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The Threshold Effect in Expected Volatility: A Model Based on Asymmetric Information

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  • Longin, Francois M
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    Abstract

    This article develops theoretical insight into the threshold effect in expected volatility, which means that large shocks are less persistent in volatility than small shocks. The model uses the Kyle-Admati-Pfleiderer setup with liquidity traders, informed traders, and a market maker. Information is modeled as a GARCH process. It is shown that the GARCH process for information is transformed into a TARCH process (for "threshold GARCH") for the market price changes. Working with information flows allows one to derive implications for trading volume and market liquidity which provide the basis for a more complete test of the model. Article published by Oxford University Press on behalf of the Society for Financial Studies in its journal, The Review of Financial Studies.

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    Bibliographic Info

    Article provided by Society for Financial Studies in its journal Review of Financial Studies.

    Volume (Year): 10 (1997)
    Issue (Month): 3 ()
    Pages: 837-69

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    Handle: RePEc:oup:rfinst:v:10:y:1997:i:3:p:837-69

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    Cited by:
    1. Frankel, David M., 2007. "Adaptive Expectations and Stock Market Crashes," Staff General Research Papers 12817, Iowa State University, Department of Economics.
    2. Longin, François, 1999. "From Value at Risk to Stress Testing: The Extreme Value Approach," CEPR Discussion Papers 2161, C.E.P.R. Discussion Papers.
    3. Longin, Francois M., 2000. "From value at risk to stress testing: The extreme value approach," Journal of Banking & Finance, Elsevier, vol. 24(7), pages 1097-1130, July.
    4. Hsin, Chin-Wen & Guo, Wen-Chung & Tseng, Seng-Su & Luo, Wen-Chih, 2003. "The impact of speculative trading on stock return volatility: the evidence from Taiwan," Global Finance Journal, Elsevier, vol. 14(3), pages 243-270, December.
    5. ERIC HILLEBRAND & MArcelo Cunha Medeiros, 2010. "Asymmetries, breaks, and long-range dependence: An estimation framework for daily realized volatility," Textos para discussão 578, Department of Economics PUC-Rio (Brazil).
    6. S. Bordignon & D. Raggi, 2010. "Long memory and nonlinearities in realized volatility: a Markov switching approach," Working Papers 694, Dipartimento Scienze Economiche, Universita' di Bologna.
    7. Chordia, Tarun & Sarkar, Asani & Subrahmanyam, Avanidhar, 2005. "The Joint Dynamics of Liquidity, Returns, and Volatility Across Small and Large Firms," University of California at Los Angeles, Anderson Graduate School of Management qt6z81z2wc, Anderson Graduate School of Management, UCLA.
    8. Rajesh Chakrabarti & Barry Scholnick, 2002. "Exchange rate expectations and foreign direct investment flows," Review of World Economics (Weltwirtschaftliches Archiv), Springer, vol. 138(1), pages 1-21, March.
    9. Jayasuriya, Shamila, 2005. "Stock market liberalization and volatility in the presence of favorable market characteristics and institutions," Emerging Markets Review, Elsevier, vol. 6(2), pages 170-191, June.
    10. Masahiro Watanabe, 2003. "A Model of Stochastic Liquidity," Yale School of Management Working Papers ysm385, Yale School of Management.
    11. Martin Martens & Dick van Dijk & Michiel de Pooter, 2004. "Modeling and Forecasting S&P 500 Volatility: Long Memory, Structural Breaks and Nonlinearity," Tinbergen Institute Discussion Papers 04-067/4, Tinbergen Institute.
    12. Adam Clements & Scott White, 2005. "Non-linear filtering with state dependant transition probabilities: A threshold (size effect) SV model," School of Economics and Finance Discussion Papers and Working Papers Series 191, School of Economics and Finance, Queensland University of Technology.

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