In this master thesis we are investigating the impact of economic bubbles and crisis, when composing investor’s optimal portfolio. The main purpose of this master thesis is to develop an asset allocation model, which is using the classic portfolio theory framework combined with new optimization methods. Financial crises are becoming a more and more known phenomenon as it almost happens once or twice in a decade. The world has always experienced these events, but still nobody seems able to predict or tell why they are happening. We do an analysis on the S&P 500 index, and show the length and the frequency of the major drawdown periods. As investor it is very important to know what to expect, when determining the risk-profile. Therefore when forecasting the future, our models should assign a significant probability to these events. Using the classic Markowitz approach, the investor automatic agrees on some standard assumption, which not always matches the real world. We are testing the reliability of these assumptions and investigate the weaknesses. Building our own asset allocation model we try to eliminate the assumptions that stock market returns is normally distributed and the correlation effects between the assets are kept constant. Our model is meant to be used by both private and professional investors as a tool, when choosing their asset allocation. In our thesis we have used a stock sector universe. Most people divide their asset by the geography and choose their portfolio as a regional weighting. We believe that choosing a sector universe is more correct, because it does not conflict with overlap or changing categories such as emerging markets. Another good reason compared with allocating on regions, is that the correlations between sectors are lower, and therefore gives greater diversifications effect. Besides it allows us to include assets such as real estate or commodities, which often are referred to as alternatives. Our model includes two mainly differently approaches. Investors has an asymmetric utility functions hence it would be an overestimation to look at the standard deviation as the risk measure. We believe that the downside risk is more appropriate and therefore use the Value at Risk concept. The other new focus in which our model contains is how we calculate the expected return. Long term investors would care more about the cumulative growth of their investment in which case therefore it would be more correct to use a geometric mean. Many models often fail when estimating some kind of mean, so one should be careful and critical about that. We end up in our master thesis by comparing our own model with the classic mean variance model and the later alternative Black-Litterman model.
|Educations||MSc in Finance and Accounting, (Graduate Programme) Final Thesis|
|Number of pages||151|