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Hedge Ratio and Time Series Analysis

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

Author

Listed:
  • Sheng-Syan Chen
  • Cheng Few Lee
  • Keshab Shresth

Abstract

This chapter discusses both static and dynamic hedge ratio in detail. In static analysis, we discuss minimum-variance hedge ratio, Sharpe hedge ratio, and optimum mean-variance hedge ratio. In addition, several time series analysis methods such as the multivariate skew-normal distribution method, the autoregressive conditional heteroskedasticity (ARCH) and generalized autoregressive conditional heteroskedasticity (GARCH) methods, the regime-switching GARCH model, and the random coefficient method are used to show how hedge ratio can be estimated.

Suggested Citation

  • Sheng-Syan Chen & Cheng Few Lee & Keshab Shresth, 2020. "Hedge Ratio and Time Series Analysis," World Scientific Book Chapters, in: Cheng Few Lee & John C Lee (ed.), HANDBOOK OF FINANCIAL ECONOMETRICS, MATHEMATICS, STATISTICS, AND MACHINE LEARNING, chapter 11, pages 431-483, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9789811202391_0011
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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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