Instead of posting papers separately, I’ve decided to transition to a weekly reading list format. I’ll update this post over the course of the week, but here’s the initial list:
- Gelpern, Anna and Gulati, G. Mitu, Sovereign Snake Oil. Law and Contemporary Problems, Forthcoming.
- Chan, Kam Fong and Marsden, Alastair D.E., Empirical Tests on the Credit Default Swap and Stock Markets During the Global Credit Crisis.
- Friedman, Craig and Zhang, Yangyong, Estimating Flexible, Fat-Tailed Conditional Asset Return Distributions.
- Han, Bing and Zhou, Yi, Term Structure of Credit Default Swap Spreads and Cross-Section of Stock Returns.
I received an email from Quantitative Finance informing me that my paper with A. Duran,
A Profitable Trading and Risk Management Strategy Despite Transaction Cost, will be freely available online in a “virtual issue” of the journal on risk. This issue is designed to coincide with the RiskMinds 2010 conference currently taking place. Please access the published version of my paper from InformaWorld here or the entire Risk issue here through the end of the month.
Here’s one of those papers that you’d always meant to write. In this case, I think I even suggested it on the blog once – if you have to use some parametric VaR/ES method, why not replace the 2-moment normal characterization of return with its generalization, the 4-moment Johnson characterization?
Abstract: The Cornish-Fisher and Gram-Charlier expansions are tools often used to compute value at risk (VaR) in the context of skewed and leptokurtic return distributions. These approximations use the first four moments of the unknown target distribution to compute approximate quantile and distribution functions. A drawback of these approaches is the limited set of skewness and kurtosis pairs for which valid approximations are possible. We examine an alternative to these approaches with the use of the Johnson (1949) system of distributions which also uses the first four moments as main inputs but is capable of accommodating all possible skewness and kurtosis pairs. Formulas for the expected shortfall are derived. The performance of the Cornish-Fisher, Gram-Charlier and Johnson approaches for computing value at risk and expected shortfall are compared and documented. The results reveal that the Johnson approach yields smaller approximation errors than the Cornish-Fisher and Gram-Charlier approaches when used with exact or estimated moments.
J.-G. Simonato. The performance of Johnson distributions for computing value at risk and expected shortfall. http://ssrn.com/abstract=1706409.
Readers might be interested in an article that A. Duran and I have published in Quantitative Finance this year entitled A Profitable Trading and Risk Management Strategy Despite Transaction Cost. In the article, a number of the tools I’ve presented on the blog here have been used in the development of strategy which outperforms the S&P500 in rigorous out-of-sample testing. We’ve made sure to check the robustness of the results, and have performed Monte Carlo simulations while varying the sets of stocks and time periods used in the calculation. Here’s the abstract and a sample figure:
We present a new profitable trading and risk management strategy with transaction cost for an adaptive equally weighted portfolio. Moreover, we implement a rule-based expert system for the daily financial decision making process by using the power of spectral analysis. We use several key components such as principal component analysis, partitioning, memory in stock markets, percentile for relative standing, the first four normalized central moments, learning algorithm, switching among several investments positions consisting of short stock market, long stock market and money market with real risk-free rates. We find that it is possible to beat the proxy for equity market without short selling for S&P 500-listed 168 stocks during the 1998-2008 period and Russell 2000-listed 213 stocks during the 1995-2007 period. Our Monte Carlo simulation over both the various set of stocks and the interval of time confirms our findings.
You can download the paper either from SSRN or Quantitative Finance.