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Leverage Effect and Volatility Asymmetry

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Current Issues in the Economy and Finance of India (ICEF 2018 2018)

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Abstract

The leverage effect , the relationship between asset volatility and returns is generally examined at contemporaneous or inter-temporal level. Instead, this paper examines the leverage effect over a period by classifying days into positive-return and negative-return days. We also examine the volatility asymmetry in leverage effect by decomposing the volatility into up and down volatilities. This paper makes use of extreme value estimators to examine 14 indices from different Emerging economies and 10 indices from developed economies. We document that the evidence of a negative relationship between volatility and returns is more prevalent in developed markets. This study also observes a dominance of down volatility over up volatility during negative-return days.

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Notes

  1. 1.

    Our definition of Good Days and Bad Days are based on daily opening and closing data hence does not consider intra-day changes in the price movements.

  2. 2.

    See Bekaert and Wu (2000) for an overview of the literature on asymmetric volatility .

  3. 3.

    GARCH family of models was initially proposed by Engle (1982) and Bollerslev (1986) and from then it has been extensively used in literature with major improvements.

  4. 4.

    SV models were first advocated by Taylor (1986).

  5. 5.

    We started with 23 emerging economies (listed in the Morgan Stanley Capital International (MSCI) emerging markets Index) but many of these countries do not have open, high and low data prior to 2010. Our estimation process requires OHLC data at the daily level. Therefore, we have excluded those countries from our sample.

  6. 6.

    The definition of Good Days and Bad Days consider only those days when opening prices and closing prices are not same. The RS and YZ estimate for the full sample will thus ignore those days when opening and closing prices are same (in the case of no trade or all trades occur at the same price). This kind of issue is there when a stock or asset is not liquid enough and generally seen to happen for small stocks. Our study doesn’t deal with individual stocks. Despite that, we still find instances where limited number of days in our data with either all the OHLC prices is same or opening and closing prices are same due to errors done by data sources. During the cleaning of data, we have deleted those days from our samples.

  7. 7.

    We also calculate overnight volatilities and close-to-close volatilities separately for Good and Bad Days for all the assets. We do not find any significant differences.

  8. 8.

    Mean of DRS and DYZ generated through 10,000 bootstrap simulation is the almost same as actual estimate. Therefore, we have presented only the actual numbers in Tables 9.3 and 9.4 for brevity.

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Author information

Authors and Affiliations

  1. Madras School of Economics, Gandhi Mandapam Road, Behind Government Data Center, Kottur, Chennai, 600025, Tamil Nadu, India

    Parthajit Kayal

  2. Institute for Financial Management and Research, 24, Kothari Road, Nungambakkam, Chennai, 600034, Tamil Nadu, India

    S. Maheswaran

Authors
  1. Parthajit Kayal
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  2. S. Maheswaran
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Correspondence to Parthajit Kayal .

Editor information

Editors and Affiliations

  1. Department of Economics, Birla Institute of Technology and Science (BITS), Pilani, Goa, India

    Aswini Kumar Mishra

  2. Trulaske College of Business, School of Accountancy, University of Missouri, Columbia, MO, USA

    Vairam Arunachalam

  3. Department of Economics, Birla Institute of Technology and Science (BITS), Pilani, Goa, India

    Debasis Patnaik

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Kayal, P., Maheswaran, S. (2018). Leverage Effect and Volatility Asymmetry. In: Mishra, A., Arunachalam, V., Patnaik, D. (eds) Current Issues in the Economy and Finance of India. ICEF 2018 2018. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-99555-7_9

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