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THE IMPLICATIONS OF HERDING ON VOLATILITY. THE CASE OF THE SPANISH STOCK MARKET

THE IMPLICATIONS OF HERDING ON VOLATILITY. THE CASE OF THE SPANISH STOCK MARKET. Natividad Blasco* Pilar Corredor # Sandra Ferreruela* * Dept. of Accounting and Finance. (University of Zaragoza) # Dept. of Business Management (Public University of Navarre). 1.- Introduction.

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THE IMPLICATIONS OF HERDING ON VOLATILITY. THE CASE OF THE SPANISH STOCK MARKET

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  1. THE IMPLICATIONS OF HERDING ON VOLATILITY. THE CASE OF THE SPANISH STOCK MARKET Natividad Blasco* Pilar Corredor # Sandra Ferreruela* * Dept. of Accounting and Finance. (University of Zaragoza) # Dept. of Business Management (Public University of Navarre) X Workshop on Quantitative Finance Milan, 29th January 2009

  2. 1.- Introduction • ACCURATE VOLATILITY MEASUREMENT is the basis of pricing models, investment and risk management strategies. • If MARKET IS EFFICIENT prices instantaneously adjust to new information Then volatility is only caused by the adjustment of stock prices to new information • But… X Workshop on Quantitative Finance Milan, 29th January 2009

  3. 1.- Introduction • …THERE IS EVIDENCE of price adjustments that are due not to the arrival of new information but to market conditions or collective phenomena such as herding • Herding is said to be present in a market when investors opt to imitate the trading decisions of those who they consider to be better informed, rather than acting upon their own beliefs and information. X Workshop on Quantitative Finance Milan, 29th January 2009

  4. 1.- Introduction • The link between investor behavior and market volatility was first noted by Friedman (1953): • Irrational investor destabilize prices, rational investors move prices towards their fundamentals. • Hellwig (1980) and Wang (1993) • Volatility is driven by uninformed or liquidity trading • Froot et al (1992), Avramov et al (2006) • Investors imitate each other and this drives volatility • Following these authors we… X Workshop on Quantitative Finance Milan, 29th January 2009

  5. 1.- Introduction • …we set out to assess the effect of different levels of herding intensity on the degree of market volatility. • This study contributes to provide an explanation for that portion of volatility not due to changes on fundamentals or other know effects. • Results could prove highly relevant in achieving a better understanding of market functioning and serve both academics and practitioners. X Workshop on Quantitative Finance Milan, 29th January 2009

  6. 2.- Dataset • Sample period: Jan 1st 1997 to Dec 31st 2003 • Data supplied by Spanish Sociedad de Bolsas SA • Intraday data include: date, exact time, stock code, price and volume traded of ALL TRADES • Ibex-35 data include: composition of the index, volume traded and number of trades, daily opening, closing, maximum and minimun prices, 15-minute price data. X Workshop on Quantitative Finance Milan, 29th January 2009

  7. 2.- Dataset • Database provided by MEFF (the official Spanish futures and options market) including date of trade, underlying of the contract (Ibex-35), expiration date, exercise price and volatility at the close of trading. X Workshop on Quantitative Finance Milan, 29th January 2009

  8. 3.- Herding intensity measure • Measure proposed by Patterson and Sharma (2006) based on the information cascade models of Bikhchandani, Hirshleifer and Welch (1992). • Major advantages: • Constructed from intraday data. • It considers the market as whole rather than only a few institutional investors. X Workshop on Quantitative Finance Milan, 29th January 2009

  9. 3.- Herding intensity measure • An information cascade will be observed when buyer initiated or seller initiated runs last longer than would be expected if no such cascade existed S can take three different values (Ha Hb Hc) X Workshop on Quantitative Finance Milan, 29th January 2009

  10. 3.- Herding intensity measure • On average herding intensity is significantly negative accross all types of run. X Workshop on Quantitative Finance Milan, 29th January 2009

  11. 3.- Volatility measures • Absolute return residuals are obtained from the following regression: • Rit is the index return i on day t, four values: • AA from opening on day t to opening on day t+1, • AC from opening to closing on day t, • CC from closing on day t-1 to closing on day t, • CA from closing on day t to opening on day t+1 X Workshop on Quantitative Finance Milan, 29th January 2009

  12. 3.- Volatility measures • Realized volatility.Andersen et al (2001): • By summing up the squares of intraday returns calculated from high frequency data we obtain an accurate volatility estimator. X Workshop on Quantitative Finance Milan, 29th January 2009

  13. 3.- Volatility measures • Historic volatility. • Parkinson (1980) • and Garman and Klass (1980) X Workshop on Quantitative Finance Milan, 29th January 2009

  14. 3.- Volatility measures • Implied volatility. Result from the inversion of the Black Scholes option pricing model. • Fleming(1998), Christensen and Prabhala (1998), Corredor and Santamaría (2001, 2004) show that implied volatility is a reliable predictor of future volatility. • We focus on the implied volatility of short term ATM call options on the Ibex-35. X Workshop on Quantitative Finance Milan, 29th January 2009

  15. 3.- Correlation X Workshop on Quantitative Finance Milan, 29th January 2009

  16. 4.- Volatility and herding • Having obtained the volatility measures the second stage is to purge them of the volume and day of the week effects documented in the literature. • We run a series of regressions where the described volatility measures are made to depend on the Monday effect and a proxy for the volume (V, NT, ATS) and corrected for autocorrelation. • We take the residuals of the regressions. X Workshop on Quantitative Finance Milan, 29th January 2009

  17. 4.- Volatility and herding X Workshop on Quantitative Finance Milan, 29th January 2009

  18. 4.- Volatility and herding • Having obtained the “CLEAN” volatility series we determine the extent of the linear effect of herding on volatility on day t. • We run linear regressions where the residuals of prior regressions depend on a constant and PS(2006) herding intensity measure. X Workshop on Quantitative Finance Milan, 29th January 2009

  19. 4.- Volatility and herding • Since there is no reason why the relationships between variables need to be exclusively linear, we test for possible non-linear causality between the different measures of volatility and the herding level. • Using the procedure described in Hiemstra and Jones (1994), we find no evidence at all of non-linear causality in the results. X Workshop on Quantitative Finance Milan, 29th January 2009

  20.   Ha Hb Hc Ha Hb Hc Ha Hb Hc | eAA | -0.0003 -0.0003 -0.0006 -0.0001 -0.0001 -0.0004 -0.0007 -0.0006 -0.0010 | e AC | -0.0004 -0.0004 -0.0008 -0.0003 -0.0003 -0.0007 -0.0009 -0.0008 -0.0013 | e CC | -0.0005 -0.0004 -0.0010 -0.0005 -0.0004 -0.0014 -0.0010 -0.0008 -0.0015 | e CA | -0.0004 -0.0002 -0.0008 -0.0003 -0.0003 -0.0007 -0.0008 -0.0008 -0.0012 -0.0002 -0.0001 -0.0002 -0.0001 0.0000 -0.0001 -0.0005 -0.0003 -0.0005 -0.0003 -0.0002 -0.0005 -0.0002 -0.0001 -0.0004 -0.0006 -0.0005 -0.0008 -0.0003 -0.0002 -0.0005 -0.0003 -0.0001 -0.0004 -0.0007 -0.0005 -0.0008 -0.0003 -0.0001 -0.0003 -0.0002 -0.0000 -0.0002 -0.0006 -0.0004 -0.0006 Implied -0.0001 0.0000 -0.0000 -0.0000 0.0001 0.0001 -0.0001 0.0000 -0.0000 5.-Results X Workshop on Quantitative Finance Milan, 29th January 2009

  21. 5.- Results • Overall, all three types of herding have a significantly negative effect on all the volatility measures except implied volatility. • The results for the measures of historical and realized volatility are very similar, irrespective of which volume proxy is used, and also unanimous. • a higher level of herding (uninformed trading) leads to greater price changes (volatility) • Herding affects current volatility but not expected volatility X Workshop on Quantitative Finance Milan, 29th January 2009

  22. 5.- Results • Our results are partially consistent with prior literature. • The short-term implied volatility provide new information that has not be presented in former studies. • Our study contributes to the robustness and novelty of the herding literature through the number of volatility measures and types of volume considered and the explicit use of a measure of intraday herding. X Workshop on Quantitative Finance Milan, 29th January 2009

  23. 6.-Conclusions • This paper examines the way in which market volatility is affected by the presence of herding behavior. • The results presented are consistent with prior literature: • the higher the observed level of herding intensity, the greater volatility we can expect to find. • This does not apply in the case of implied volatility X Workshop on Quantitative Finance Milan, 29th January 2009

  24. 6.-Conclusions • Herding affects current market volatility, but has no impact on expected future volatility • The overall results of this paper may be useful for interpreting the concept of risk and for defining risk management strategies. • The choice of a specific type of volatility measure may be relevant since estimates calculated with stock market data are “contaminated” by herding X Workshop on Quantitative Finance Milan, 29th January 2009

  25. Thanks X Workshop on Quantitative Finance Milan, 29th January 2009

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