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- W3146616393 abstract "This paper examines rescaled range analysis for detecting persistence, autocorrelation and mean reversion for the S&P 500 in highly volatile markets. Closely associated with rescaled range analysis, R/S, is the Hurst exponent H, known as the index of long-range dependence. A Random function X(t) is generalized by fractional Brownian motion, (fBm). Changing H=1/2 to 0 < H <1, 1/H is the tail index parameter, alpha, α for a power-law distribution with heavy tails. 1/H has implications for volatility skew reflected in the VIX. When α is < 2, variance for power-law distributions is undefined. Power-law probability distributions are tested for VaR returns. I used the Yahoo finance database, and mathwave EasyFit for goodness-of-fit tests to determine the theoretical distribution that best fit the empirical distribution. The Hill tail estimator tested the 1/H statistic as a predictor of the tail index. Statistically, the Pareto 2 distribution seemed a reasonable approximation for the S&P 500 left tail. VaR and hedging strategies can be determined using the Hurst exponent. I examined probabilities of extreme losses in S&P 500 returns that exceeded predetermined threshold loss of 0.5%." @default.
- W3146616393 created "2021-04-13" @default.
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- W3146616393 date "2018-01-01" @default.
- W3146616393 modified "2023-09-25" @default.
- W3146616393 title "A Hedged Portfolio Risk Manager's Observations and Practical Applications" @default.
- W3146616393 doi "https://doi.org/10.2139/ssrn.3255805" @default.
- W3146616393 hasPublicationYear "2018" @default.
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