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연속신념시스템의 확장모형을 이용한 주식시장의 군집행동 분석

The extension of a continuous beliefs system and analyzing herd behavior in stock markets

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한국은행|2011.06|경제분석 (Economic Analysis)|제17권 제2호|pp. 27-55|DOI 10.23299/bokeri.2011.17.2.002

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참고문헌

키워드

글로벌 금융위기Global Financial Crisis군집행동마코프 체인 몬테칼로(MCMC) 알고리즘Time-varying Herding Parameter연속신념시스템시변 군집행동 파라미터Continuous Beliefs SystemHerd behaviorMarkov chain Monte Carlo(MCMC) algorithm
주제분류경제학

초록

최근 금융시장의 변동성이 현저하게 증폭되면서 이에 대한 원인으로 금융시장의 군집행동에 대한 이론적 연구가 활발하게 진행되고 있지만 군집행동의 시계열적 특성을 파악할 수 있는 실증적 연구는 거의 없었다. 따라서 본 연구는 연속신념시스템 (continuous beliefs system)의 이론적 확장을 통해 군집행동을 시계열적으로 측정할 수 있는 군집행동 파라미터를 도출하였으며 이를 추정하기 위한 계량모형을 제안하였다. 또한 이 모형의 효율적 추정을 위해 MCMC 추정법을 적용하였다. KOSPI와 DOW 지수 월별자료를 이용한 실증분석 결과에 의하면 미국보다 우리나라 주식시장의 군집행동이, 그리고 글로벌 금융위기 전보다 글로벌 금융위기 이후에 군집행동이 강하게 나타났다. 또한 글로벌 금융위기로 인해 군집행동의 변동성(표준편차)이 증가하였으며 군집행동은 수익률 변동성과는 달리 지속적인 자기상관을 유지하지 않았다. 이런 결과는 군집행동이 금융시장을 불안하게 만드는 한 원인이 될 수 있음을 나타낸다.

Abstract

Although many theoretical studies have tried to explain the volatility in financial markets using models of herd behavior, there have been few empirical studies on dynamic herding due to the technical difficulty of detecting herd behavior with time-series data. Thus, this paper theoretically extends a continuous beliefs system belonging to an agent based economic model by introducing a term representing agents’mutual dependence into each agent’s utility function and derives a SV(stochastic volatility)-type econometric model. From this model the time-varying herding parameters are efficiently estimated by a Markov chain Monte Carlo method. Using monthly data of KOSPI and DOW, this paper provides some empirical evidences for stronger herding in the Korean stock market than in the U.S. stock market, and further stronger herding after the global financial crisis than before it. More interesting finding is that time-varying herd behavior has weak autocorrelation and the global financial crisis may increase its volatility significantly.

참고문헌 (37)

  1. Asai, M. M. McAeer, "Multivariate stochastic volatility: A review", Econometric Reviews, 25, pp.145~175 (2006)
  2. Avery, C, "Multidimensional uncertainty and herd behavior in financial markets", American Economic Review, 88, pp.724~48 (1998)
  3. Banerjee, A. V, "A simple model of herd behavior", Quarterly Journal of Economics, 107, pp.797~817 (1992)
  4. Berg, A, "Deviance information criterion for comparing stochastic volatility models", Journal of Business and Economic Statistics, 22, pp.107~120 (2004)
  5. Bikhchandani, S, "A theory of fads, fashion, custom, and cultural change in informational cascades", Journal of Political Economy, 100, pp.992~1026 (1992)
  6. Agency and asset prices, "Brennan, M", Finance Working Paper, UCLA, 6~93 (1993)
  7. Brock, W. A, "A rational route to randomness", aEconometrica, 65, pp.1059~1096 (1997)
  8. Brock, W. A, "A dynamic structural model for stock return volatility and trading volume", Review of Economics and Statistics, 78, pp.94~110 (1996)
  9. Brock W. A., "Evolutionary dynamics in markets with many trader types", Journal of Mathematical Economics, 41, pp.7~42 (2005)
  10. Broto, C, "Estimation methods for stochastic volatility models: A survey", Journal of Economic Surveys, 18, pp.613~649 (2004)
  11. Chang, E, "An examination of herd behavior in equity markets: An international perspective", Journal of Banking and Finance, 24, pp.1651~1679 (2000)
  12. Chib, S, "Markov chain Monte Carlo methods: Computation and inference In Handbook of Econometrics 5", pp.3569~3649 (2001), Elsevier, Amsterdam
  13. Chib, S, "Markov chain Monte Carlo methods for stochastic volatility models", Journal of Econometrics, 108, pp.281~316 (2002)
  14. Christie, W, "Following the pied piper: Do individual returns herd around the market", Financial Analysts Journal, 51, pp.31~37 (1995)
  15. Cipriani, M, "Herd behavior in a laboratory financial market", American Economic Review, 95, pp.1427~1443 (2005)
  16. Cont, R, "Herd behavior and aggregate fluctuations in financial markets", Journal of Macroeconomic Dynamics, 4, pp.170~195 (2000)
  17. Heterogeneity as a natural source of randomness in Technical Report. CeNDEF working paper 03-05, University of Amsterdam, "Diks, C", Tinbergen Institute Discussion Paper, ~, pp.2003-073 (2003), 1.
  18. Diks, C, "Herding, a-synchronous updating and heterogeneity in memory in a CBS", Journal of Economic Dynamics and Control, 29, pp.741~763 (2005)
  19. Eguīluz, V. M, "Transmission of information and herd behavior: An application to financial markes", Physical Review Letters, 85, pp.5659~5662 (2000)
  20. Gelfand, A, "Sampling-based approaches to calculating marginal densities", Journal of the American Statistical Association, 85, pp.398~409 (1990)
  21. Graham, J. R., "Herding among investment newsletters: Theory and evidence", Journal of Finance, 54, pp.237~268 (1999)
  22. Hommes, C, "Financial markets as nonlinear adaptive evolutionary systems", Quantitative Finance, 1, pp.149~167 (2001)
  23. Hwang, S, "Market stress and herding", Journal of Empirical Finance, 11, pp.585~616 (2004)
  24. Jacquier, E., "Bayesian analysis of stochastic volatility models (with discussion)", Journal of Business and Economic Statistics, 12, pp.371~417 (1994)
  25. Johannes, M, "MCMC methods for continuous-time financial econometrics in Handbook of Financial Econometrics 2", pp.1~72 (2009), Elsevier
  26. Kim, S, "Stochastic volatility: Likelihood inference and comparison with ARCH models", Review of Economic Studies, 65, pp.361~393 (1998)
  27. Kim, K, "Herd behaviors in the stock and foreign exchange markets", Physica A, 341, pp.526~532 (2004)
  28. Lux, T, "Scaling and criticality in a stochastic multi-agent model of a financial market", Nature, 397, pp.498~500 (1999)
  29. Herd behavior and volatility in financial markets, "Park, B.-J", The 3rd International Conference on Asia-Pacific Financial Markets(CAFM), ~ (2008)
  30. Scharfstein, D. C, "Herd behavior and investment", American Economic Review, 80, pp.465~479 (1990)
  31. Shephard, N, "Likelihood analysis of non-Gaussian measurement time series", Biometrika, 84, pp.653~667 (1997)
  32. Stauffer, D, "Monte Carlo simulation of volatility clustering in market model with herding", International Journal of Theoretical and Applied Finance, 2, pp.83~94 (1999)
  33. Stauffer, D, "Self-organized percolation model for stock market fluctuations", Physica A, 271, pp.496~506 (1999)
  34. Summers, L. H, "Does the stock market rationally reflect fundamental values", Journal of Finance, 41, pp.591~601 (1986)
  35. Taylor, S. J, "Modeling Financial Time Series" (1986), John Wiley
  36. Tierney, L, "Markov chains for exploring posterior distributions", The Annals of Statistics, 22, pp.1701~1728 (1994)
  37. Yu, J, "Multivariate stochastic volatility models: Bayesian estimation and model comparison", Econometric Reviews, 25, pp.361~384 (2006)

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