There exists a well-developed statistical theory predicting extreme price values for financial markets known as extreme value theory (EVT). This approach relies on the seemingly obvious, but rarely analyzed, assumption that price displacement extremes actually exist for various markets. This paper attempts to describe the behavior of financial markets as a set of functions in terms of the dynamic variables price and time based on the net difference between ask and bid volumes over a unit period, thereby offering evidence to support the assumption that price extremes exist. Yet, it’s not meaningful to show merely that extremes exist. If the extreme negative price displacement simply represents a complete market collapse then the assumption becomes trivial. Accordingly, the paper also introduces a method to determine whether price displacements are constrained by non-trivial extremes. This description might have implications for EVT and market risk management in approximating the magnitude of “Black Swan” events. The paper also shows that if one can closely approximate the magnitude of such a rare event, one cannot also predict when the event will occur with any meaningful degree of certainty.
KeywordsExtreme Value TheoryStock ValuationRisk ManagementBlack SwansUncertainty
Fisher, R.A. and Tippett, L.H.C. (1928) Limiting Forms of the Frequency Distribution of the Largest and Smallest Member of a Sample. Proceedings of the Cambridge Philosophical Society, 24, 180-190. https://doi.org/10.1017/S0305004100015681
Gumbel, E.J. (1935) Les valeurs extrêmes des distributions statistiques. Annales de l’Institut Henri Poincaré, 5, 115-158.
Gnedenko, B.V. (1943) Sur la distribution limite du terme maximum d’une serie aleatoire. Annals of Mathematics, 44, 423-453. https://doi.org/10.2307/1968974
Broussard, J.P. and Booth, G.G. (1998) The Behavior of Extreme Values in Germany’s Stock Index Futures: An Application to Intradaily Margin Setting. European Journal of Operational Research, 104, 393-402. https://doi.org/10.1016/S0377-2217(97)00014-3
Davis, R. and Resnick, S. (1984) Tail Estimates Motivated by Extreme Value Theory. Annals of Statistics, 12, 1467-1487. https://doi.org/10.1214/aos/1176346804
Marohn, F. (1998) Testing the Gumbel Hypothesis via the Pot-Method. Extremes, 1, 191-213. https://doi.org/10.1023/A:1009910806693
Tiku, M.L. and Singh, M. (1981) Testing the Two Parameter Weibull Distribution. Communication in Statistics—Theory and Methods, 10, 907-918. https://doi.org/10.1080/03610928108828082
Poterba, J.M. and Summers, L.H. (1988) Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22, 27-59. https://doi.org/10.1016/0304-405X(88)90021-9
Kim, M.J., Nelson, C.R. and Startz, R. (1991) Mean Reversion in Stock Prices? A Reappraisal of the Empirical Evidence. Review of Economic Studies, 58, 515-528. https://doi.org/10.2307/2298009
Balvers, R., Wu, Y. and Gilliland, E. (2000) Mean Reversion across National Stock Markets and Parametric Contrarian Investment Strategies. Journal of Finance, 55, 745-772. https://doi.org/10.1111/0022-1082.00225
Akarim, Y.D. and Sevim, S. (2013) The Impact of Mean Reversion Model on Portfolio Investment Strategies: Empirical Evidence from Emerging Markets. Economic Modelling, 31, 453-459. https://doi.org/10.1016/j.econmod.2012.11.028
Caldararo, N. (2009) Primitive and Modern Economics: Derivatives, Liquidity, Value, Panic and Crises: A Uniformitarian View. Forum for Social Economics, 38, 31-51. https://doi.org/10.1007/s12143-008-9029-2
Kohara, K., Ishikawa, T., Fukuhara, Y. and Nakamura, Y. (1997) Stock Price Prediction Using Prior Knowledge and Neural Networks. Intelligent Systems in Accounting Finance & Management, 6, 11-22.
Jeon, S., Hong, B. and Chang, V. (2018) Pattern Graph Tracking-Based Stock Price Prediction Using Big Data. Future Generation Computer Systems, 80, 171-187. https://doi.org/10.1016/j.future.2017.02.010
Nair, B.B., Saravana Kumar, P.K., Sakthivel, N.R. and Vipin, U. (2017) Clustering Stock Price Time Series Data to Generate Stock Trading Recommendations: An Empirical Study. Expert Systems with Applications, 70, 20-36. https://doi.org/10.1016/j.eswa.2016.11.002
de Haan, L. (1976) Sample Extremes: An Elementary Introduction. Statistica Neerlandica, 30, 161-172. https://doi.org/10.1111/j.1467-9574.1976.tb00275.x
Weissman, I. (1978) Estimation of Parameters and Large Quantiles Based on the k Largest Observations. Journal of the American Statistical Association, 73, 812-815.
Naveau, P., Guillou, A., Cooley, D. and Diebolt, J. (2009) Modelling Pairwise Dependence of Maxima in Space. Biometrika, 96, 1-17. https://doi.org/10.1093/biomet/asp001
Culp, C.L. (2012) The “At-Risk” Metrics and Measures. In: Lane, M., Ed., Alternative (Re)Insurance Strategies, 2nd Edition, Risk Books, London, 359.
Embrechts, P., Resnick, S.I. and Samorodnitsky, G. (1999) Extreme Value Theory as a Risk Management Tool. North American Actuarial Journal, 3, 30-41. https://doi.org/10.1080/10920277.1999.10595797
Gilli, M. and Kellezi, E. (2006) An Application of Extreme Value Theory for Measuring Financial Risk. Computational Economics, 27, 207-228. https://doi.org/10.1007/s10614-006-9025-7
Singh, A.K., Allen, D.E. and Robert, P.J. (2013) Extreme Market Risk and Extreme Value Theory. Mathematics and Computers in Simulation, 94, 310-328. https://doi.org/10.1016/j.matcom.2012.05.010
Omari, C., Mwita, P. and Waititu, A. (2017) Using Conditional Extreme Value Theory to Estimate Value-at-Risk for Daily Currency Exchange Rates. Journal of Mathematical Finance, 7, 846-870. https://doi.org/10.4236/jmf.2017.74045
Bauwens, L. and Giot, P. (2001) Econometric Modelling of Stock Market Intraday Activity. Springer, Boston, 46-47.
Paté-Cornell, E. (2012) On “Black Swans” and “Perfect Storms”: Risk Analysis and Management When Statistics Are Not Enough. Risk Analysis, 32, 1823-1833. https://doi.org/10.1111/j.1539-6924.2011.01787.x
Hamilton, W.R. (1834) On a General Method in Dynamics. Philosophical Transactions of the Royal Society, Part II, 247-308.
Schmüdgen, K. (1990) Unbounded Operator Algebras and Representation Theory. Birkhäuser, Basel, 16.
Kasana, H.S. (2005) Complex Variables: Theory and Applications. 2nd Edition, Prentice Hall-India, New Delhi, 14.
Mann, S. and Haykin, S. (1995) The Chirplet Transform: Physical Considerations. IEEE Transactions on Signal Processing, 43, 2745-2761. https://doi.org/10.1109/78.482123
Kennard, E.H. (1927) Zur Quantenmechanik einfacher Bewegungstypen. Zeitschrift für Physik, 44, 326-352. (In German)
Weyl, H. (1928) Gruppentheorie und Quantenmechanik, Leipzig: Hirzel. Transl. by Robertson, H.P. (1931) The Theory of Groups and Quantum Mechanics. Dover.
Stein, E. and Shakarchi, R. (2003) Fourier Analysis: An Introduction. Princeton University Press, Princeton, 158.
Taylor, A.M. (2001) Potential Pitfalls for the Purchasing-Power-Parity Puzzle? Sampling and Specification Biases in Mean-Reversion Tests of the Law of One Price. Econometrica, 69, 473-498. https://doi.org/10.1111/1468-0262.00199