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:

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.

Abstract: We introduce a multivariate GARCH-Copula model to describe joint dynamics of overnight and daytime returns for multiple assets. The conditional mean and variance of individual overnight and daytime returns depend on their previous realizations through a variant of GARCH specification, and two Student’s t copulas describe joint distributions of both returns respectively. We employ both constant and time-varying correlation matrices for the copulas and with the time-varying case the dependence structure of both returns depends on their previous dependence structures through a DCC specification. We estimate the model by a two-step procedure, where marginal distributions are estimated in the first step and copulas in the second. We apply our model to overnight and daytime returns of SPDR ETFs of nine major sectors and briefly illustrate its use in risk management and asset allocation. Our empirical results show higher mean, lower variance, fatter tails and lower correlations for overnight returns than daytime returns. Daytime returns are significantly negatively correlated with previous overnight returns. Moreover, daytime returns depend on previous overnight returns in both conditional variance and correlation matrix (through a DCC specification). Most of our empirical findings are consistent with the asymmetric information argument in the market microstructure literature. With respect to econometric modelling, our results show a DCC specification for correlation matrices of t copulas significantly improves the fit of data and enables the model to account for time-varying dependence structure.

L. Kang, S. Babbs. Modelling Overnight and Daytime Returns Using a Multivariate Garch-Copula Model

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 fi rst 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.

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.