This thesis investigates the choices under risk in the framework of non-expected utility theories. One of the key contributions of this thesis is providing an approach that allows for a complete characterisation of Cumulative Prospect Theory (CPT) preferences without prior knowledge of the reference point. The location of the reference point that separates gains from losses is derived endogenously, thus, without any additional assumptions on the decision maker’s risk behaviour. This is different to the convention used in the literature, according to which, the reference point is preselected. The problem arising from imposing the location of the reference point is that the underlying preference conditions might not be alligned with the predictions made by the model. Consequently, it is difficult to verify such a model or to test it empirically. The present contribution offers a set of normatively and descriptively appealing preference conditions, which enable the elicitation of the reference point from the decision maker’s behaviour. Since these conditions are derived using objective probabilities, they can also be applied to settings such as health or insurance, where the continuity of the utility function is not required. As a result, the obtained representation theorem is not only the most general foundation for CPT currently available, but it also provides further support for the use of CPT as a modelling tool in decision theory and fi…nance. Another contribution that this thesis can be credited with is an application of rank-dependent utility theory (RDU) to the problem of insurance demand in the monopoly market affected by adverse selection. The present approach extends the classical model of Stiglitz (1977) by accounting for an additional component of heterogeneity among consumers, the heterogeneity in risk perception. Speci…fically, consumers employ distinctive probability weighting functions to assess the likelihood of risky events. This aspect of consumers’' behaviour highlights the importance that the probabilistic risk attitudes within the RDU framework, such as optimism and pessimism, have for the choice of insurance contract. The analysis yields a separating equilibrium, with full insurance for a sufficiently pessimistic decision maker. An important implication of this result is that any low-risk individual who sufficiently overestimates his probability of loss will induce the uninformed insurer to o¤er him full coverage, thereby, affecting the high-risk type adversely. This outcome is consistent with the recent empirical puzzle regarding the correlation between ex-post risk and insurance coverage, according to which, agents with low exposure to risk receive a larger amount of compensation. By providing an explanation of this pattern of individual behaviour, the current work demonstrates that theory and practice of insurance demand can be reconciled to a greater extent. The paper also provides a behavioural rationale for policy intervention in the market with RDU agents, where the initial distortions in contracts due to unobservable risks are aggravated by the non-linear weighting of probability of a risky event.

The main objective of this thesis is to investigate whether multivariate models using Highfrequency data provide significantly more accurate forecasts of Value at Risk and Expected Shortfall than multivariate models using only daily data. Our objective is very topical since the Basel Committee announced in 2013 that is going to change the risk measure used for calculation of capital requirement from Value at Risk to Expected Shortfall. The further improvement of accuracy of both risk measures can be also achieved by incorporation of high-frequency data that are rapidly more available due to significant technological progress. Therefore, we employed parsimonious Heterogeneous Autoregression and its asymmetric version that uses high-frequency data for the modeling of realized covariance matrix. The benchmark models are chosen well established DCC-GARCH and EWMA. The computation of Value at Risk (VaR) and Expected Shortfall (ES) is done through parametric, semi-parametric and Monte Carlo simulations. The loss distributions are represented by multivariate Gaussian, Student t, multivariate distributions simulated by Copula functions and multivariate filtered historical simulations. There are used univariate loss distributions: Generalized Pareto Distribution from EVT, empirical and standard parametric distributions. The main finding is that Heterogeneous Autoregression model using high-frequency data delivered superior or at least the same accuracy of forecasts of VaR to benchmark models based on daily data. Finally, the backtesting of ES remains still very challenging and applied Test I. and II. did not provide credible validation of the forecasts.

This thesis aplied tools of economic analysis on situation in poker, where players choose to finish tournament or make a deal and take certain amount of money immediately. Theoretical frame consists of economic theories for decesion under risk and poker literature. Hypotheses are tested with regresion analysis on dataset which I obtain by my own observing. Estimations support hypothesis that players are risk-averze and loss-averze. In bigger tournaments are bigger prizepools and that is the reason, why players in bigger tournament make deals more often. Moreover deal is more likely, when players are approximetly equal skilled in poker.

<div> <br></div><div> We consider the behavior of zeros of random polynomials of the from</div><div> \begin{equation*}</div><div> P_{n,m}(z) := \eta_0\varphi_m^{(m)}(z) + \eta_1 \varphi_{m+1}^{(m)}(z) + \cdots + \eta_n \varphi_{n+m}^{(m)}(z)</div><div> \end{equation*}</div><div> as \( n\to\infty \), where \( m \) is a non-negative integer (most of the work deal with the case \( m =0 \) ), \( \{\eta_n\}_{n=0}^\infty \) is a sequence of i.i.d. Gaussian random variables, and \( \{\varphi_n(z)\}_{n=0}^\infty \) is a sequence of orthonormal polynomials on the unit circle \( \mathbb T \) for some Borel measure \( \mu \) on \( \mathbb T \) with infinitely many points in its support. Most of the work is done by manipulating the density function for the expected number of zeros of a random polynomial, which we call the intensity function.</div>

The portfolio theory is microeconomic discipline which deals with the exploration of capital markets and assets that are traded on them. This diploma thesis is focused on optimal stock portfolio selection. The main aim is to find a final portfolio fulfilling the requirements. The first part provides the theory needed for the subsequent establishment of a practical case of the optimal portfolio. The second part is devoted to the actual calculations leading to finding the portfolio with the desired rate of return.

This thesis deals with methods of stochastic programming and their application in financial investment. Theoretical part is devoted to basic terms of mathematical optimization, stochastic programming and decision making under uncertainty. Furter, there are introduced basic principles of modern portfolio theory, substantial part is devoted to risk measurement techniques in the context of investment, mostly to the methods Value at Risk and Expected shortfall. Practical part aims to creation of optimization models with an emphasis to minimize investment risk. Created models deal with real data and they are solved in optimization software GAMS.

We consider tail empirical processes for long memory stochastic volatility models with heavy tails and leverage. We show a dichotomous behaviour for the tail empirical process with fixed levels, according to the interplay between the long memory parameter and the tail index; leverage does not play a role. On the other hand, the tail empirical process with random levels is not affected by either long memory or leverage. The tail empirical process with random levels is used to construct a family of estimators of the tail index, including the famous Hill estimator and harmonic moment estimators. The limiting behaviour of these estimators is not affected by either long memory or leverage. Furthermore, we consider estimators of risk measures such as Value-at-Risk and Expected Shortfall. In these cases, the limiting behaviour is affected by long memory, but it is not affected by leverage. The theoretical results are illustrated by simulation studies.

We model Normal Inverse Gaussian distributed log-returns with the assumption of stochastic volatility. We consider different methods of parametrization of returns and following the paper of Lindberg, [21] we assume that the volatility is a linear function of the number of trades. In addition to the Lindberg’s paper, we suggest daily stock volumes and amounts as alternative measures of the volatility. As an application of the models, we perform Value-at-Risk and Expected Tail Loss predictions by the Lindberg’s volatility model and by our own suggested model. These applications are new and not described in the literature. For better understanding of our caluclations, programmes and simulations, basic informations and properties about the Normal Inverse Gaussian and Inverse Gaussian distributions are provided. Practical applications of the models are implemented on the Nasdaq-OMX, where we have calculated Value-at-Risk and Expected Tail Loss for the Ericsson B stock data during the period 1999 to 2004.

Background: Since the emergence of Covid-19 has it reaped and created havoc within every segment of society on a national and global scale. The financial market experienced significant declines and losses but some asset items handled the fluctuations better than others. Moreover, since some asset items are associated with different risk levels will various investors with contrasting risk attitude allocate dissimilar proportion of their disposable capital between these alternatives. Especially during low and high levels of economic uncertainty which is related to the volatile market of Covid-19. Although, little to no research has been conducted aimed at understanding how Covid-19 impacted Swedish students asset allocation prior and post the pandemic with different risk profiles.   Purpose: The purpose of this study is to investigate if students with different risk attitudes (risk-preference, risk-neutral and risk-averse) conduct statistically different asset allocation prior and post the Covid-19 pandemic. Furthermore, investigate shifts in asset holdings prior and post the pandemic. Moreover, in order to fill the identified literature gap and add to the current body of knowledge regarding asset allocation and variability concerning risk attitudes since its exclusion of Swedish student’s risk attitudes and impact of Covid-19 on preferable asset items.                                    Method: This investigative study concerns a quantitative survey of 81 different students attending Jönköping University. The survey was structured in a way to uncover whether students with different risk attitudes conduct asset allocation statistically different prior and post the Covid-19 pandemic. Moreover, incorporate sociodemographic factors of students in order to measure its relation to risk attitudes and uncertainty changes. This will be done through non-parametric tests (distribution free) such as the Chi-square, Kruskal-Wallis and Bonferroni adjusted p-value approach. The data is later discussed and interpreted through various academic sources and in the context of the frame of reference (expected utility theory).                              Conclusion: The impact of Covid-19 resulted into increased asset allocation of less risky and “safe” asset in order to deal with the declining stock market and future economic uncertainty. The study also suggest that students liquidated some of their current/fixed deposits and re-invested their disposable capital into a more conservative money management strategy, which was a continuous identified pattern.  Furthermore, the results indicate that students with different risk attitudes conduct significantly different asset allocation concerning commercial insurance, stocks/funds and various bond types prior to Covid-19. However, post the eruption has the statistical identified differences in bonds asset allocation reduced which refers to that the statistical power and dissimilar allocated proportion amongst asset items has diminished. Further multiple comparison reinsures this conclusion. Thusly, the study implies that the differences between asset allocation and student risk profiles are diminished post Covid-19 and therefore students perceived and allocated more similar capital proportions into various asset items. Hence answer the initial stated research question and empirically state that risk attitude of students impact how they conduct asset allocation prior to and to a lesser extent post Covid-19

This study examines the impact of U.S. states’ staggered adoption of the inevitable disclosure doctrine (IDD) on rival companies’ auditor choice. I posit that, in states where the IDD limits employee mobility among rival companies, the IDD adoption exogenously increases the costs of disclosing proprietary information through other channels. I find that on average peer companies do not show any changes in the probability of audit office sharing after the companies’ headquarter states adopt the IDD. I also find that companies with trade secrets respond to IDD adoption by avoiding audits conducted by the same audit office as their competitors’ audit office, supporting the proprietary cost hypothesis. The results are robust not only in various levels of auditor sharing but also after I incorporate factors including Mergers and Acquisitions, SOX, and differentiations of IDD adoption and rejection. Cross-sectional results related to Big N auditors suggest that peer companies with trade secrets that hire Big N auditors increase audit office sharing because Big N auditors’ higher levels of reputation, higher litigation costs, and deep pockets alleviate concerns of potential information leakage through audit office sharing in the post IDD adoption periods. My cross-sectional results related to audit committee experts show that peer companies with trade secrets respond to IDD adoption by engaging in more frequent audit office sharing when they have industry experts and accounting financial experts on audit committees. Supervisory financial expertise on audit committees of peer companies with trade secrets does not seem to affect the probability of audit office sharing after the IDD adoption. To my knowledge, this study is the first to document the causal effect of proprietary information costs on audit office choices of U.S. companies with trade secrets. / Business Administration/Accounting