Fat-Tailed and Skewed Asset Return Distributions : Implications for Risk Management, Portfolio Selection, and Option Pricing

By Svetlozar T. Rachev, Christian Menn, Frank J Fabozzi 3. 2005

263 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/71017ef2066b09a8016fc40b261e66b90452c2f9

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Citations in the corpus

Financial market models with Lévy processes and time-varying volatility. (2008, background): “Then P1 and P2 are equivalent if and only if the parameters satisfy the condition (21).”
The inefficiency of Bitcoin revisited: A high-frequency analysis with alternative currencies (2019, background): “It is widely known that asset returns exhibit volatility clustering most of the time (Rachev et al., 2005).”
Quantitative models for operational risk: Extremes, dependence and aggregation (2006, background): “For strong support on and consequences of the power-tail behavior in finance, see for instance Mandelbrot (1997) and Rachev et al. (2005).”
Tempered stable distributions and processes in finance: numerical analysis (2010, background): “[18], see also references therein, have focused attention on a general framework for market and credit risk management, option pricing, and portfolio selection based on the α-stable distribution.”
t-Statistic Based Correlation and Heterogeneity Robust Inference (2010, methodology): “The considered tests are the t−statistic approach with groups defined by individuals (“t−statistic“); OLS coefficient based tests with Rogers (1993) standard errors (“clustered“); and OLS coefficient based test which includes individual Fixed Effects and Arellano (1987) standard errors (“clustered, ”
Portfolio optimization for heavy-tailed assets: Extreme Risk Index vs. Markowitz (2015, background): “Several modifications addressing this issue have been discussed; see, e.g., Rachev et al. (2005) for the relevance of this type of heavy-tailed models.”
Portfolio diversification and value at risk under thick-tailedness (2009, background): “It was documented in numerous studies that the time series encountered in many fields in economics and finance are heavy-tailed (see the discussion in Loretan and Phillips 1994, Gabaix et al. 2003 and Rachev et al. 2005 and references therein).”
Rank − 1 / 2: A Simple Way to Improve the OLS Estimation of Tail Exponents (2007, unknown): “…Littauer Center, 1805 Cambridge St., Cambridge, MA 02138 (ribragim@fas.harvard.edu) Despite the availability of more sophisticated methods, a popular way to estimate a Pareto exponent is still to run an OLS regression: log(Rank) = a − b log(Size), and take b as an estimate of the Pareto exponent.”