk and expected shortfall forecasting for stock indices, commodities and exchange rates: inter-day versus intra-day data
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- Degiannakis, Stavros & Potamia, Artemis, 2017. "Multiple-days-ahead value-at-risk and expected shortfall forecasting for stock indices, commodities and exchange rates: Inter-day versus intra-day data," International Review of Financial Analysis, Elsevier, vol. 49(C), pages 176-190.
References listed on IDEAS
- Giot, Pierre & Laurent, Sebastien, 2004.
"Modelling daily Value-at-Risk using realized volatility and ARCH type models,"
Journal of Empirical Finance, Elsevier, vol. 11(3), pages 379-398, June.
- Giot, P. & Laurent, S.F.J.A., 2001. "Modelling daily value-at-risk using realized volatility and arch type models," Research Memorandum 026, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
- GIOT, Pierre & LAURENT, Sébastien, 2004. "Modelling daily Value-at-Risk using realized volatility and ARCH type models," LIDAM Reprints CORE 1708, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Pierre Giot & Sébastien Laurent, 2002. "Modelling Daily Value-at-Risk Using Realized Volatility and ARCH Type Models," Computing in Economics and Finance 2002 52, Society for Computational Economics.
- Degiannakis, Stavros & Floros, Christos & Dent, Pamela, 2013.
"Forecasting value-at-risk and expected shortfall using fractionally integrated models of conditional volatility: International evidence,"
International Review of Financial Analysis, Elsevier, vol. 27(C), pages 21-33.
- Degiannakis, Stavros & Floros, Christos & Dent, Pamela, 2013. "Forecasting Value-at-Risk and Expected Shortfall using Fractionally Integrated Models of Conditional Volatility: International Evidence," MPRA Paper 80433, University Library of Munich, Germany.
- Pierre Giot & Sébastien Laurent, 2003.
"Value-at-risk for long and short trading positions,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(6), pages 641-663.
- GIOT, Pierre & LAURENT, Sébastien, 2001. "Value-at-risk for long and short trading positions," LIDAM Discussion Papers CORE 2001022, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- GIOT, Pierre & LAURENT, Sébastien, 2003. "Value-at-Risk for long and short trading positions," LIDAM Reprints CORE 1707, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Pierre Giot and S»bastien Laurent, 2001. "Value-At-Risk For Long And Short Trading Positions," Computing in Economics and Finance 2001 94, Society for Computational Economics.
- Degiannakis, Stavros & Floros, Christos, 2016.
"Intra-day realized volatility for European and USA stock indices,"
Global Finance Journal, Elsevier, vol. 29(C), pages 24-41.
- Degiannakis, Stavros & Floros, Christos, 2014. "Intra-Day Realized Volatility for European and USA Stock Indices," MPRA Paper 64940, University Library of Munich, Germany, revised Jan 2015.
- Angelidis, Timotheos & Degiannakis, Stavros, 2008.
"Volatility forecasting: Intra-day versus inter-day models,"
Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 18(5), pages 449-465, December.
- Angelidis, Timotheos & Degiannakis, Stavros, 2008. "Volatility forecasting: Intra-day versus inter-day models," MPRA Paper 96322, University Library of Munich, Germany.
- Peter R. Hansen & Asger Lunde & James M. Nason, 2011.
"The Model Confidence Set,"
Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
- Peter R. Hansen & Asger Lunde & James M. Nason, 2010. "The Model Confidence Set," CREATES Research Papers 2010-76, Department of Economics and Business Economics, Aarhus University.
- Martens, Martin & van Dijk, Dick & de Pooter, Michiel, 2009. "Forecasting S&P 500 volatility: Long memory, level shifts, leverage effects, day-of-the-week seasonality, and macroeconomic announcements," International Journal of Forecasting, Elsevier, vol. 25(2), pages 282-303.
- Jose A. Lopez, 1999.
"Methods for evaluating value-at-risk estimates,"
Economic Review, Federal Reserve Bank of San Francisco, pages 3-17.
- Jose A. Lopez, 1998. "Methods for evaluating value-at-risk estimates," Economic Policy Review, Federal Reserve Bank of New York, vol. 4(Oct), pages 119-124.
- Jose A. Lopez, 1998. "Methods for evaluating value-at-risk estimates," Research Paper 9802, Federal Reserve Bank of New York.
- Stavros Degiannakis & Pamela Dent & Christos Floros, 2014.
"A Monte Carlo Simulation Approach to Forecasting Multi-period Value-at-Risk and Expected Shortfall Using the FIGARCH-skT Specification,"
Manchester School, University of Manchester, vol. 82(1), pages 71-102, January.
- Degiannakis, Stavros & Dent, Pamela & Floros, Christos, 2014. "A Monte Carlo Simulation Approach to Forecasting Multi-period Value-at-Risk and Expected Shortfall Using the FIGARCH-skT Specification," MPRA Paper 80431, University Library of Munich, Germany.
- Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
- Huiyu Huang & Tae-Hwy Lee, 2013.
"Forecasting Value-at-Risk Using High-Frequency Information,"
Econometrics, MDPI, vol. 1(1), pages 1-14, June.
- Tae-Hwy Lee & Huiyu Huang, 2014. "Forecasting Value-at-Risk Using High Frequency Information," Working Papers 201409, University of California at Riverside, Department of Economics.
- Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-862, November.
- Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
- McMillan, David G. & Kambouroudis, Dimos, 2009. "Are RiskMetrics forecasts good enough? Evidence from 31 stock markets," International Review of Financial Analysis, Elsevier, vol. 18(3), pages 117-124, June.
- Timotheos Angelidis & Stavros Degiannakis, 2005. "Modeling risk for long and short trading positions," Journal of Risk Finance, Emerald Group Publishing, vol. 6(3), pages 226-238, May.
- Bollerslev, Tim, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
- Tim Bollerslev, 1986. "Generalized autoregressive conditional heteroskedasticity," EERI Research Paper Series EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Angelidis, Timotheos & Benos, Alexandros & Degiannakis, Stavros, 2004.
"The Use of GARCH Models in VaR Estimation,"
MPRA Paper
96332, University Library of Munich, Germany.
- Timotheos Angelidis & Alexandros Benos & Stavros Degiannakis, 2010. "The Use of GARCH Models in VaR Estimation," Working Papers 0048, University of Peloponnese, Department of Economics.
- Timotheos Angelidis & Stavros Degiannakis, 2005.
"Modeling risk for long and short trading positions,"
Journal of Risk Finance, Emerald Group Publishing Limited, vol. 6(3), pages 226-238, July.
- Angelidis, Timotheos & Degiannakis, Stavros, 2005. "Modeling Risk for Long and Short Trading Positions," MPRA Paper 80467, University Library of Munich, Germany.
- Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
- Christoffersen, Peter, 2011.
"Elements of Financial Risk Management,"
Elsevier Monographs,
Elsevier,
edition 2, number 9780123744487.
- Christoffersen, Peter, 2003. "Elements of Financial Risk Management," Elsevier Monographs, Elsevier, edition 1, number 9780121742324.
- Peter F. Christoffersen & Francis X. Diebold, 2000.
"How Relevant is Volatility Forecasting for Financial Risk Management?,"
The Review of Economics and Statistics, MIT Press, vol. 82(1), pages 12-22, February.
- Peter F. Christoffersen & Francis X. Diebold, 1997. "How Relevant is Volatility Forecasting for Financial Risk Management?," Center for Financial Institutions Working Papers 97-45, Wharton School Center for Financial Institutions, University of Pennsylvania.
- Peter F. Christoffersen & Francis X. Diebold, 1998. "How Relevant is Volatility Forecasting for Financial Risk Management?," New York University, Leonard N. Stern School Finance Department Working Paper Seires 98-080, New York University, Leonard N. Stern School of Business-.
- Peter F. Christoffersen & Francis X. Diebold, 1998. "How Relevant is Volatility Forecasting for Financial Risk Management?," NBER Working Papers 6844, National Bureau of Economic Research, Inc.
- Asger Lunde & Peter R. Hansen, 2005.
"A forecast comparison of volatility models: does anything beat a GARCH(1,1)?,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
- Asger Lunde & Peter Reinhard Hansen, 2001. "A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)?," Working Papers 2001-04, Brown University, Department of Economics.
- Dimitrios P. Louzis & Spyros Xanthopoulos‐Sisinis & Apostolos P. Refenes, 2013.
"The Role of High‐Frequency Intra‐daily Data, Daily Range and Implied Volatility in Multi‐period Value‐at‐Risk Forecasting,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(6), pages 561-576, September.
- Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "The role of high frequency intra-daily data, daily range and implied volatility in multi-period Value-at-Risk forecasting," MPRA Paper 35252, University Library of Munich, Germany.
- Robert Engle, 2004.
"Risk and Volatility: Econometric Models and Financial Practice,"
American Economic Review, American Economic Association, vol. 94(3), pages 405-420, June.
- Engle III, Robert F., 2003. "Risk and Volatility: Econometric Models and Financial Practice," Nobel Prize in Economics documents 2003-4, Nobel Prize Committee.
- Adam Krzemienowski & Sylwia Szymczyk, 2016. "Portfolio optimization with a copula-based extension of conditional value-at-risk," Annals of Operations Research, Springer, vol. 237(1), pages 219-236, February.
- Adam Krzemienowski & Sylwia Szymczyk, 2016. "Portfolio optimization with a copula-based extension of conditional value-at-risk," Annals of Operations Research, Springer, vol. 237(1), pages 219-236, February.
- Stavros Degiannakis, 2004.
"Volatility forecasting: evidence from a fractional integrated asymmetric power ARCH skewed-t model,"
Applied Financial Economics, Taylor & Francis Journals, vol. 14(18), pages 1333-1342.
- Degiannakis, Stavros, 2004. "Volatility Forecasting: Evidence from a Fractional Integrated Asymmetric Power ARCH Skewed-t Model," MPRA Paper 96330, University Library of Munich, Germany.
- Koopman, Siem Jan & Jungbacker, Borus & Hol, Eugenie, 2005.
"Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements,"
Journal of Empirical Finance, Elsevier, vol. 12(3), pages 445-475, June.
- Siem Jan Koopman & Borus Jungbacker & Eugenie Hol, 2004. "Forecasting Daily Variability of the S&P 100 Stock Index using Historical, Realised and Implied Volatility Measurements," Tinbergen Institute Discussion Papers 04-016/4, Tinbergen Institute.
- Eugenie Hol & Siem Jan Koopman & Borus Jungbacker, 2004. "Forecasting daily variability of the S\&P 100 stock index using historical, realised and implied volatility measurements," Computing in Economics and Finance 2004 342, Society for Computational Economics.
- Lo, Andrew W. & Craig MacKinlay, A., 1990.
"An econometric analysis of nonsynchronous trading,"
Journal of Econometrics, Elsevier, vol. 45(1-2), pages 181-211.
- Andrew W. Lo & Craig A. MacKinlay, "undated". "An Econometric Analysis of Nonsyschronous-Trading," Rodney L. White Center for Financial Research Working Papers 19-89, Wharton School Rodney L. White Center for Financial Research.
- Andrew W. Lo & A. Craig MacKinlay, 1989. "An Econometric Analysis of Nonsynchronous Trading," NBER Working Papers 2960, National Bureau of Economic Research, Inc.
- Toshiaki Watanabe, 2012. "Quantile Forecasts Of Financial Returns Using Realized Garch Models," The Japanese Economic Review, Japanese Economic Association, vol. 63(1), pages 68-80, March.
- Angelidis, Timotheos & Degiannakis, Stavros, 2007.
"Backtesting VaR Models: A Τwo-Stage Procedure,"
MPRA Paper
96327, University Library of Munich, Germany.
- Angelidis, Timotheos & Degiannakis, Stavros, 2007. "Backtesting VaR Models: A Τwo-Stage Procedure," MPRA Paper 80418, University Library of Munich, Germany.
- Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
- Pierre Giot, 2005.
"Market risk models for intraday data,"
The European Journal of Finance, Taylor & Francis Journals, vol. 11(4), pages 309-324.
- GIOT, Pierre, 2005. "Market risk models for intraday data," LIDAM Reprints CORE 1850, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
- Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
- Diebold, Francis X & Mariano, Roberto S, 2002.
"Comparing Predictive Accuracy,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
- Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-263, July.
- Francis X. Diebold & Roberto S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
- Manuela Braione & Nicolas K. Scholtes, 2016. "Forecasting Value-at-Risk under Different Distributional Assumptions," Econometrics, MDPI, vol. 4(1), pages 1-27, January.
- Kellner, Ralf & Rösch, Daniel, 2016. "Quantifying market risk with Value-at-Risk or Expected Shortfall? – Consequences for capital requirements and model risk," Journal of Economic Dynamics and Control, Elsevier, vol. 68(C), pages 45-63.
- Giot, Pierre & Laurent, Sebastien, 2003.
"Market risk in commodity markets: a VaR approach,"
Energy Economics, Elsevier, vol. 25(5), pages 435-457, September.
- GIOT, Pierre & LAURENT, Sébastien, 2003. "Market risk in commodity markets: a VaR approach," LIDAM Reprints CORE 1682, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- GIOT, Pierre & LAURENT, Sébastien, 2003. "Market risk in commodity markets: a VaR approach," LIDAM Discussion Papers CORE 2003028, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Su, Jung-Bin, 2015. "Value-at-risk estimates of the stock indices in developed and emerging markets including the spillover effects of currency market," Economic Modelling, Elsevier, vol. 46(C), pages 204-224.
- Hansen, Bruce E, 1994.
"Autoregressive Conditional Density Estimation,"
International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 35(3), pages 705-730, August.
- Hansen, B.E., 1992. "Autoregressive Conditional Density Estimation," RCER Working Papers 322, University of Rochester - Center for Economic Research (RCER).
- Tom Doan, "undated". "RATS programs to replicate Hansen's GARCH models with time-varying t-densities," Statistical Software Components RTZ00086, Boston College Department of Economics.
- Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
- Peter Reinhard Hansen & Asger Lunde, 2005. "A Realized Variance for the Whole Day Based on Intermittent High-Frequency Data," Journal of Financial Econometrics, Oxford University Press, vol. 3(4), pages 525-554.
- BRAIONE, Manuela & SCHOLTES, Nicolas K., 2016. "Forecasting Value-at-Risk under Different Distributional Assumptions," LIDAM Reprints CORE 2733, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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More about this item
Keywords
Basel II; Basel III; Value-at-Risk; Expected Shortfall; volatility forecasting; intra-day data; multi-period-ahead; forecasting accuracy; risk modelling.;All these keywords.
JEL classification:
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
NEP fields
This paper has been announced in the following NEP Reports:- NEP-FOR-2016-10-30 (Forecasting)
- NEP-ORE-2016-10-30 (Operations Research)
- NEP-RMG-2016-10-30 (Risk Management)
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