<p> </p><p>The statistics has shown that men and women have different investing strategies, where men tend to choose riskier investments and women lean towards less risky investments. The financial theory states that individuals are risk averse in general, and some prior studies have shown that women are more so than men. Moreover, financial knowledge and experience have been pointed out to be one of the factors affecting one’s financial risk tolerance. This paper researches these issues by addressing the following to questions: Are there any gender differences in Financial Risk Tolerance? Does knowledge and experience have impact on Financial Risk Tolerance? A literature search has been done and relevant theory has been gathered and review, which served as a base and a framework for conducting this study. A quantitative methodological study has been carried out by handing out questionnaires, based on a 13-item Financial Risk Tolerance scale developed by Grabble & Lytton (1999). The target population was the Umeå University students. The size of the sample was 139. The findings of this paper confirm prior studies which state that women, in general, are less risk tolerant then men – female students scored lower on the Financial Risk Tolerance Score than male students. Furthermore, this study also shows that one’s Financial Risk Tolerance is affected by experience and knowledge in the field of finance – students that are studying economics had higher Financial Risk Tolerance score than students that were studying other subjects.</p><p> </p>

The statistics has shown that men and women have different investing strategies, where men tend to choose riskier investments and women lean towards less risky investments. The financial theory states that individuals are risk averse in general, and some prior studies have shown that women are more so than men. Moreover, financial knowledge and experience have been pointed out to be one of the factors affecting one’s financial risk tolerance. This paper researches these issues by addressing the following to questions: Are there any gender differences in Financial Risk Tolerance? Does knowledge and experience have impact on Financial Risk Tolerance? A literature search has been done and relevant theory has been gathered and review, which served as a base and a framework for conducting this study. A quantitative methodological study has been carried out by handing out questionnaires, based on a 13-item Financial Risk Tolerance scale developed by Grabble &amp; Lytton (1999). The target population was the Umeå University students. The size of the sample was 139. The findings of this paper confirm prior studies which state that women, in general, are less risk tolerant then men – female students scored lower on the Financial Risk Tolerance Score than male students. Furthermore, this study also shows that one’s Financial Risk Tolerance is affected by experience and knowledge in the field of finance – students that are studying economics had higher Financial Risk Tolerance score than students that were studying other subjects.

Most firms are actively assessing the financial risks exposure and do determine a policy for the hedging activities. It is not solely the risk aversive attitude from the managers that need to be overlooked, but to provide sufficient information to the shareholder is desirable for minimizing the gap of information asymmetry, which is by itself considered a tool for value creation (Bergstrand et al. 2009:45-47). To narrow this gap, listed Swedish companies have since 2005 been required to disclose their financial risk in their Annual Reports.  By using a quantitative approach the researcher will review the financial risk note in Annual Reports of 2008 to identify characteristics and determinant variables on firms depending on whether they utilize currency derivatives or not. An independent two-sample t-test has showed statistical significance that there difference of the means regarding size, FX exposure and leverage between users and non-users of currency derivatives. The means of currency derivatives users were higher for Size and FX exposure, while lower for leverage. A positive correlation between a firm’s size and FX exposure was found, suggesting that the determinant for hedging FX exposure could be explained by the size of the firm and vice versa.

Thesis (MBA) -- Stellenbosch University, 2007. / ENGLISH ABSTRACT: Hedge funds are amongst the fastest growing types of investment funds, both in tenns of worldwide assets under management, as well as the number of private and institutional investors. More recently, analysts and investors focussed their attention on accurately estimating the inherent risks of hedge funds (e.g, Brooks & Kat, 2001; Fung & Hsieh, 2004). Past research suggests that the traditional approach of assessing the risks of investment funds through mean-variance analysis can lead to severe underestimation of left-hand-tail risks for hedge funds (Amenc, Malaise, Martellini & Vaissie, 2004; Favre & Galeano, 2002; Fung & Hsieh, 1999). This phenomenon is mainly attributab le to the non-normal distribution of monthly hedge fund returns around the mean. In addition, it has been found that skewed return distribution with high excess kurtosis has substantial impact on the rel iability of beta as a measure of systemic risk in hedge funds (Chan, Getmansky, Haas & Lo, 2005). Other problems when estimating hedge fund risks arise from serial correlation of time series (Getmansky, Lo & Makarov, 2003), managerial and survivorship bias (Amin & Kat, 2001 ), as well as spurious bias when estimating performance from economic time series (Fung & Hsieh, 2000). The following thesis provides statistical evidence of the limitations of traditional risk measures when applied to hedge fund investments. It also includes advice on how to improve the significance of the aforementioned risk measures. In the course of the mean-variance analysis, the applicability and reliability of Value at Risk as a risk measurement tool for hedge funds is explored. Furthennore, the reliability and accuracy of different univariate and multivariate regression models is tested. In the final chapter emphasis is placed on the possibilities of predicting the inherent risks of single funds from hedge fund style index performance. This should provide investors and analysts with an introductory framework for the appropriate risk assessment of hedge funds, considering the unique structure and dynamics of these alternative investment funds. / AFRIKAANSE OPSOMMING: Skansfondse tel onder die vinnigste groeiende tipes beleggingsfondse in terme van sowel wereldwye bates onder bestuur as die aantal private en institusionele beleggers. OnJangs het analiste en beleggers hulle aandag daarop begin toespits om die inherente risiko's verbonde aan skansfondse akkuraat te bereken (Brooks & Kat. 2001; Fung & Hsieh, 2004). Vroeere navorsing het daarop gedui dat die tradisioncle benadering om die risiko's verbonde aan beleggingsfondse deur gemiddeldevariansie-analise te takseer, daartoe kan lei dat linkerkantse-eindrisiko's verbonde aan skansfondse emstig onderskat word (Fung & Hsieh, 1999; Favre & Galeano, 2002; Amenc. Malaise, Martellini & Vaissie, 2004). Hierdie verskynsel is hoofsaaklik toe te skryf aan die abnonnale verspreiding van maandeliksc skansfondsopbrengste rondom die gemiddelde. Boonop is bevind dat skewe verdeling met hoe kurtose-oorskryding aansienlik inslaan op die betroubaarheid van beta as 'n meting van sistemiese risiko by skansfondse (Chan. Getmansky. Haas & Lo, 2005). Ander probleme by die raming van skansfondsrisiko's spruit uit tydreekskorrelasie (Getmansky, Lo & Markov, 2003), bestuurs- en oorlewingsydigheid (Amin & Kat, 2002) en vals sydigheid by die beraming van prestasie uil die ekonomiese tydsreeks (Fung & Hsieh, 2000). Hierdie tesis gaan statistiese bewyse lewer van die tradisioncle risikometings se beperkings wanneer dit op skansfondsbeleggings toegepas word. Verder sal daar raad gegee word oor hoe om die beduidendheid van die genoemde risikometings te verbeter. In die loop van die gemiddeldevariansie-analise sal die toepasbaarheid en betroubaarheid van die Waarde onder Risiko as 'n risikometing vir skansfondse ondersoek word. Voorts sal die betroubaarheid en akkuraatheid van verskillende ecnvariaat- en meervariaatregressiemodelle getoets word. In die laaste hoofstuk val die klem op die moontlikheid om die inherente risiko's van enkelfondse aan die hand van 'n skansfondstipe-indeksprestasie te voorspel. Wat hier volg, behoort beJeggers en analistc van 'n inleidende raamwerk vir die toepaslike risikotaksering van skansfondse - met inagneming van die unieke struktuur en dinamika van hierdie altcmatiewe beleggingsfondse - te voorsien.

This empirical study employs a different methodology to examine the change in wealth associated with mergers and acquisitions (M&As) for US firms. Specifically, we employ the standard CAPM, the Fama-French three-factor model and the Carhart four-factor models within the OLS and GJR-GARCH estimation methods to test the behaviour of the cumulative abnormal returns (CARs). Whilst the standard CAPM captures the variability of stock returns with the overall market, the Fama-French factors capture the risk factors that are important to investors. Additionally, augmenting the Fama-French three-factor model with the Carhart momentum factor to generate the four-factor captures additional pricing elements that may affect stock returns. Traditionally, estimates of abnormal returns (ARs) in M&As situations rely on the standard OLS estimation method. However, the standard OLS will provide inefficient estimates of the ARs if the data contain ARCH and asymmetric effects. To minimise this problem of estimation efficiency we re-estimated the ARs using GJR-GARCH estimation method. We find that there is variation in the results both as regards the choice models and estimation methods. Besides these variations in the estimated models and the choice of estimation methods, we also tested whether the ARs are affected by the degree of liquidity of the stocks and the size of the firm. We document significant positive post-announcement cumulative ARs (CARs) for target firm shareholders under both the OLS and GJR-GARCH methods across all three methodologies. However, post-event CARs for acquiring firm shareholders were insignificant for both sets of estimation methods under the three methodologies. The GJR-GARCH method seems to generate larger CARs than those of the OLS method. Using both market capitalization and trading volume as a measure of liquidity and the size of the firm, we observed strong return continuations in the medium firms relative to small and large firms for target shareholders. We consistently observed market efficiency in small and large firm. This implies that target firms for small and large firms overreact to new information resulting in a more efficient market. For acquirer firms, our measure of liquidity captures strong return continuations for small firms under the OLS estimates for both CAPM and Fama-French three-factor models, whilst under the GJR-GARCH estimates only for Carhart model. Post-announcement bootstrapping simulated CARs confirmed our earlier results.

D.Comm. / Copulas provide a useful way to model different types of dependence structures explicitly. Instead of having one correlation number that encapsulates everything known about the dependence between two variables, copulas capture information on the level of dependence as well as whether the two variables exhibit other types of dependence, for example tail dependence. Tail dependence refers to the instance where the variables show higher dependence between their extreme values. A copula is defined as a multivariate distribution function with uniform marginals. A useful class of copulas is known as Archimedean copulas that are constructed from generator functions with very specific properties. The main aim of this thesis is to construct multivariate Archimedean copulas by nesting different bivariate Archimedean copulas using the vine construction approach. A characteristic of the vine construction is that not all combinations of generator functions lead to valid multivariate copulas. Established research is limited in that it presents constraints that lead to valid multivariate copulas that can be used to model positive dependence only. The research in this thesis extends the theory by deriving the necessary constraints to model negative dependence as well. Specifically, it ensures that the multivariate copulas that are constructed from bivariate copulas that capture negative dependence, will be able to capture negative dependence as well. Constraints are successfully derived for trivariate copulas. It is, however, shown that the constraints cannot easily be extended to higher-order copulas. The rules on the types of dependence structures that can be nested are also established. A number of practical applications in the financial markets where copula theory can be utilized to enhance the more established methodologies, are considered. The first application considers trading strategies based on statistical arbitrage where the information in the bivariate copula structure is utilised to identify trading opportunities in the equity market. It is shown that trading costs adversely affect the profits generated. The second application considers the impact of wrong-way risk on counterparty credit exposure. A trivariate copula is used to model the wrong-way risk. The aim of the analysis is to show how the theory developed in this thesis should be applied where negative correlation is present in a trivariate copula structure. Approaches are considered where conditional and unconditional risk driver scenarios are derived by means of the trivariate copula structure. It is argued that by not allowing for wrong-way risk, a financial institution’s credit pricing and regulatory capital calculations may be adversely affected. The final application compares the philosophy behind cointegration and copula asset allocation techniques to test which approach produces the most profitable index-tracking portfolios over time. The copula asset allocation approach performs well over time; however, it is very computationally intensive.

Chapter 1, "Emergency Preparedness: Rare Events and the Persistence of Uncertainty," develops a framework to understand how uncertainty might spike and persist after low-probability events occur. Unexpected events can have lasting effects on financial uncertainty, which in turn affects the real economy. This chapter uses a model in which the realizations of ex-ante unlikely events endogenously result in lower levels of private information. Lower levels of information propagate within the model, as uncertainty makes it harder for agents to acquire information about future periods, resulting in uncertainty persistence. This model of uncertainty is applied to an economy with a financial market. Uncertainty reduces asset demand and expected wealth, while increasing dispersion of beliefs. It also reduces investment and output, and results in higher credit spreads. Data on financial uncertainty, dispersion of beliefs, risk appetite, and credit spreads confirm the predictions of the model. Chapter 2, "Inattentive Valuation and Belief Polarization," uses a similar motivation to think about how two agents can disagree on the truth after seeing the same data. Based on the recent literature in inattention, we build a model allowing identical agents, shown the same set of signals from an objective state of the world, to permanently diverge in their posteriors. The inattentive framework allows for two effects: a confirmation and a confidence effect. The former states that agents who have a bias arrange their attention to perceive signals that agree with that bias. The latter states that agents pay less attention to any signal the more biased they are. These effects allow for permanent polarization of posteriors, even on issues with objective truth. Chapter 3, "The Real Consequences of Countercyclical Capital Controls," looks at the consequences of capital controls on investment and consumption in Brazil. Brazil is the most preeminent case of controls being imposed countercyclically. We find that capital controls have a significant negative impact on investment. The macro analysis uses a synthetic control method and finds that investment could have been approximately 20% higher if controls had not been put in place. The micro analysis uses a panel data approach and finds that the controls reduced the investment to assets ratio by as much as 40%, with some of its effects mitigated by the extension of subsidized credit by the government through the development bank. These results indicate that the renewed support for controls since the Great Financial Crisis should be more cautiously evaluated as it might harm the potential growth rate of Emerging Economies for a long-lasting period.

by Leung Tsun Ip. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 72-78). / Abstracts in English and Chinese. / Chapter 1. --- Introduction --- p.1 / Chapter 2. --- Literature Review --- p.6 / Chapter 2.1 --- Development of estimation of Value-at-Risk (VaR) --- p.6 / Chapter 2.2 --- Methods to evaluate VaR --- p.9 / Chapter 2.2.1 --- Non-paremetric Method --- p.9 / Chapter 2.2.2 --- Semi-parametric Method --- p.11 / Chapter 2.2.3 --- Parametric Method --- p.12 / Chapter 3. --- Extreme Value Theory (EVT) --- p.16 / Chapter 3.1 --- Introduction of Extreme Value Theory (EVT) --- p.16 / Chapter 3.1.1 --- Block Maxima Approach --- p.18 / Chapter 3.1.2 --- Peaks over Threshold (POT) Approach --- p.21 / Chapter 3.1.3 --- Comparison between Block Maxima and POT Approach --- p.22 / Chapter 3.2 --- Numerical Illustration --- p.23 / Chapter 3.2.1 --- Data --- p.23 / Chapter 3.2.2 --- Diagnosis --- p.24 / Chapter 4. --- Dynamic Extreme Value Theory (DEVT) --- p.29 / Chapter 4.1 --- Theoretical Framework of DEVT --- p.29 / Chapter 4.2 --- Estimation of Parameters --- p.32 / Chapter 4.3 --- Determination of Threshold Level --- p.37 / Chapter 4.4 --- Estimation of zq --- p.44 / Chapter 5. --- Backtesting and Time Aggregation --- p.49 / Chapter 5.1 --- Backtesting DEVT --- p.49 / Chapter 5.2 --- Time Aggregation --- p.55 / Chapter 6. --- Case Study: China Aviation Oil Singapore (CAO) Incident --- p.61 / Chapter 6.1 --- Background Information --- p.61 / Chapter 6.2 --- Data Analysis --- p.63 / Chapter 6.3 --- Suggestion --- p.68 / Chapter 7. --- Discussion --- p.71 / References --- p.72 / Chapter A. --- Appendix --- p.79

Originally presented as the author's doctoral Thesis (Universität, St. Gallen, 2006). / Description based on print version record. Includes bibliographical references.

This paper provides a credit risk quantification system for banks to estaminate the credit risk of loans to small and mediume nterprises(SMEs). As we know, the most difficult thing for banks to handle SME loans is whose financial reporting lacks transparency and no valuable reference. We use non-financial variables and employ the logisitic regression to develop the credit risk predict model. We concludet: first, when construct a SMEs credit rating system, non-financial factors should be seriously considered and adopted. Second, because of positioned different stage of firm life cycle, the credit rating model should be set up differently by different stage of firm. Third, SME loans should to make much of establishing ¡§relationship-based¡¨ in order to meet the various demands of risk management.