The aim of this study is to evaluate the risk-return efficiency of credit policies for managing portfolio credit risk of banking institutions. The focus of the empirical analysis is on the impact of risk pricing and problem loan restructuring on bank risk and returns using a simulation model that represents an operating environment of lenders servicing the Australian farm sector. Insurance theory principles and agency relationships between a borrower and a lender are integrated into the portfolio theory framework. The portfolio theory framework is then couched in terms of the capital budgeting approach to generate a portfolio return distribution function for a particular credit policy regime. Borrowers are segmented by region, industry, loan maturity and credit risk class. Each credit risk class defines risk constraints on which a stochastic simulation model may be developed for credit scoring an average borrower in a portfolio segment. The stochastic simulation method is then used to generate loan security returns for a particular credit policy regime through time with loan return outcomes weighted by the number of borrowers in a segment to give measures of portfolio performance. Stochastic dominance efficiency criteria are used to choose between distributions of NPV of bank returns measured for a number of credit policy alternatives. The findings suggest that banks servicing the Australian farm sector will earn more profit without additional portfolio risk if the maximum limit to which pricing accounts for default risk in loan reviews is positively linked to volatility of gross incomes of farm business borrowers. Importantly, credit-underwriting standards must also be formulated so as to procure farm business borrowers of above average productivity with loans that are fully secured using fixed assets. The results of simulations also suggest that restructuring loans in event of borrower default provide for large benefits compared to a �no restructuring� option.

Introduction: Within the private economy individuals are today accepting an increased individual responsibility for retirement funds and other economic challenges. This is due to the decreased confidence in government programs and that the increased life expectancy raises the risk to outlive the own life savings. The shift from state run security systems to more private responsibility could be spotted in Sweden as well, where one important part of the private economy, the saving system for retirement, has been changed. The pension plan met critics when it was proposed and implemented, especially for the part where some of the responsibility relies on the individual. It was discussed that this huge responsibility might be larger then what many individuals would be able to handle. These factors have increased the importance of successes in the individuals own saving plans. To enhance the chances of a certain level of success, individuals turn to private bankers to plan their wealth and savings. The position of these private bankers and their performance has amplified more then ever before. Purpose: The purpose of the thesis is to describe: if Swedish private bankers look on enough features of an investor to be able to prescribe the appropriate portfolio for the investor? Methodology: A qualitative research has been used since the purpose and the information gathered demanded it. Cases where the authors created four fictitious investors was sent out by electronic mail and the private bankers where asked to construct suitable portfolios to each investor. The cases were sent out to ten different private bakers, however only two replied within the deadline. The authors have strived to keep high reliability and validity in the paper; however the small response rate lowers the reliability. Conclusion: The qualitative research found that Swedish private bankers look on enough features on a client to be able to prescribe an appropriate portfolio for the investor. However the Private bankers’ main focus seems to be time horizon and risk profile of the investor.

Modern portfolio theory commenced the ensuing debate regarding the benefits of active versus passive management in regards to mutual funds. The two opposing mutual fund management styles claim that they produce superior risk-adjusted performance. The dissertation explores the research question: During the full 1995 to 2008 market cycle, which investment management style, active or passive, produced the better risk-adjusted performance? The significance of this historic stock market period relates to the fact it contains the two greatest bull markets (1995-1999 & 2003-2007) followed by subsequent bear markets (2000-2002 & 2008). The study tested nine hypotheses, derived from the above research question, for the 5 different time periods (1995-1999, 2000-2002, 2003-2007, 2008, and 1995-2008). Based on previous research, one would expect the passive management styles to out-perform the active styles during expansion whereas the performance would reverse during market contraction. The Sharpe composite portfolio performance measure, that combines risk and return into a single value, was used to measure, analyze, and rank risk-adjusted performance. The study, comprised of 45 statistical tests, found that on a risk-adjusted basis that the active indices (proxies for active management) Sharpe ratios were significantly greater than those of the passive indices (proxies for passive management) Sharpe ratios for; 1) the midcap blend category for the periods 1995 to 2008 and 1995 to 1999; 2) the small blend category for the periods 1995 to 2008 and 1995 to 1999, and 3) the small value category for the periods 1995 to 2008, 1995 to 1999, and 2000 to 2002. Therefore, the active indices Sharpe ratio significantly exceeded the passive indices Sharpe ratio for 16% of the statistical tests conducted while the active indices Sharpe ratio did not significantly exceed the passive indices Sharpe ratio for 84% of the statistical tests conducted. The findings suggested that in the long run passive management produced better performance results than active management.

An investor wants to maximize return at the cost of as little risk as possible and theBlack-Litterman model can help see that this condition is met. This thesis willinvestigate whether a portfolio created by using modern portfolio theory can beat thebenchmark index in terms of risk-adjusted return during a five year backtest period(2013-2017). Harry Markowitz provides the mean variance optimization frameworkwhile the practical Black-Litterman model adds the opportunity to tweak performancewith views on stock returns. The method for producing views for the Black-Littermanmodel can vary a lot and is what makes this thesis, for all that we know, unique. Theviews in our model stem from regression on the summed up earnings per share forthe last four quarters multiplied by the corresponding historical price earnings ratioand the historical stock price. The regressions provide data on how over orundervalued the stocks are. Backtesting our modified Black-Litterman model yieldsimpressive results in terms of risk-adjusted return and we encourage other studentsof the financial market to further investigate the performance of our modified portfolio.However most of the results are not statistically significant on a 5% significance leveldue to the need for more data points. This method is purely quantitative and can befully replicated to yield the same results for any interested investor.

This paper analyzes theperformance of five commercial real estate property types (office, retail, industrial, apartment, and hotel) between 2000 and 2012 to determine the U.S. housing crisis'simpact on Real Estate investing. Under the concept of Modern Portfolio Theory, the data was analyzed using investment analysis programs to determine correlation, risk/return characteristics, and trade-offs (Sharpe ratio) as well as the optimal allocation among the individual property types. In light of the results, each property type plays a different role in investment strategies in various economic cycles. Some assets are attractive solely based onpotential return, or risk for return tradeoffs; however, through diversification, other property types play valuable roles in hedging risk on investors' target returns.

Agricultural economists have long studied crop yields and risk to help farm-level risk management. Producers face difficult decisions every year regarding market prices, management practices, and the uncertainty of weather. In our research, we use crop yield records while incorporating the modern portfolio theory to find the optimal planting portfolios giving a specific risk level. Our assets are on corn, cotton, and soybeans yields from the Mississippi Delta region. This study is unique because there are not any previous studies using crop histories linked to the modern portfolio theory. The main idea is to realize how much of each asset or what percentage to invest in out of the specific portfolio. By having these portfolios readily available for farmers, we aim to diminish the risk to help producers with springtime decision-making. Armed with these findings, we can better understand the economic implications of how crop rotations factor into farm-level risk management.

Systemic risk is the risk that an economic shock may result in the breakdown of the fundamental functions of the financial system. It can involve multiple vectors of infection such as chains of losses or consecutive failures of financial institutions that may ultimately cause the failure of the financial system to provide liquidity, stable prices, and to perform economic activities. This thesis develops methods to quantify systemic risk, its effect on the financial system and perhaps more importantly, to determine its cause. In the first chapter, we provide an overview and a literature review of the topics covered in this thesis. First, we present a literature review on network-based models of systemic risk. Finally we end the first chapter with a review on market impact models. In the second chapter, we consider one unregulated financial institution with constant absolute risk aversion investment risk preferences that optimizes its strategies in a multi asset market impact model with temporary and permanent impact. We prove the existence and derive explicitly the optimal trading strategies. Furthermore, we conduct numerical exploration on the sensitivity of the optimal trading curve. This chapter sets the foundation for further research into multi-agent models and systemic risk models with optimal behaviours. In the third chapter, we extend the market impact models to the multi-agent setting. The agents follow a game theoretic strategy that is constrained by the regulations imposed. Furthermore, the agents must liquidate themselves if they become insolvent or unable to meet the regulations imposed on them. This paper provides a bridge between market impact models and network models of systemic risk. In chapter four, we introduce a financial network model that combines the default and liquidity stress mechanisms into a ``double cascade mapping''. Unlike simpler models, this model can quantify how illiquidity or default of one bank influences the overall level of liquidity stress and default in the system. We derive large-network asymptotic cascade mapping formulas that can be used for efficient network computations of the double cascade. Finally we use systemic risk measures to compare the results of including with and without an asset firesale mechanism. / Thesis / Doctor of Philosophy (PhD)

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The point of this thesis is to compare classic asset pricing models using historic UK data. It looks at three of the most commonly used asset pricing models in Finance and tests the suitability of each for the UK market. The models considered are the Capital Asset Pricing Model (1964, 65 and 66) (CAPM), the Fama-French 3-Factor Model (1993) (FF3F) and the Carhart 4-Factor Model (1997) (C4F). The models are analysed using a 34 year sample period (1980-2014). The sample data follows the structure explained in Gregory et al (2013) and is compiled of stocks from the London Stock Exchange (LSE). The stocks are grouped into portfolios arranged by market capitalisation, book-to-market ratio, past 2-12 month stock return and past 12 month standard deviation of stock return. Statistical analysis is performed and the suitability of the models is tested using the methods of Black, Jensen \& Scholes (1972), Fama \& MacBeth (1973) and Gibbons, Ross \& Shanken (1989). The results compare descriptive and test statistics across the range of risk factors and test portfolios for the each testing method on all three models. They show that although the UK market has some noticeable factor anomalies, none of the models clearly explains the 1980-2014 stock returns. However, of the three models, C4F shows the highest explanatory power in predicting stock returns.

This empirical study has shown that optimal portfolios need approximately 10 securities to diversify away the unsystematic risk. This challenges previous studies of randomly chosen portfolios which states that at least 30 securities are needed. The result of this study sheds light upon the difference in risk diversification between random portfolios and optimal portfolios and is a valuable contribution for investors. The study suggests that a major part of the unsystematic risk in a portfolio can be diversified away with fewer securities by using portfolio optimization. Individual investors especially, who usually have portfolios consisting of few securities, benefit from these results. There are today multiple user-friendly software applications that can perform the computations of portfolio optimization without the user having to know the mathematics behind the program. Microsoft Excel’s solver function is an example of a well-used software for portfolio optimization. In this study however, MATLAB was used to perform all the optimizations. The study was executed on data of 140 stocks on NASDAQ Stockholm during 2000-2014. Multiple optimizations were done with varying input in order to yield a result that only depended on the investigated variable, that is, how many different stocks that are needed in order to diversify away the unsystematic risk in a portfolio. / <p>Osäker på examinatorns namn, tog namnet på den person som skickade mejl om betyg.</p>