تخصیص دارایی کالا های بهینه با مدل سازی ریسک بازار منسجم

This paper fills a fundamental gap in commodity price risk management and optimal portfolio selection literatures by contributing a thorough reflection on trading risk modeling with a dynamic asset allocation process and under the supposition of illiquid and adverse market settings. This paper analyzes, from a portfolio managers' perspective, the performance of liquidity adjusted risk modeling in obtaining efficient and coherent investable commodity portfolios under normal and adverse market conditions. As such, the author argues that liquidity risk associated with the uncertainty of liquidating multiple commodity assets over given holding periods is a key factor in formalizing and measuring overall trading risk and is thus an important component to model, particularly in the wake of the repercussions of the recent 2008 financial crisis. To this end, this article proposes a practical technique for the quantification of liquidity trading risk for large portfolios that consist of multiple commodity assets and whereby the holding periods are adjusted according to the specific needs of each trading portfolio. Specifically, the paper proposes a robust technique to commodity optimal portfolio selection, in a liquidity-adjusted value-at-risk (L-VaR) framework, and particularly from the perspective of large portfolios that have both long and short positions or portfolios that consist of merely pure long trading positions. Moreover, in this paper, the author develops a portfolio selection model and an optimization-algorithm which allocates commodity assets by minimizing the L-VaR subject to applying credible operational and financial constraints based on fundamental asset management considerations. The empirical optimization results indicate that this alternate L-VaR technique can be regarded as a robust portfolio management tool and can have many uses and applications in real-world asset management practices and predominantly for fund managers with large commodity portfolios.

The significance of assessing the market risk of a portfolio of financial securities has long been acknowledged by academics and practitioners. In recent years, the growth of trading activities and instances of financial market upheavals have prompted new research underlining the necessity for market participants to develop reliable risk assessment methods. In measuring market risk, one technique advanced in the literature involves the use of value-at-risk (VaR) models (Hull, 2009 and Jorion, 2001) that ascertain how much the value of a trading portfolio would plunge, in monetary terms, over a given period of time with a given probability as a result of changes in market prices. Nowadays, VaR is by far the most popular and most accepted risk measure among financial institutions. Although VaR is a very popular measure of market risk of financial trading portfolios, it is not a panacea for all risk assessments (Sanders, 2002) and has several drawbacks, limitations and undesirable properties. Conversely, commodity price risk management has received less attention from researchers and that is why it is still in its infancy compared to the more developed equity, interest rate and foreign exchange markets (Bartram, 2005 and Weron, 2000). It is important to bear in mind, however, that commodity markets are not anywhere near as unambiguous as financial markets; hence few attempts have been made to measure price risk in commodity markets (Weron, 2000). Modeling market risk for commodity products thus presents an inherent complexity due to the strong interaction between the trading of products and the supply and demand imbalances that stem from the state of the economy (Al Janabi, 2009 and Giot and Laurent, 2003). As a result, the increase in tradability of commodities in emerging markets necessitates a reexamination of current commodity risk management techniques (Satyanarayan & Varangis, 1994); specifically for investment funds with large trading portfolios—of either merely pure long positions or a combination of long/short trading positions—and within short-to-medium horizons of re-balancing and reporting focuses. To address the above deficiencies, in this paper we characterize trading risk for diverse commodity products using a multivariate liquidity-adjusted value-at-risk (L-VaR) approach that focuses on the modeling of the optimum L-VaR under the notion of illiquid and adverse market conditions and by exercising different correlation factors and liquidation horizon periods. The overall objective of this paper is to construct a large commodity portfolio, which includes several crude oil/energy spot prices as well as other common commodities, and to evaluate the risk characteristics of such a portfolio besides examining an optimal process for assessing efficient and coherent1 market portfolios. To this end, we propose a general trading risk model that accounts for the characteristics of the series of commodity price returns—for example, fat tails (leptokurtosis), skewness, correlation factors, and liquidity horizons—and adequately forecasts the market risk within a short-to-medium-term time horizon. As such, our focus on a short-to-medium-term time horizon is coherent with the use of a pure risk management method in which a more fundamental economic model would be of little aid vis-à-vis short-to-medium-term risk estimations (Giot & Laurent, 2003). This study makes the following contributions to the literature in this specific commodity risk management field. First, it represents one of the limited numbers of academic/practitioner papers that empirically examines commodity trading risk management using actual data of different commodity markets. Second, unlike most empirical studies in this field, this study employs a comprehensive and real-world trading risk management model that considers risk analysis under normal, severe (crisis) and illiquid market conditions. The principal advantage of employing such a model is the ability to capture a full picture of possible loss scenarios of actual commodity trading portfolios. Third, given the fact that the classical (Markowitz, 1959) mean-variance optimizers have serious financial shortcomings, which could often lead to financially meaningless optimal portfolios (see for instance, Michaud, 1989), this paper proposes a new approach to optimal and coherent portfolio selection and within an L-VaR framework. The rationality behind introducing L-VaR 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 commodity trading portfolios on a daily basis according to prevailing market liquidity conditions. To this end, an L-VaR approach is introduced to allocate commodity assets by minimizing L-VaR 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 commodity portfolio; b) total trading volume of the investable portfolio; c) monetary asset allocation of each commodity asset class; d) portfolio managers' choices of pure long positions or a combination of long/short commodity trading positions; e) the unwinding or close-out liquidity horizons of each commodity asset-class. The focus on L-VaR as the appropriate measure of portfolio risk allows risk managers and portfolio managers to assign the desired liquidity horizon and to allocate pure long and/or a combination of long/short commodity assets according to realistic market trading conditions. 2 Another contribution of the paper is to provide a new approach in estimating portfolio managers' risk parameters. Accordingly, a robust optimization process is introduced to calculate risk tolerance in the L-VaR asset allocation model. Finally, the results of empirical testing are interesting in terms of theory as well as practical applications and provide an incentive for further research in the area of L-VaR and commodity price risk management, particularly in light of the aftermath of the latest 2008 financial crisis. Moreover, the different robust optimization studies and discussions are widely applicable to any commodity end-user, providing potential applications to practitioners and research ideas to academic scholars and researchers. In a nutshell, the proposed L-VaR risk-engine and optimization-algorithm have the potential of producing realistic risk-return profiles and could be a useful tool, for portfolio managers in many ways and applications, in developing enterprise-wide portfolio management models in the wake of the pshots of the most-recent financial crisis. This ultimately may improve real-world understanding of embedded risks and asymmetric market-microstructure patterns and could potentially create better investable coherent portfolios for fund managers.

One of the basic problems of applied finance is the optimal selection of assets, with the aim of maximizing future returns and constraining risk by appropriate measure. In this paper we examine how to determine the optimal portfolio choice for commodities portfolio managers under normal and adverse distributional assumptions and by implementing different long trading scenarios or a combination of long/short commodity trading strategies. We then provide a robust portfolio optimization technique using liquidity-adjusted value-at-risk (L-VaR) as a risk measure. In the final section of this paper, we describe the selection process for coherent investable commodity portfolios, of either merely long positions or a combination of long/short trading positions, and provide the composition of each investable commodity portfolio. This paper extends previous approaches to optimization problems with L-VaR constraints. In particular, the suggested technique can be used for minimizing L-VaR under several budget constraints that are drawn from realistic and meaningful financial investment considerations and applications. In this work, the optimization problem is formulated by finding a set of portfolios that minimize L-VaR subject to given expected returns and total portfolio volume. To this end, the L-VaR's risk-function is constrained by a downside risk measure in addition to meaningful financial and operational constraints, such as: total portfolio volume, long and short trading positions, commodity portfolio asset allocations, and the close-out liquidity horizons (unwinding periods). In essence, this approach can aid in solving some of the real-world trading dilemmas under adverse market conditions: when liquidity dries up; correlation factors switching signs; and the incorporation of non-normal distribution of asset returns in risk measurement. In addition, our L-VaR method has shown that portfolio managers can obtain financially meaningful coherent investable portfolios and demonstrated interesting market-microstructure patterns (e.g. the impact of close-out periods, overall trading volume, expected returns, etc., on the optimization-algorithm process) which cannot be attained by using the classical Markowitz mean-variance approach. The empirical analyses are provided using 25 distinct commodity assets. Several scenarios for the assessment of L-VaR are performed to demonstrate how the new optimization techniques can be implemented in real-world commodity markets. The empirical findings indicate that the proposed L-VaR technique performs adequately over the entire sample period (1987–2007) and across alternative investment horizons. The results indicate that portfolio managers' L-VaR depends on the minimum expected return, individual L-VaRs of each commodity asset, liquidity horizons (close-out periods) of each commodity asset, and the asset allocations of investable portfolios. The empirical results can have several practical applications and could aid in overcoming some of the shortcomings of VaR and the classical mean-variance approach, especially in light of the aftermath of the recent financial crisis. Likewise, the different robust optimization studies and analyses are widely applicable to any commodity end-user, offering potential applications to practitioners and research ideas to academics. In a nutshell, our demonstration of the usefulness of L-VaR as a potential portfolio management tool is interesting in terms of theory and practical application; and we believe it can offer concrete tools to commodities portfolio managers in many diverse ways and uses. We believe that our L-VaR technique could be a better tool for dealing with market price gap and illiquidity issues and can have many different uses and applications in real-life asset management practices, particularly for portfolio managers with very large commodity portfolios. Finally, we can stress that our modeling and optimization process of distinctive commodity portfolios with the aid of an L-VaR algorithm is quite beneficial for practical applications and it can produce realistic risk-return profiles. This ultimately may improve real-world understanding of embedded risks and asymmetric microstructure patterns and could potentially create better investable portfolios for fund managers.