International Diversification Assignment Help

Risks in International Diversification Assignment Help: Although government representatives play a key role in regulating global financial markets, financial (e.g., exchange rate), economic, and political risk factors continue to impact international capital markets. For this Activity, evaluate and compare three (3) optimization methods and models (e.g., portfolio) aimed to reduce the risks associated with international diversification. Provide detailed examples within your paper.


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Risks in International Diversification:   Introduction Many investors invest in different sectors to reduce the risk by diversifying across industries, asset categories, or nations. There is correlation amongst international asset prices that affect the diversification opportunities for the global investors and enhance the risk probability for the investment. In concern of this, the paper evaluates and compares different portfolio optimization methods that are used to reduce the risks related to international diversification.


Evaluation of Risk Reducing Methods for International Diversification: Government representatives or authorities always try to reduce the risks like currency exchange rate, political risk, market risk, etc. associated with global diversification in order to regulate global investment market. They implement different methods and models to perform this task effectively. The Mean-Absolute Deviation (MAD) model, Markowitz’s mean-variance model (MVM) and Minimax (MM) Modelcan be used to reduce these risks in international market (Satchell & Scowcroft, 2003). Following is the evaluation and comparison of models:


Markowitz’s Mean-Variance Model: Markowitz’s model is used to build the relationship between mean returns and variance of the returns to find a minimum variance point in the feasible region. It was proposed by Markowitz, along with Sharpe and Miller. Moreover, it gives idea about selection of suitable portfolios by implementing weighting criteria. Markowitz model may consist of n assets. This model focuses on the weight of assets that will minimize the portfolio variance at a level of required rate of return. In addition, this model is a quadratic programming model and popular due to its simplicity. Its portfolio represents the number of assets (Farias, Vieira & Santos, 2006). If you looking for international business case study assignment help services then you can stop here. It consists of two statistics, which are mean and variance. In this model, it is easier to build the efficient frontier with the combination of return and risk. It is assumed that the returns of assets are multivariate normally distributed or investor’s utility function is quadratic.


This model evaluates the tradeoff between investment returns and their related risks. It determines the highest expected return providing portfolio on the given risk level (Fabozzi, 2008). On the other hand, there are various drawbacks of using this model. It is very difficult to calculate covariance of assets to build this model. Moreover, in large scale problems, it is not suitable because of difficulty in solving quadratic programming model. Therefore, this model cannot be used for large scale portfolio optimization problems because it is very difficult to solve a quadratic model with a complex covariance matrix (Jobst, Horniman, Lucas & Mitra, 2001). Perception of Investor against risk is not symmetric around the mean. It gives feasible solution of having many stocks in small amounts. This leads large transaction costs to the investors. Furthermore, this method can be very useful in a single time period investment decision. In order to overcome the drawbacks of this model, alternative models such as Mean-Absolute Deviation, Minimax and Lower Partial Moment (LPM) are used to minimize the risk of international diversification (Farias, Vieira & Santos, 2006).


Mean-Absolute Deviation (MAD) Model: Mean-Absolute Deviation (MAD) model was proposed by Konno and Yamazaki. It can be said equivalent to Markowitz model in the presence of multivariate normal distribution of the assets returns. It incorporates all the positive aspects of the Markowitz model while overcoming the drawbacks of Markowitz’s mean-variance model in terms of calculation effort and feasibility of solution implementation (Hassan, Siew & Shen, 2012). In this method, there is no requirement to calculate the covariance matrix. Furthermore, it is a linear program. It is easier to solve a linear program and faster than solving a quadratic program. In this model, optimal portfolio consists of at most 2T + 2 assets in spite of the size, n. T represents the time periods of the investment. Additionally, it minimizes the difference between average forecasted rate of return and the expected rate of return for the given investment. This model is regularly used by the researchers because it keeps some of the theoretical concepts of the MV model in its practices, which is significant for further analysis and calculations.


It also includes fewer assets that charge fewer transaction costs for the investors (Hassan, Siew & Shen, 2012). MAD can solve a large scale portfolio optimization problem. In addition, to ignore the covariance matrix in calculation can cause greater estimation risk. There is no difference between positive deviations and negative deviations. However, investors prefer higher positive deviations and avoid lower negative deviations in portfolio return. It has also some limitations. It implements a deterministic approach that has no probabilities and associated rate of return scenarios that are account for uncertain nature of future returns (Farias, Vieira & Santos, 2006).


Additionally, it assumes that asset returns do not change with time but it does not include mid-term rebalancing. With this, it has been converted in advanced form of a new stochastic MAD (SMAD) model. This new model retains the advantages of the deterministic MAD model in terms of calculation efficiency and minimal security selection. In addition, it incorporates multiple potential return scenarios and provides timely guidance on portfolio re-balancing to ensure the achievement of the investment goal (Hassan, Siew & Shen, 2012). So, it can be said that it is advanced form of MAD model that overcomes it’s all limitations in effective manner.


Minimax Model: It was proposed by Young by using minimum return as a measurement of risk. It is equivalent to MV model, if the assets returns are multivariate normally distributed. It is also a linear programming model. It emphasizes on maximization of expected return exceed minimum level of return. This model is more advantageous if returns are non-normally distributed. Therefore, it can be used in hedge funds, in which returns are non-normally distributed. It is appropriate for investors, who have a strong downside risk aversion. It is also suitable for a more complex investment portfolio that includes fixed transaction costs constraints. Its objective is to minimize the maximum loss. It is sensitive to outliers in the historical data. Its main disadvantage is that it cannot be used in condition of lacking the historical data on the past returns (Farias, Vieira & Santos, 2006).


As per business assignment help experts, this model is based on game theory and very useful in studies of portfolio optimization. According to this theory, two or more players know the goals and strategies of opponents and try to minimize their maximum expected losses. All risky conditions for decision making are presented in well manner that gives proper solution for each problem in investment. The main advantage of this model is the avoidance of large losses for investors (Hoe, Hafizah & Zaidi, 2010). It concentrates on huge portfolio losses and offers suitable performance characteristics that allow positive portfolio returns. It is more suitable for those investors, who are interested to invest for long period of time and averse to downside risk. With this, it is more advantageous for are non-normally distributed returns that makes it more effective for complex situations in portfolio. Therefore, MM model gives an opportunity to the investor to invest safely in different securities by analyzing risk involved with those securities effectively.


Comparison among Models: The MAD is the less complicated method to use than M-V method. The M-V model assumes normality of stock returns but MAD model does not consider this assumption. MAD is easier to compute than Markowitz because it eliminates the need for a large-scale covariance matrix. Both MM and MV models are sensitive to outliers in the historical data. MAD is less sensitive to outliers in the historical data than both models. In a Markowitz model, the risk is measured by a variance that results in a quadratic programming model. In contrast to this, the MAD model is based on absolute deviation instead of a variance. The MAD model is computationally attractive and based on linear programming method. With this, MM model is a linear program like MAD model as it can be solved faster than MV model. Furthermore, MV model assumes a multivariate normal distribution of assets returns, while remaining both models are not based on this assumption.


Apart from this, MV model represents the portfolio in size nor number of assets but portfolio in other models is represented in time periods T (Hoe, Hafizah & Zaidi, 2010). The portfolio under MAD model is more controllable because, in this, T works as a control variable to restrict the number of assets. MAD and MM models use integer programming techniques that are useful to get an integer solution. These models can solve the problem faster and more efficiently than MV model. Both models take less time to reach a solution rather than MV model (Hassan, Siew & Shen, 2012). For example: In order to analyze the effectiveness of these all models in investment, a research was conducted, in which various data related to monthly returns of 54 stocks were recorded in the Kuala Lumpur Composite Index (KLCI) from January 2004 until December 2007.


Comparison of Portfolio optimization models:


Parameters MV MAD MM
Mean return 0.0100 0.0100 0.0205
Risk 0.0181 0.0123 0.0131
Performance 0.5525 0.8130 1.5649

(Source: Hoe, Hafizah, & Zaidi, 2010)

Through various data, different parameters were calculated under all models that are mentioned in the above table. On the basis of above result, it can be stated that the mean return of MM model is the highest (0.0205) among the three models. In addition to this, it can be also identified that the MV model is the riskiest portfolio (0.0181), while the less risky portfolio is MAD model (0.0123). The MM model shows the highest performance (1.5649), whereas the MV model (0.5525) gives the lowest performance (Hoe, Hafizah, & Zaidi, 2010). It is because the MM model is consistent with expected utility maximization principle that represents an extreme form of risk aversion. In this study, it is found that MM model outperforms the other models and the MV model is not up to mark in order to perform like other models. It is based on game theory that assures about positive investment returns to the investors. Therefore, the MM model can be a better choice for portfolio optimization as compared to other methods because of having higher performance (Fang, Lai & Wang, 2008).


Conclusion: On the basis of above discussion, it can be stated that optimization models are very effective to analyze the risk associated with international investment or diversification. These methods are used to determine the risk in different parameters that is beneficial to reduce the involved risks in portfolio investment. Through these methods, it would be easy for the investors to identify poetical risk and plan mitigation strategies. It can be also concluded that the MM model can be very effective tool to measure the risk associated with the investment in global sectors because of having more advantages than other models.


References: Fabozzi, F.J. (2008). Handbook of Finance, Financial Markets and Instruments. USA: John Wiley & Sons. Fang, Y., Lai, K.K. & Wang, S. (2008). Fuzzy Portfolio Optimization: Theory and Methods. UK: Springer. Farias, C.V., Vieira, W.C. & Santos, M.L. (2006). Portfolio Selection Models: Comparative Analysis and Applications to the Brazilian Stock Market. Revista De Economia E Agronegócio, 4(3), 387-407. Hassan, N., Siew, L.W. & Shen, S.Y. (2012). Portfolio Decision Analysis with Maximin Criterion in the Malaysian Stock Market. Applied Mathematical Sciences, 6(110), 5483-5486. Hoe, L. W., Hafizah, J.S. & Zaidi, I. (2010). An empirical comparison of different risk measures in portfolio optimization. Business and Economic Horizons, 1(1), 39-45. Jobst, N.J., Horniman, M.D., Lucas, C.A. & Mitra, G. (2001). Computational aspects of alternative portfolio selection models in the presence of discrete asset choice constraints. QUANTITATIVE FINANCE, 1, 1–13. Satchell, S. & Scowcroft, A. (2003). Advances in Portfolio Construction and Implementation. USA: Butterworth-Heinemann.


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