پرتفوی بهینه و قابل سرمایه گذاری : تجزیه و تحلیل تجربی با سناریو الگوریتم های بهینه سازی تحت چشم انداز بازار بحران

This paper develops scenario optimization algorithms for the assessment of investable financial portfolios under crisis market outlooks. To this end, this research study examines from portfolio managers' standpoint the performance of optimum and investable portfolios subject to applying meaningful financial and operational constraints as a result of a financial turmoil. Specifically, the paper tests a number of alternative scenarios considering both long-only and long and short-sales positions subject to minimizing the Liquidity-Adjusted Value-at-Risk (LVaR) and various financial and operational constraints such as target expected return, portfolio trading volume, close-out periods and portfolio weights. Robust optimization algorithms to set coherent asset allocations for investment management industries in emerging markets and particularly in Gulf Cooperation Council (GCC) financial markets are developed. The results show that the obtained investable portfolios lie off the efficient frontier, but that long-only portfolios appear to lie much closer to the frontier than portfolios including both long and short-sales positions. The proposed optimization algorithms can be useful in developing enterprise-wide portfolio management models in light of the aftermaths of the most-recent financial crisis. The developed methodology and risk optimization algorithms can aid in advancing portfolio management practices in emerging markets and predominantly in the wake of the latest credit crunch.

As a result of the recent financial market shocks, capital-market corporations, institutional investors and portfolio managers are reconsidering specific issues and focusing on: 1) How to not only incorporate risk and reward tradeoffs using modern portfolio theory, but also plan for unexpected market shocks; and 2) the resulting effects of these shocks on the asset management business and its impact on asset allocation and the construction of robust investable portfolios. To this end, prominent financial institutions are linking their downside portfolio risk with the return on capital and integrating market liquidity risk into their assessments in an effort to obtain better understanding of embedded-risk and expected return. As a result, optimization of the capital deployed–rather than just a single view of risk exposures–and its application for optimizing the asset allocation structures of investable market portfolios has become the new role of risk management.1 While common risk technique such as Value-at-Risk (VaR) and probability of default are still employed, they fail to anticipate systemic changes in the structure of financial markets. These techniques assume that volatility of the market and correlations among assets change slowly or not at all; they are not designed to handle systemic negative changes caused by jumps in the availability of liquidity or jumps in market values (Scholes and Kimner, 2010). One other critique that can be leveled against the VaR method is that it does not explicitly consider portfolios' asset liquidity risk during the unwinding (close-out) period. In fact, typical VaR models assess the worst change in the mark-to-market portfolio value over a given time horizon but do not account for the actual risk of liquidation. Indeed, neglecting asset liquidity risk can lead to an underestimation of the overall market risk and misapplication of capital cushion for the safety and soundness of financial institutions (Al Janabi, 2011a and Al Janabi, 2011b). In this backdrop and to address the above shortcomings, the goals and challenges in this paper are to develop robust scenario optimization-algorithms for the assessment of investable financial portfolios under crisis market prospects. To this end, this paper examines from portfolio managers' perspective the performance of investable structured portfolios within a Liquidity-Adjusted Value-at-Risk (LVaR) framework, subject to the application of meaningful operational and financial constraints, particularly in the wake of the aftermaths of most-recent global financial crisis.2 The rationality behind introducing LVaR as an effective portfolio management tool is because it complies with real-life trading situations, where traders can liquidate (or re-balance) small portions of their portfolios on a daily basis according to prevailing market liquidity conditions. To this end, an LVaR approach is introduced to allocate financial assets by minimizing LVaR subject to enforcing meaningful operational and financial constraints that are based on fundamental asset management considerations and practices, such as: a) the target expected return of the investable portfolio; b) total trading volume of the investable portfolio; c) monetary asset allocation of each asset class; d) portfolio managers' choices of pure long positions or a combination of long/short trading positions; and e) the unwinding or close-out liquidity horizons of each asset-class. In a nutshell, the primary motivation of this research is to set advanced portfolio management optimization techniques (drawn from rational and meaningful financial investment considerations) that can be applied to investable portfolios in emerging markets, such as in the context of the Gulf Cooperation Council (GCC) stock markets. As such, this research study and the obtained empirical results can contribute to the existing body of knowledge and extend current optimization-techniques' literatures related to the assessment of investable financial portfolios. Specifically this paper provides generalized scenario optimization-algorithm foundation that is theoretically appealing while capturing the essential aspects of optimal and investable financial assets and risk-capital allocations under difficult and unfavorable market circumstances. Essentially, the proposed scenario optimization-algorithms can be useful in developing enterprise-wide portfolio management models that financial entities may consider in assessing coherent risk-capital allocations and can offer practical tools to portfolio managers. As such, the portfolio modeling techniques and the achieved empirical results can have many uses and applications for portfolio managers and can have relevant practical implications that will benefit several end-users, such as: institutional investors, portfolio managers, mutual-fund industry and other financial institutions in the GCC region as well as other emerging financial markets. The remainder of the paper is organized as follows. The following section discusses relevant literatures reviews and highlights specific objectives of this paper. This is followed by Section 3 in which the quantitative infrastructure of a non-linear dynamic risk-function and robust scenario optimization-algorithms are described. In Section 4 we analyze and interpret empirical results and discuss the simulation results of optimal and investable portfolios. Summary and concluding remarks are drawn in the final section. Full set of empirical testing and simulation results of optimal and investable portfolios are included in Appendix A.

This paper develops scenario optimization algorithms by which investable financial portfolios during crisis periods can be evaluated. Specifically, the paper examines a number of alternative scenarios considering both long-only and long and short positions subject to minimizing the Liquidity-Adjusted Value-at-Risk (LVaR) and various financial and operational constraints such as target expected return, portfolio trading volume, close-out periods and portfolio weights. The results show that the obtained investable portfolios lie off the efficient frontier, but that long-only portfolios appear to lie much closer to the frontier than portfolios including both long and short positions. More specifically, in this research paper a non-linear risk-function and robust optimization technique for the assessment of illiquidity of pure long trading position is incorporated. To this end, the liquidity technique that is applied in this work is more suitable for real-world trading and asset management practices since it considers selling small fractions of the trading assets on a daily basis. Furthermore, this liquidity model can be implemented for the entire portfolio or for each individual security within the equity trading portfolio. Indeed, the developed methodology and risk assessment algorithms can aid in advancing portfolio management risk management practices in emerging markets and above all in the wake of the latest credit crunch and the succeeding financial upheavals. The empirical results for the GCC zone confirm that in almost all tests, there are strong asymmetric characteristics in the distribution of daily returns of the selected stock market indices. The appealing result of this research study endorses the necessity of combining LVaR estimations with other techniques such as stress-testing (under crisis market circumstances) and scenario analysis (as a consequence of a financial crunch) to obtain systematic descriptions of other remaining risks (such as, fat-tails in the probability distribution) that cannot be disclosed with the simple assumption of normality. Indeed, our suggested modeling technique is in-line with the recommendation of previous studies (see, for example, Neftci, 2000). In essence, a number of authors have argued that many asset distributions have “fat tails” and that the assumption of normal distribution can definitely underestimate the risk of extreme losses. As such, our proposed portfolio risk-engine is a combination of a closed-form parametric LVaR along with a stress-testing approach that is based on historical simulation of real severe upheavals in the GCC markets. As a matter of fact, our empirical results indicate generally that under crisis market conditions the actual obtained investable portfolios can expect to realize greater downside-risk of approximately 6.0 times more than the results under normal market conditions. In addition, our proposed risk-algorithm has shown that portfolio managers can obtain financially meaningful investable portfolios and demonstrated interesting market-microstructures' patterns (e.g. the impact of close-out periods, overall trading volume, and expected returns, on the optimization-algorithm process) which cannot be attained by using the classical Markowitz's mean–variance approach. In view of that, the obtained investable benchmark portfolios are noticeably located far away from the optimal frontiers and particularly for combinations of long and short-sales positions. While, it seems that this optimization phenomenon could not be attained for long and short-sales trading position, however for long-only trading positions it seems that it is possible to get closer to the optimal frontier and synchronize to a certain degree the performance of both optimal and investable portfolios. For this reason, the empirically obtained benchmark investable portfolios of pure long positions, under the notion of a crisis event, are noticeably located near the optimal frontier, nevertheless not on the efficient frontier as the classical mean–variance theory might suggest. This is due to the fact that financial and operational real-world investment considerations make it unlikely that an investable portfolio will behave exactly as theory predicts. Imperfections such as restriction on long trading positions, total trading volume and liquidation horizons make it unlikely to create an optimal equity portfolio. As such, the portfolio manager should apply active strategies in order to earn excess returns and particularly under adverse market perceptions. Thus, it seems that incorporating the liquidity holding periods and other operational constraints into the constrained optimization function, portfolio managers could employ active trading and investment strategies so as to capture additional returns that could be, one way or another, quite different of what the classical mean–variance efficient frontier indicates.13 In a nutshell, the obtained empirical results can have several practical applications and could aid in overcoming some of the shortcomings of conventional VaR and the classical mean–variance approach, especially in light of the aftermaths of the latest financial crunch. In conclusion, the presented methodology and empirical results of this research paper can have important practical uses and applications for financial institutions, risk managers, portfolio managers, financial regulators and policymakers operating in the GCC and other emerging markets, and particularly in the wake of the most recent financial crisis. For instance, the proposed modeling technique and simulation algorithms can be used by risk managers and portfolio managers for the assessment of appropriate asset allocations of different structured investable portfolios. Finally, we believe that the proposed risk-engine and optimization-algorithm have the potential of producing realistic risk-return profiles and may improve real-world understanding of embedded risks and asymmetric microstructure patterns and could potentially create better investable portfolios for portfolio managers in the GCC zone and other developing economies.