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The Jump Behavior of a Foreign Exchange Market: Analysis of the Thai Baht

In: HANDBOOK OF FINANCIAL ECONOMETRICS, MATHEMATICS, STATISTICS, AND MACHINE LEARNING

Author

Listed:
  • Jow-Ran Chang
  • Mao-Wei Hung
  • Cheng Few Lee
  • Hsin-Min Lu

Abstract

We study the heteroskedasticity and jump behavior of the Thai baht using models of the square root stochastic volatility with or without jumps. The Bayesian factor is used to evaluate the explanatory power of competing models. The results suggest that in our sample, the square root stochastic volatility model with independent jumps in the observation and state equations (SVIJ) has the best explanatory power for the 1996 Asian financial crisis. Using the estimation results of the SVIJ model, we are able to link the major events of the Asian financial crisis to jump behavior in either volatility or observation.

Suggested Citation

  • Jow-Ran Chang & Mao-Wei Hung & Cheng Few Lee & Hsin-Min Lu, 2020. "The Jump Behavior of a Foreign Exchange Market: Analysis of the Thai Baht," World Scientific Book Chapters, in: Cheng Few Lee & John C Lee (ed.), HANDBOOK OF FINANCIAL ECONOMETRICS, MATHEMATICS, STATISTICS, AND MACHINE LEARNING, chapter 52, pages 1943-1968, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9789811202391_0052
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    References listed on IDEAS

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    1. Eraker, Bjorn, 2002. "Do Stock Prices and Volatility Jump? Reconciling Evidence from Spot and Option Prices," Working Papers 02-23, Duke University, Department of Economics.
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    Citations

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    Cited by:

    1. Cheng Few Lee, 2020. "Financial econometrics, mathematics, statistics, and financial technology: an overall view," Review of Quantitative Finance and Accounting, Springer, vol. 54(4), pages 1529-1578, May.
    2. Sharif Mozumder & Ghulam Sorwar & Kevin Dowd, 2013. "Option pricing under non-normality: a comparative analysis," Review of Quantitative Finance and Accounting, Springer, vol. 40(2), pages 273-292, February.
    3. Ahmed Hachicha & Fatma Hachicha & Afif Masmoudi, 2012. "A comparative study of two models SV with MCMC algorithm," Review of Quantitative Finance and Accounting, Springer, vol. 38(4), pages 479-493, May.
    4. Konstantinos Gkillas & Rangan Gupta & Mark E. Wohar, 2020. "Oil shocks and volatility jumps," Review of Quantitative Finance and Accounting, Springer, vol. 54(1), pages 247-272, January.
    5. Chih-Chen Hsu & An-Sing Chen & Shih-Kuei Lin & Ting-Fu Chen, 2017. "The affine styled-facts price dynamics for the natural gas: evidence from daily returns and option prices," Review of Quantitative Finance and Accounting, Springer, vol. 48(3), pages 819-848, April.
    6. Cheng-Few Lee & Oleg Sokolinskiy, 2015. "R-2GAM stochastic volatility model: flexibility and calibration," Review of Quantitative Finance and Accounting, Springer, vol. 45(3), pages 463-483, October.

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    More about this item

    Keywords

    Financial Econometrics; Financial Mathematics; Financial Statistics; Financial Technology; Machine Learning; Covariance Regression; Cluster Effect; Option Bound; Dynamic Capital Budgeting; Big Data;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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