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Information Theoretic Ranking of Extreme Value Returns

Author

Listed:
  • Parthajit Kayal

    (Madras School of Economics (MSE), Behind Government Data Centre)

  • Sumanjay Dutta

    (Indian Institute of Science)

  • Vipul Khandelwal

    (Tata Consultancy Services (TCS))

  • Rakesh Nigam

    (Madras School of Economics (MSE), Behind Government Data Centre)

Abstract

This study highlights the importance of the information contained extreme value ratios (or returns) in the volatility estimation of financial assets. Most popular extreme value estimators like Parkinson (Journal of Business, 61–65, 1980), Garman Klass (Journal of business, 67–78, 1980), Rogers Satchell (The Annals of Applied Probability, 504–512, 1991) and Yang Zhang (The Journal of Business, 73 (3), 477–492, 2000) use a subset of all available extreme value ratios but not the full set. We examine if there are other extreme value ratios which contain more information than the most widely used ratios. This study shows empirically how much information is contained in various extreme value ratios of financial assets, using both real and simulated data. Using information theory, we find out their variability in relation to a uniform distribution in each quarter. We then rank them using the Kullback–Leibler metric (in accordance with a scoring methodology we developed in this study) to ascertain which set of ratios are more variable than others and thus may provide better estimation in computing volatility. We also calculate the rank of the matrix to identify the set of linearly independent ratios, for ascertaining the number of ratios that would be enough to generate a class of volatility estimators. The empirical results demonstrate that the need for incorporating other ratios in volatility estimation. We also observe that each dataset has other more informative ratios which are uniquely attributed to that dataset.

Suggested Citation

  • Parthajit Kayal & Sumanjay Dutta & Vipul Khandelwal & Rakesh Nigam, 2021. "Information Theoretic Ranking of Extreme Value Returns," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(1), pages 1-21, March.
    • Handle: RePEc:spr:jqecon:v:19:y:2021:i:1:d:10.1007_s40953-020-00214-y
    DOI: 10.1007/s40953-020-00214-y
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    Cited by:

    1. Thillaikkoothan Palanichamy & Parthajit Kayal, 2022. "Multiple Dimensions of Cyclicality in Investing," Working Papers 2022-216, Madras School of Economics,Chennai,India.
    2. Moinak Maiti & Parthajit Kayal, 2022. "Asymmetric Information Flow between Exchange Rate, Oil, and Gold: New Evidence from Transfer Entropy Approach," JRFM, MDPI, vol. 16(1), pages 1-14, December.

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    More about this item

    Keywords

    Extreme value estimators; Information theory; Volatility;
    All these keywords.

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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