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Modeling daily realized futures volatility with singular spectrum analysis

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  • Thomakos, Dimitrios D.
  • Wang, Tao
  • Wille, Luc T.

Abstract

Using singular spectrum analysis (SSA), we model the realized volatility and logarithmic standard deviations of two important futures return series. The realized volatility and logarithmic standard deviations are constructed following the methodology of Andersen et al. [J. Am. Stat. Ass. 96 (2001) 42–55] using intra-day transaction data. We find that SSA decomposes the volatility series quite well and effectively captures both the market trend (accounting for about 34–38% of the total variance in the series) and, more importantly, a number of underlying market periodicities. Reliable identification of any periodicities is extremely important for options pricing and risk management and we believe that SSA can be a useful addition to the financial practitioners’ toolbox.

Suggested Citation

  • Thomakos, Dimitrios D. & Wang, Tao & Wille, Luc T., 2002. "Modeling daily realized futures volatility with singular spectrum analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 312(3), pages 505-519.
    • Handle: RePEc:eee:phsmap:v:312:y:2002:i:3:p:505-519
    DOI: 10.1016/S0378-4371(02)00845-2
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    1. Andersen T. G & Bollerslev T. & Diebold F. X & Labys P., 2001. "The Distribution of Realized Exchange Rate Volatility," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 42-55, March.
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    2. M. Atikur Rahman Khan & D.S. Poskitt, 2014. "On The Theory and Practice of Singular Spectrum Analysis Forecasting," Monash Econometrics and Business Statistics Working Papers 3/14, Monash University, Department of Econometrics and Business Statistics.
    3. Arteche, Josu & García-Enríquez, Javier, 2017. "Singular Spectrum Analysis for signal extraction in Stochastic Volatility models," Econometrics and Statistics, Elsevier, vol. 1(C), pages 85-98.
    4. Dimitrios D. Thomakos, 2008. "Optimal Linear Filtering, Smoothing and Trend Extraction for Processes with Unit Roots and Cointegration," Working Paper series 14_08, Rimini Centre for Economic Analysis.
    5. Degiannakis, Stavros & Filis, George & Hassani, Hossein, 2015. "Forecasting implied volatility indices worldwide: A new approach," MPRA Paper 72084, University Library of Munich, Germany.
    6. Lahmiri, Salim, 2018. "Minute-ahead stock price forecasting based on singular spectrum analysis and support vector regression," Applied Mathematics and Computation, Elsevier, vol. 320(C), pages 444-451.
    7. Degiannakis, Stavros & Filis, George & Hassani, Hossein, 2018. "Forecasting global stock market implied volatility indices," Journal of Empirical Finance, Elsevier, vol. 46(C), pages 111-129.
    8. Lee, Heera & Bogner, Christina & Lee, Saem & Koellner, Thomas, 2016. "Crop selection under price and yield fluctuation: Analysis of agro-economic time series from South Korea," Agricultural Systems, Elsevier, vol. 148(C), pages 1-11.
    9. Khan, M. Atikur Rahman & Poskitt, D.S., 2017. "Forecasting stochastic processes using singular spectrum analysis: Aspects of the theory and application," International Journal of Forecasting, Elsevier, vol. 33(1), pages 199-213.
    10. Md Atikur Rahman Khan & D.S. Poskitt, 2010. "Description Length Based Signal Detection in singular Spectrum Analysis," Monash Econometrics and Business Statistics Working Papers 13/10, Monash University, Department of Econometrics and Business Statistics.
    11. Marcin Wk{a}torek & Stanis{l}aw Dro.zd.z & Jaros{l}aw Kwapie'n & Ludovico Minati & Pawe{l} O'swik{e}cimka & Marek Stanuszek, 2020. "Multiscale characteristics of the emerging global cryptocurrency market," Papers 2010.15403, arXiv.org, revised Mar 2021.
    12. Wei, Yu, 2012. "Forecasting volatility of fuel oil futures in China: GARCH-type, SV or realized volatility models?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5546-5556.
    13. Papailias, Fotis & Thomakos, Dimitrios, 2017. "EXSSA: SSA-based reconstruction of time series via exponential smoothing of covariance eigenvalues," International Journal of Forecasting, Elsevier, vol. 33(1), pages 214-229.
    14. Sella Lisa, 2008. "Old and New Spectral Techniques for Economic Time Series," Department of Economics and Statistics Cognetti de Martiis. Working Papers 200809, University of Turin.

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