Narrow Results

This paper aims at investigating issues of asset allocation and equity trading risk in the Gulf Cooperation Council (GCC) stock markets. The intent of this work is to bridge the gap in current asset market liquidity risk management methodologies and to assist GCC financial institutions in developing proactive asset market liquidity risk management techniques to assess potential market risks in light of the upshots of the current financial crisis. Using daily data of main market indicators for the period 2004–2009 and the Liquidity-Adjusted Value at Risk (L-VaR) model, the author finds that the distribution of the equity returns in the GCC stock markets is far from being normal and thus justifies using the L-VaR model, combined with other methods such as stress-testing, to incorporate the other remaining risks. Furthermore, the author shows that although there is a clear departure from normality, the asset market liquidity risk can be estimated without the need of complex mathematical and analytical procedures. To this end, several financial modeling strategies are achieved with the objective of creating a realistic framework of equity trading risk measurement in addition to the instigation of a practical iterative optimization technique for the calculation of maximum authorized L-VaR limits, subject to meaningful real-word operational constraints. Our modeling technique and empirical analysis have important implications for the GCC financial markets and can aid local financial institutions in developing advanced internal risk models and in complying with the requirements of the Basel II committee on capital adequacy.

I juxtapose Cover’s vaunted universal portfolio selection algorithm ([Cover, TM (1991). Universal portfolios. Mathematical Finance, 1, 1–29]) with the modern representation of a portfolio as a certain allocation of risk among the available assets, rather than a mere allocation of capital. Thus, I define a Universal Risk Budgeting scheme that weights each risk budget, instead of each capital budget, by its historical performance record, á la Cover. I prove that my scheme is mathematically equivalent to a novel type of [Cover, TM and E Ordentlich (1996). Universal portfolios with side information. IEEE Transactions on Information Theory, 42, 348–363] universal portfolio that uses a new family of prior densities that have hitherto not appeared in the literature on universal portfolio theory. I argue that my universal risk budget, so-defined, is a potentially more perspicuous and flexible type of universal portfolio; it allows the algorithmic trader to incorporate, with advantage, his prior knowledge or beliefs about the particular covariance structure of instantaneous asset returns. Say, if there is some dispersion in the volatilities of the available assets, then the uniform or Dirichlet priors that are standard in the literature will generate a dangerously lopsided prior distribution over the possible risk budgets. In the author’s opinion, the proposed “Garivaltis prior” makes for a nice improvement on Cover’s timeless expert system, that is properly agnostic and open to different risk budgets from the very get-go. Inspired by [Jamshidian, F (1992). Asymptotically optimal portfolios. Mathematical Finance, 2, 131–150], the universal risk budget is formulated as a new kind of exotic option in the continuous time Black–Scholes market, with all the pleasure, elegance, and convenience that entails.