Evidence of Information Asymmetry and Herding Behaviour – The Swiss Franc Unpegging Event in Perspective
DOI:
https://doi.org/10.12725/ujbm.52.4Keywords:
Herding , Unpegging, Heavy Tailed eventAbstract
The paper aims to find the impact of financial events that occurred in one country on another. Taking the case of the Swiss Franc Unpegging of 2015 in Switzerland, the paper observes its impact on the Indian economy. This is done by studying the information asymmetry and herding behaviour in Indian market on the day of the event. The study uses two sets of data, (i) high frequency data and (ii) 3 years index data of both countries. The Ganger Causality test has been conducted to study the cause and effect relationship between the economies, which helps determine the impact on any of the countries. The study found that herding behaviour and information asymmetry in Indian market are now linked to each other in such a way that the country is affected even if the event has not occurred in the economy itself, however, only for a short duration of time. There also seems to be a huge gap between information available amongst all investors.
References
Acharya, V. V. A., & Yorulmazer, T. (2019). Information contagion and bank herding. Journal of Money, 40(1), 215–231.
Bachelier, L. (1900). Th’eorie de La Sp’ Eculation. Annales Scientifiques de l’École Normale Supérieure, 17, 21–86.
Belke, A., & Setzer, R. (2004). Contagion, herding and exchange-rate instability- A survey. Intereconomics, 222–228.
Bensaïda, A. (2017). Herding effect on idiosyncratic volatility in U. S. industries. Finance Research Letters, 3(1), 1–21.
Botta, F., Moat, H. S., Stanley, H. E., & Preis, T. (2015). Quantifying stock return distributions in financial markets. PLOS ON, 10(9), 1–10.
Dungey, M., & Tambakis, D. (2003). International financial contagion: What do we know ? Mardi Dungey & Demosthenes Tambakis Comments, 9, 1–23.
Evangelista, J., & Segovia, T. (2017). Introducing Hurst exponent in pair trading. Physica A: Statistical Mechanics and Its Applications, 1–14.
Fama, E. F. (2008). The behavior of stock-market prices. The Journal of Business, 38(1), 34–105.
Farmer, D., & Geanakoplos, J. (2011). Power Laws in economics and finance. SSRN Electronic Journal, 1–57.
Gabaix, X. (2016). Power laws in economics: An introduction. Journal of Economic Perspectives, 30 (1), 185–206.
Ghorbel, A., Boujelbene, M. A., & Boujelbene, Y. (2014). Behavioral explanation of contagion between oil and stock markets. International Journal of Energy Sector Management, 8(1), 121–144.
Ghosh, B., & Kozarevic, E. (2018). Identifying explosive behavioral trace in the CNX Nifty Index: A quantum finance approach. Investment Management and Financial Innovations, 15(1), 209–223.
Ghosh, B., & Kozarevic, E. (2019). Multifractal analysis of volatility for detection of herding and bubble: Evidence from CNX Nifty HFT. Investment Management and Financial Innovations, 16(3), 182–193.
Ghosh, B., & Krishna, M. C. (2019). Power law in tails of bourse volatility- Evidence from India. Investment Management and Financial Innovations, 16(1), 291–298.
Ghosh, B., Mc, K., Kozarevic, E., & Pandey, R. K. (2018). Predictability and herding of bourse volatility: An econophysics analogue. Investment Management and Financial Innovations, 15(2), 317–326.
Ghosh, B., & Nisha, N. (2018). Comparative volatility study on India VIX by Garch and Shannon entropy from a behavioural finance viewpoint. International Journal of Applied Business and Economic Research, 16(2), 317–324.
Gibrat, R. (1931). Les Inégalités Économiques; Ap- Plications: Aux Inégalités Des Richesses, à La Con- Centration Des Entreprises, Aux Populations Des Villes, Aux Statistiques Des Familles, Etc., d’une Loi Nouvelle, La Loi de l’effet Proportionnel. Libraire Du Recueil Sirey. Paris.
Gneiting, T., & Schlather, M. (2001). Stochastic models which separate fractal dimension and Hurst effect. SIAM review, 1–8.
Hausdorff, F. (1918). Dimension and ausseres Mass. Mathematische Annalen, 79(1–2), 157–179.
Hill, B. M. (1975). A simple general approach to inference about the tail of a distribution. The Annals of Statistics, 3(5), 1163–1174.
Hurst. (1951). H.E.Long-term storage capacity of reservoirs. Trans. Am. Soc. Civ. Eng., 116, 770–799.
Ihlen, E. A. F. (2012). Introduction to multifractal detrended fluctuation analysis in Matlab. Methods Article, 3(June), 1–18.
Jansen, D. W., & Vries, C. G. de. (1991). On the frequency of Large Stock Returns: putting Booms and Busts into Perspective. The Review of Economics and Statistics, 73(1), 18–24.
Jiang, Z., Xie, W., Zhou, W., & Sornette, D. (2018). Multifractal analysis of financial markets Multifractal analysis of financial markets.
Kantelhardt, J. W., Zschiegner, S. A., Koscielny-bunde, E., Bunde, A., Havlin, S., & Stanley, H. E. (2002). Multifractal Detrended Fluctuation Analysis of Nonstationary Time Series. Physica A: Statistical Mechanics and Its Applications, 316, 1–4.
Kim, M., & Kim, M. (2014). Group-Wise Herding Behavior in Financial Markets : An Agent-Based Modeling Approach. PLOS One, 9(4), 1–7.
Kumar, A., Bharti, M., & Bansal, S. (2018). An examination of herding behavior in an emerging economy – A study of Indian stock market. Global Journal of Management and Business Research: B Economics and Commerce, 16(5), 58–64. Kyle, A. S., & Obizhaeva, A. A. (2019). Large Bets and Stock Market Crashes. (AFA 2013 San Diego Meetings Paper).
Linders, D., Dhaene, J., & Schoutens, W. (2015). Option prices and model-free measurement of implied herd behavior in stock markets. International Journal of Financial Engineering,02(02), 150012.
Mandelbrot, B. B. (1970). Comment on “Stochastic Models in Hydrology.” Water Resour. Res., 6, 1791.
Mandelbrott, B. (2009). The Variation of Certain Speculative Prices. The Journal of Business, 36(4), 394–419.
Min, H., & Hwang, Y. (2012). Dynamic correlation analysis of US financial crisis and contagion : evidence from four OECD countries. Applied Financial Economics, 22, 2063–2074.
Nirei, M., Stachurski, J., & Stachurski, J. (2018). Herding and Power Laws in Financial Markets. CARF - F-434.
Ormos, M., & Imotity, D. (2016). Market Microstructure During Financial Crisis: Dynamics of Informed and Heuristic-Driven Trading. Finance Research Letters, 1–15.
Parkinson, M. (1980). The Extreme Value Method for Estimating the Variance of the Rate of Return. The Journal of Business, 53(1), 31–65.
Redelico, F. O., & Proto, A. N. (2012). Empirical fractal geometry analysis of some speculative financial bubbles. Physica A, 391(21), 5132–5138.
Riedi, R. H. (1999). Introduction to Multifractals. A Paradigm: The Binomial Measure, 1-23.
Watkins, N. W., & Franzke, C. (2017). A brief history of long memory : Hurst, Mandelbrot and the road to ARFIMA, 1951-1980. Entropy, 19(437), 1–21.
Wu, L. (2006). Dampened power law: Reconciling the tail behavior of financial security returns. The Journal of Business, 79(3), 1445–1473.
Zhang, C., & Huang, L. (2010). A quantum model for the stock market. Physica A: Statistical Mechanics and Its Applications, 389(24), 5769–5775.
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