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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 l.jpg

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


1 introduction l.jpg
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


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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


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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


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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


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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


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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


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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


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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


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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


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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


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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


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3.- Volatility measures

  • Historic volatility.

    • Parkinson (1980)

    • and Garman and Klass (1980)

X Workshop on Quantitative Finance

Milan, 29th January 2009


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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


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3.- Correlation

X Workshop on Quantitative Finance

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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


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4.- Volatility and herding

X Workshop on Quantitative Finance

Milan, 29th January 2009


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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.

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Milan, 29th January 2009


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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


5 results l.jpg

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

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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


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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


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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


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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


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Thanks

X Workshop on Quantitative Finance

Milan, 29th January 2009


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