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Extreme Returns (extreme + return)
Selected AbstractsCauses and Consequences of the Relation Between Split-Adjusted Share Prices and Subsequent Stock ReturnsJOURNAL OF BUSINESS FINANCE & ACCOUNTING, Issue 1-2 2007William D. Brown Jr Abstract:, In this manuscript, we document and explain an empirical artifact , a persistent and substantial negative relation between split-adjusted share prices and subsequent stock returns , that has potentially important ramifications for capital markets research design. This relation pervades all commonly-used commercial databases and is insensitive to the choice of database used for either prices or returns. We investigate four potential causes of the empirical regularity: survivorship bias, asymmetric returns to low-priced stocks, extreme returns, and the effects of stock-split adjustments on portfolio classifications. We find that survivorship bias accounts for approximately half of the returns documented to a share-price-based hedge strategy and that re-classifications caused by stock split adjustments account for substantially all of the remaining returns. We do not find that controlling for either low-priced stocks or extreme returns is effective in purging the data of the empirical price artifact. These findings and our explanations thereof are important, as they show that there are potentially troublesome consequences of using share price as a deflator in markets-based research. In particular, we note and illustrate cause for concern when interpreting associations between share-price-scaled variables and subsequent returns as evidence of market inefficiency. [source] An Analysis of the Distribution of Extreme Share Returns in the UK from 1975 to 2000JOURNAL OF BUSINESS FINANCE & ACCOUNTING, Issue 5-6 2004G. D. Gettinby This paper seeks to characterise the distribution of extreme returns for a UK share index over the years 1975 to 2000. In particular, the suitability of the following distributions is investigated: Gumbel, Frechet, Weibull, Generalised Extreme Value, Generalised Pareto, Log-Normal and Generalised Logistic. Daily returns for the FT All Share index were obtained from Datastream, and the maxima and minima of these daily returns over a variety of selection intervals were calculated. Plots of summary statistics for the weekly maxima and minima on statistical distribution maps suggested that the best fitting distribution would be either the Generalised Extreme Value or the Generalised Logistic. The results from fitting each of these two distributions to extremes of a series of UK share returns support the conclusion that the Generalised Logistic distribution best fits the UK data for extremes over the period of the study. The Generalised Logistic distribution has fatter tails than either the log-normal or the Generalised Extreme Value distribution, hence this finding is of importance to investors who are concerned with assessing the risk of a portfolio. [source] KLSE Long Run Overreaction and the Chinese New-Year EffectJOURNAL OF BUSINESS FINANCE & ACCOUNTING, Issue 1-2 2001Zamri Ahmad This study investigates long run overreaction and seasonal effects for Malaysian stocks quoted on the Kuala Lumpur Stock Exchange (KLSE), for the period 1986,1996. Stocks exhibiting extreme returns relative to the market over a three year period experience a reversal of fortunes during the following three years. There is also evidence that employing a contrarian trading strategy may yield excess returns. Of particular interest is the apparent existence of a Chinese New Year effect in both the level of market returns, and the overreaction profile for KLSE stocks. These seasonalities mirror the January-effect observed in US markets. [source] A Generalized Extreme Value Approach to Financial Risk MeasurementJOURNAL OF MONEY, CREDIT AND BANKING, Issue 7 2007TURAN G. BALI financial risk management; value at risk; extreme value theory; skewed fat-tailed distributions This paper develops an unconditional and conditional extreme value approach to calculating value at risk (VaR), and shows that the maximum likely loss of financial institutions can be more accurately estimated using the statistical theory of extremes. The new approach is based on the distribution of extreme returns instead of the distribution of all returns and provides good predictions of catastrophic market risks. Both the in-sample and out-of-sample performance results indicate that the Box,Cox generalized extreme value distribution introduced in the paper performs surprisingly well in capturing both the rate of occurrence and the extent of extreme events in financial markets. The new approach yields more precise VaR estimates than the normal and skewed t distributions. [source] | ||||||||||