Browsing by Author "Dissanayake, AR"

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item: Thesis-Full-text
Portfolio optimization through quadratic Programming when there is perturbation in the return matrix
Dharmathilaka, NT; Dissanayake, AR; Ranasinghe, LP
According to Finance the investor who invests in risky assets such as stocks, after forming a diversified portfolio or collection of securities; is interested in earning maximum return out of minimum risk, and it is more technically known as Portfolio Optimization(PO). The present problem is a Quadratic Programming problem is consisted of simultaneous variations of initial return vector of each company. In this study the main objective was to study the behavior of covariance-variance matrix, correlation matrix and the optimum weights vector when there is small perturbation in the mean return vector. Then fitting a model between perturbation values versus optimum weights was also performed. The results show that there is a significant variation of optimum weights when there is small perturbation in the return matrix. And there is no change in the covariance or correlation matrices. This is done under the assumption that there is no short selling. Apart from that results show that when there is perturbation in the return matrix the expected return of the portfolio is also changing. When the value of perturbation is increased individually for one company only, to drive away at least one company from the optimum weights (to zero the optimum weight of one company) it was observe that the perturbation value should be increased extensively for the SGX data sample. That means the weights are not very much sensitive to perturbations in the market. If negatively mean companies are removed and perturbation is done for positively mean companies the effort to remove at least one company from the optimum weights is less.
item: Thesis-Full-text
Pragmatic portfolio optimization : gauging black-litterman model in emerging markets
Sivathas, K; Dissanayake, AR; Ranasinghe, LP
With the advent of modern portfolio theory1 in 1952 by Harry Markowitz, the investment management industry had witnessed an uprising. Yet the encountered shortfalls and rigidity of the methodologies lead to the development of Black- Litterman model by 1990s. The Black- Litterman model addressed those deficiencies and introduced the luxury of incorporating the unique views of Asset managers about the assets under management in their portfolios. This projected research efforts implementing the difficult phases of the Black-Litterman model and depicts its practical and pertinent nature by comparing to other portfolio allocation methods which uses the historical and CAPM methods. The modeling of mean variance (reward and risk) and then the portfolio allocation has been done using these three distinct methods. Thereafter the benevolent leads of the BL method over others have been discussed. To assess the BL model, eight stocks such as Samsung Electronics Co., Ltd (SAMSUNGKorea), China Mobile Communications Corporation (CHINA MOB- China), Naspers Limited (NASPERS-South Africa), Emaar Properties (EMAR- United Arab Emirates), Koc Holding AS (KCHOL- Turkey), Akbank (AK BANK- Turkey), Braskem SA (BRKM5- Brazil) and Taiwan Cement Corporation (TAIWAN CE- Taiwan) which comes under Emerging markets have been considered. For the analysis, the monthly stock closing prices published by Bloomberg L.P. have been taken. In addition to this the monthly closings of the MSCI Emerging Markets Index and US Treasury rates have been obtained to use respectively as the market benchmark and market risk free rate. Four outlooks/views about these stocks were evaluated and the vector of BL Expected Excess Return which is the weighted average of Equilibrium market return vector and the View vector have been established using the Black- Litterman model. The grandeur of the BL method that’s tailored portfolio weightages corresponding the Asset managers’ views was studied. The model has been implemented using the scientific software MATLAB. Other than the Black-Littreman methodology, the concepts of Markowitz portfolio theory, efficient frontier, CAPM returns, Portfolio expected returns, Portfolio variances and the Sharp ratios have been used to describe the portfolio dynamics. The portfolio weightages derived using BL Expected Excess Returns did accord with the four views. It has been clearly witnessed that the incorporation of View vector, had caused the Equilibrium market return vector to get adjusted with respect to the outlooks/views.
item: Thesis-Full-text
Stochastic differential equation approach for daily gold prices in Sri Lanka
Weerasinghe, WMHN; Dissanayake, AR
In our day to day life, predictability of gold prices is significant in many domains such as economic, financial and political environment. The objectives of this research are to study the behavior of the gold price in Sri Lanka, to forecast the daily gold prices making use of four Stochastic Differential Equation (SDE) models, Brownian motion, Geometric Brownian motion, Cox-Ingersoll-Ross (CIR) model and Vasicek model and compare the results with an ARIMA (2,1,2) model which is used to forecast the Sri Lankan gold prices in a previous research. The daily gold prices per troy ounce in Sri Lanka are obtained from 01st of October 2015 to 14th of October 2016 from the website http://www.cbsl.gov.lk/htm/english/_cei/er/g_1.asp on 1st of November, 2016. The gold prices from 01st of October 2015 to 07th of October 2016 are used to estimate the parameters of the four models and the parameter estimation is done using maximum likelihood estimation method. The gold prices from 10th of October 2016 to 14th of October 2016 are used to forecast the gold price. By taking the gold price on 10th of October 2016 as the initial value, daily gold prices from 11th of October 2016 to 14th of October 2016 are forecasted. Numerical approximations are carried out using Euler-Maruyama approximation method and the Monte Carlo simulation technique is used to simulate the daily gold prices. After evaluating forecasting accuracy of estimated models and existing ARIMA (2,1,2) model by root mean square error (RMSE) and mean absolute percentage error (MAPE), it turns out that the Vasicek model has the minimum RMSE and MAPE values for the given data set. The price of the gold may change rapidly because of some economic factors such as inflation, currency exchange rates etc. In these situations the best SDE model to forecast the daily gold price in Sri Lanka may be changed to another model. Hence this method is suitable for short runs only.