GARCHNet - Value-at-Risk forecasting with novel approach to GARCH models based on neural networks
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- Segal, Gill & Shaliastovich, Ivan & Yaron, Amir, 2015.
"Good and bad uncertainty: Macroeconomic and financial market implications,"
Journal of Financial Economics, Elsevier, vol. 117(2), pages 369-397.
- Gill Segal & Ivan Shaliastovich & Amir Yaron, 2014. "Good and Bad Uncertainty: Macroeconomic and Financial Market Implications," 2014 Meeting Papers 488, Society for Economic Dynamics.
- Luca Zanin & Giampiero Marra, 2012. "Rolling Regression Versus Time‐Varying Coefficient Modelling: An Empirical Investigation Of The Okun'S Law In Some Euro Area Countries," Bulletin of Economic Research, Wiley Blackwell, vol. 64(1), pages 91-108, January.
- Abad, Pilar & Benito, Sonia, 2013. "A detailed comparison of value at risk estimates," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 94(C), pages 258-276.
- 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.
- Robert F. Engle & Simone Manganelli, 2004.
"CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
- Engle, Robert F & Manganelli, Simone, 1999. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," University of California at San Diego, Economics Working Paper Series qt06m3d6nv, Department of Economics, UC San Diego.
- Robert Engle & Simone Manganelli, 2000. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Econometric Society World Congress 2000 Contributed Papers 0841, Econometric Society.
- 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.
- Jonas Rothfuss & Fabio Ferreira & Simon Walther & Maxim Ulrich, 2019. "Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks," Papers 1903.00954, arXiv.org, revised Apr 2019.
- Nelson, Daniel B & Cao, Charles Q, 1992. "Inequality Constraints in the Univariate GARCH Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(2), pages 229-235, April.
- Ergün, A. Tolga & Jun, Jongbyung, 2010. "Time-varying higher-order conditional moments and forecasting intraday VaR and Expected Shortfall," The Quarterly Review of Economics and Finance, Elsevier, vol. 50(3), pages 264-272, August.
- Aloui, Chaker & Mabrouk, Samir, 2010. "Value-at-risk estimations of energy commodities via long-memory, asymmetry and fat-tailed GARCH models," Energy Policy, Elsevier, vol. 38(5), pages 2326-2339, May.
- Escanciano, J. Carlos & Olmo, Jose, 2010.
"Backtesting Parametric Value-at-Risk With Estimation Risk,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 36-51.
- Juan Carlos Escanciano & Jose Olmo, 2007. "Backtesting Parametric Value-at-Risk with Estimation Risk," CAEPR Working Papers 2007-005, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington, revised Sep 2008.
- Chlebus Marcin, 2017.
"EWS-GARCH: New Regime Switching Approach to Forecast Value-at-Risk,"
Central European Economic Journal, Sciendo, vol. 3(50), pages 01-25, December.
- Marcin Chlebus, 2016. "EWS-GARCH: New Regime Switching Approach to Forecast Value-at-Risk," Working Papers 2016-06, Faculty of Economic Sciences, University of Warsaw.
- Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
- 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.).
- Wang, Zong-Run & Chen, Xiao-Hong & Jin, Yan-Bo & Zhou, Yan-Ju, 2010. "Estimating risk of foreign exchange portfolio: Using VaR and CVaR based on GARCH–EVT-Copula model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4918-4928.
- Matteo Bonato, 2012. "Modeling fat tails in stock returns: a multivariate stable-GARCH approach," Computational Statistics, Springer, vol. 27(3), pages 499-521, September.
- Vorbrink, Jörg, 2014. "Financial markets with volatility uncertainty," Journal of Mathematical Economics, Elsevier, vol. 53(C), pages 64-78.
- Giovanni Barone Adesi & Robert F. Engle & Loriano Mancini, 2014.
"A GARCH Option Pricing Model with Filtered Historical Simulation,"
Palgrave Macmillan Books, in: Giovanni Barone Adesi (ed.), Simulating Security Returns: A Filtered Historical Simulation Approach, chapter 4, pages 66-108,
Palgrave Macmillan.
- Giovanni Barone-Adesi & Robert F. Engle & Loriano Mancini, 2008. "A GARCH Option Pricing Model with Filtered Historical Simulation," The Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1223-1258, May.
- 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.
- Degiannakis, Stavros & Floros, Christos & Livada, Alexandra, 2012. "Evaluating Value-at-Risk Models before and after the Financial Crisis of 2008: International Evidence," MPRA Paper 80463, University Library of Munich, Germany.
- Omer Berat Sezer & Mehmet Ugur Gudelek & Ahmet Murat Ozbayoglu, 2019. "Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019," Papers 1911.13288, arXiv.org.
- Nick Costanzino & Michael Curran, 2018. "A Simple Traffic Light Approach to Backtesting Expected Shortfall," Risks, MDPI, vol. 6(1), pages 1-7, January.
- BenSaïda, Ahmed, 2015. "The frequency of regime switching in financial market volatility," Journal of Empirical Finance, Elsevier, vol. 32(C), pages 63-79.
- Trong-Nghia Nguyen & Minh-Ngoc Tran & David Gunawan & R. Kohn, 2019. "A Statistical Recurrent Stochastic Volatility Model for Stock Markets," Papers 1906.02884, arXiv.org, revised Jan 2022.
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More about this item
Keywords
Value-at-Risk; GARCH; neural networks; LSTM;All these keywords.
JEL classification:
- G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
NEP fields
This paper has been announced in the following NEP Reports:- NEP-BIG-2021-06-14 (Big Data)
- NEP-CMP-2021-06-14 (Computational Economics)
- NEP-CWA-2021-06-14 (Central and Western Asia)
- NEP-ECM-2021-06-14 (Econometrics)
- NEP-ETS-2021-06-14 (Econometric Time Series)
- NEP-FOR-2021-06-14 (Forecasting)
- NEP-ORE-2021-06-14 (Operations Research)
- NEP-RMG-2021-06-14 (Risk Management)
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