Herd behavior towards the market index: evidence from Romanian stock exchange

Pop, Raluca Elena (2012): Herd behavior towards the market index: evidence from Romanian stock exchange.

Abstract

This paper uses the cross-sectional variance of the betas from the CAPM model to study herd behavior towards market index in Romania. For time-varying beta determination, three different modeling techniques are employed: two bivariate GARCH models (DCC and FIDCC GARCH), two Kalman filter based approaches and two bivariate stochastic volatility models. A comparison of the different models’ in-sample performance indicates that the mean reverting process in connection with the Kalman filter and the stochastic volatility model with a t distribution for the excess return shocks are the preferred models to describe the time-varying behavior of stocks betas. Through the estimated values, the evolution of the herding measure, especially the pattern around the beginning of the subprime crisis is examined. Herding towards the market shows significant movements and persistence independently from and given market conditions and macro factors. Contrary to the common belief, the subprime crisis reduces herding and is clearly identified as a turning point in herding behavior.

• Item Type: MPRA Paper
• Language: English
• Keywords: Herd Behavior, CAPM, GARCH Models, Stochastic Volatility Models, Kalman Filter
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models; G - Financial Economics > G0 - General > G02 - Behavioral Finance: Underlying Principles; G - Financial Economics > G1 - General Financial Markets > G11 - Portfolio Choice ; Investment Decisions

References

Admanti, A. and P. Pfleiderer (1997), “Does it all add up? Benchmarks and the compensation of active portfolio managers”, Journal of Business, 70, 323-350

Altăr, M. (2002), “Teoria portofoliului”, DOFIN, Academy of Economic Studies Bucharest

Andersen, T. G., H. J. Chung and B. E. Sorensen (1999), “Efficient method of moments estimation of a stochastic volatility model: A Monte Carlo study”, Journal of Econometrics, 91, 61-87

Asai, M., M. McAleer and J. Yu (2006), “Multivariate stochastic volatility: a review”, Econometric Reviews, 25, 145-175

Avery, C. and P. Zemsky (1998), “Multidimensional Uncertainty and Herd Behavior in Financial Markets”, American Economic Review, 88, 724-748

Baillie, R., T. Bollerslev and H. Mikkelsen (1996), “Fractionally integrated generalized autoregressive conditional heteroskedasticity”, Journal of Econometrics, 74, 3-30

Banerjee, A. (1992), “A Simple Model of Herd Behavior”, Quarterly Journal of Economics, 107, 787—818

Bauwens, L., S. Laurent and J. Rombouts (2006), “Multivariate GARCH models: a survey”, Journal of Applied Econometrics, 21, 79-109

Bikhchandani, S., D. Hirshleifer and I. Welch (1992), “A Theory of Fads, Fashion, Custom and Cultural Change As Informational Cascades”, Journal of Political Economy, 100, 992—1027

Bikhchandani, S. and S. Sharma (2001), “Herd Behavior in Financial Markets: A Review”, IMF working paper

Bollerslev, T. (1986), “Generalized Autoregressive Conditional Heteroscedasticity”, Journal of Econometrics, 31, 307-327

Bossche, F. (2011), “Fitting State Space models with Eviews”, Journal of statistical Software, 41, 1- 16

Brooks, R.D., R. W. Faff and J.H. Lee (1994), “Beta Stability and Portfolio Formation”, Papers 94-3, Melbourne - Centre in Finance Brooks, R.D., R. W. Faff and M. McKenzie (2002), “Time-varying country risk: an assessment of alternative modeling techniques”, The European Journal of Finance, 8:3, 249-274

Butler, K. C., W. C. Gerken and K. Okada (2011), “A Test for Long Memory in the Conditional Correlation of Bivariate Returns to Stock and Bond Market Index Futures”, working paper, Social Science and Research Network (SSRN)

Calvo, G. and E. Mendoza (1998), “Rational herd behavior and globalization of securities markets”, University of Maryland

Campenhout, G.V. and J. F. Verhestraeten (2010), “Herding behavior among financial analysts: a literature review”, HUB research paper

Caporin, M. (2002), “FIGARCH models: stationarity, estimation methods and the identification problem”, working paper, Foscari University, Venice

Casella, G. and E. George (1992), “Explaining the Gibbs sampler”, The American Statistician, 46, 167-174

Chang, E., J. W. Cheng and A. Khorana (2000), “An examination of herd behavior in equity markets: An international perspective”, Journal of Banking and Finance, 24, pp.1651–1679.

Chang, C.Y., X. Y. Qian and S. Y. Jian (2012), “Bayesian estimation and comparison of MGARCH and MSV models via WinBUGS”, Journal of Statistical Computation and Simulation, 82:1, 19-39

Chari, V.V. and P. Kehoe (1999), “Financial Crises as Herds”, Federal Reserve Bank of Minneapolis

Choudhry, T. and H. Wu (2007), “Forecasting the weekly time-varying beta of UK firms: comparison between GARCH models vs Kalman filter method”, working paper, University of Southampton

Christie, W. and R. Huang (1995), “Following the pied piper: do individual returns herd around the market”, Financial Analysts Journal, 51, 31– 37

Chung, C. F. (2001), “Estimating the Fractionally Integrated GARCH Model”, National Taiwan University discussion paper

Cipriani, M. and A. Guarino (2005), “Herd Behavior in a Laboratory Financial Market”, American Economic Review, 95, 1427—1443

Cipriani, M. and Guarino, G. (2008), “Herd Behavior in Financial Markets: An Experiment with Financial Market Professionals”, Journal of the European Economic Association, 7, 206—233

Cipriani, M. and A. Guarino (2010), “Estimating a structural model of herd behavior in financial markets”, IMF working paper

Demirer, R. and A. M. Kutan (2006), “Does herding behavior exist in Chinese stock markets?”, Journal of International Financial Markets, Institutions and Money, 16, 123–142.

Devenow, A. and I. Welch (1996), “Rational herding in financial economics”, European Economic Review, 40, 603-615

Engle, R. F. (1982), “Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation”, Econometrica, 50, 987-1007

Engle, R.F. (2002), “Dynamic conditional correlation—a simple class of multivariate GARCH models”, Journal of Business and Economic Statistics, 20, 339–350

Engle, R.F. and K. Sheppard (2001), “Theoretical and empirical properties of dynamic conditional correlation multivariate GARCH”, mimeo, UCSD

Fabozzi, F. J. and J. C. Francis (1978), “Beta as a random coefficient”, Journal of Financial and Quantitative Analysis, 13 (1), 101-116

Gamerman, D. and H. Lopes (2006), “Markov Chain Monte Carlo”, Statistical Science, 2nd edition, Chapman & Hall, New York.

Graham, J. (1999), “Herding among investment newsletters: theory and evidence”, Journal of Finance, 54, 237-268

Grinblatt, M., S. Titman and R. Wermers (1995), “Momentum investment strategies, portfolio performance, and herding: A study of mutual fund behavior”, American Economic Review, 85, 1088–1105

Hachicha, N. (2010), “New sight of herding behavioral through trading volume”, Economics ejournal, no. 2010-11

Halbleib, R. and V. Voev (2010), “Modelling and forecasting multivariate realized volatility”, Journal of Applied Econometrics, 26, 922-947

Hamilton, J. (1994), “Time Series Analysis”, Princeton University Press, Princeton

Harvey, A. C., E. Ruiz and N. Shepard (1994), “Multivariate stochastic variance models”, Review of Economic Studies, 61, 247-264

Hirshliefer, D. (2001), “Investor psychology and asset pricing”, Journal of Finance, 56, 1533-1597

Hirshleifer, D., and S. H. Teoh (2003), “Herding and cascading in capital markets: a review and synthesis”, European Financial Management, 9, 25– 66.

Hwang, S. and M. Salmon (2001), “A New Measure of Herding and Empirical Evidence”, CUBS Financial Econometrics Working Paper No WP01-3, Cass Business School

Hwang, S. and M. Salmon (2004), “Market Stress and Herding”, Journal of Empirical Finance, 11, 585-616

Hwang, S. and M. Salmon (2008), “Sentiment and beta herding”, working paper, University of Warwick

Jacquier, E., N.G. Polson and P.E. Rossi (1994), “Bayesian analysis of stochastic volatility models”, Journal of Business and Economics Statistics, 12, 371-389

Johansson, A. (2009), “Stochastic volatility and time-varying beta country risk in emerging markets”, The European Journal of Finance, 15:3, 337-363

Khan, H., S. Hassairi and J. L. Viviani (2011), “Herd behavior and market stress: The case of four European countries”, International Business Research, vol.4, 53-67

Lakonishok, J., A. Shleifer and W. Vishny (1992), “The impact of institutional trading on stock prices”, Journal of Financial Economics, 32, 23–44

Lunn, D.J., A. Thomas, N. Best, and D. Spiegelhalter (2000), “WinBUGS -- a Bayesian modelling framework: concepts, structure, and extensibility”, Statistics and Computing, 10, 325--337.

Maug, E. and N. Naik (1996), “Herding and delegated portfolio management”, London Business School

Melino, A. and S. M. Turnbull (1990), “Pricing foreign currency options with stochastic volatility”, Journal of Econometrics 45, 239-265

Mergner, S. and J. Bulla (2005), “Time-varying beta risk of Pan-European industry portfolios: a comparison of alternative modeling techniques”, Econpaper

Meyer, R. and J. Yu (2000), “BUGS for a Bayesian analysis of stochastic volatility models”, Econometrics Journal, 3, 198-215

Meyer, R. and J. Yu (2006), “Multivariate stochastic volatility models: Bayesian estimation and model comparison”, Econometrics Reviews, 25, 361-384

Nelson, D. and C. Cao (1992), “Inequality constraints in the univariate GARCH model”, Journal of Business and Economic Statistics, 10, 229-235

Ntzoufras, I. (2009), “Bayesian Modeling Using WinBUGS”, John Wiley & Sons, Inc., Publication, New Jersey

Peters, T. (2008), “Forecasting the covariance matrix with the DCC GARCH model”, Mathematical Statistics Stockholm University dissertation paper

Scharfstein, D. J. and Stein (1990), “Herd behavior and investment“, American Economic Review, 80, 465-479

Schwert, G. W. (1989), “Why Does Stock Market Volatility Change Over Time?”, The Journal of Finance, 44, 1115-1153

Sharpe, W. F. (1964), “Capital assets prices: A theory of market equilibrium under conditions of risk”, Journal of Finance, 19, 425-442

Sima, I. (2007), “Stylized facts and discrete stochastic volatility models”, Econpaper

Spiegelhalter, D. J., N. G. Best, B. P. Carlin, and A. van der Linde (2002), “Bayesian measures of model complexity and fit”, Journal of the Royal Statistical Society, Series B, 64, Part 3

Tan, L., T. C. Chiang, J. R. Mason and E. Nelling (2008), “Herding behavior in Chinese stock markets: An examination of A and B shares”, Pacific-Basin Finance Journal, 16, 61–77

Taylor, S. J. (1982), “Modeling Financial Time Series”, New York, Wiley

Wang, D. (2008), “Herd behavior towards the market index: Evidence from 21 financial markets”, working paper, University of Navarra

Welch, I. (1992), “Sequential sales, learning and cascades”, Journal of Finance, 47, 695—732

Wermers, R. (1995), “Herding, trade reversals and cascading by institutional investors”, University of Colorado, Boulder

Wermers, R. (1999), “Mutual fund herding and the impact on stock prices”, Journal of Finance, 54, 581-622