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FX Market Volatility

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  • Anton Koshelev

Abstract

This paper aims at solving FX market volatility modeling problem and finding the most becoming approach to this task. Validity of two competing approaches, classical econometric generalized conditional heteroscedasticity and mathematical (singular spectrum analysis and dynamical systems stability analysis) are tested on major currency pairs (EUR/USD, USD/JPY, GBP/USD) and unique high-frequency USD/RUB data. The study shows that both mathematical tools, understudied in econometric discourse, have a great potential in scope of discussed problematic, as for all experiments covered in this research, both of them show promising results.

Suggested Citation

  • Anton Koshelev, 2021. "FX Market Volatility," Papers 2104.14190, arXiv.org.
    • Handle: RePEc:arx:papers:2104.14190
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    References listed on IDEAS

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    1. 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.
    2. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
    3. Philip Wooldridge, 2019. "FX and OTC derivatives markets through the lens of the Triennial Survey," BIS Quarterly Review, Bank for International Settlements, December.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. Klein, Michael W. & Shambaugh, Jay C., 2012. "Exchange Rate Regimes in the Modern Era," MIT Press Books, The MIT Press, edition 1, volume 1, number 026251799x, December.
    6. Stavros Degiannakis, 2008. "ARFIMAX and ARFIMAX-TARCH realized volatility modeling," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(10), pages 1169-1180.
    7. Engle, Robert F & Ng, Victor K, 1993. "Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-1778, December.
    8. Andrew Harvey & Esther Ruiz & Neil Shephard, 1994. "Multivariate Stochastic Variance Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(2), pages 247-264.
    9. Böhm, Volker & Wenzelburger, Jan, 1999. "Expectations, Forecasting, And Perfect Foresight," Macroeconomic Dynamics, Cambridge University Press, vol. 3(2), pages 167-186, June.
    10. Christensen, B. J. & Prabhala, N. R., 1998. "The relation between implied and realized volatility," Journal of Financial Economics, Elsevier, vol. 50(2), pages 125-150, November.
    11. 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.
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