Um dos objetivos deste estudo é testar modelos de precificação de ativos financeiros, especialmente o de risco downside de Ang et al. (2006), em todas as ações da Bovespa, para o período que se estende de janeiro de 1999 a julho de 2012. Para atingi-lo, aplica-se o método de regressões Fama e MacBeth (1973) com retornos um período à frente. A quase totalidade dos modelos testados é rejeitada, inclusive a existência de um eventual prêmio para o risco downside. A exceção é o modelo que inclui com o beta tradicional e o seu quadrado, o que permite rejeitar o CAPM devido a não linearidade no risco de mercado. A relação existente entre o beta e o retorno das ações seria positiva até beta igual a 0,642 e depois negativa. Outra meta desta dissertação é comparar as estimações condicionais às não condicionais do modelo CoVaR de Adrian e Brunnermeier (2011) para as 16 ações da Bovespa utilizadas por Almeida et al. (2012), que obtiveram apenas estimações não condicionais para o Brasil em um período semelhante. Os resultados daqui mostram uma baixa e não estatisticamente significante correlação com os de Almeida et al. (2012). Para este estudo, tem-se que as duas formas de calcular o CoVaR são similares para o teste de estresse, mas não para o risco sistêmico. / This research pursues as an objective to test cross-sectional returns of some asset pricing models, specially the downside risk suggested by Ang et al. (2006). To accomplish this goal, all the Brazilian Bovespa\'s stocks are used, from January 1999 to July 2012, in one month forward returns Fama-MacBeth regressions. Not only the downside risk model is rejected: almost all models, including the traditional CAPM and versions of the 3 factors Fama-French. A nonlinear CAPM (beta and beta squared) is the exception in the universe of tested models, which produces the best predictions and a positive relationship between betas and forward returns until beta equals 0,642, after this value, the relationship becomes negative. Another issue followed by this study is to compare conditional estimates of the CoVaR model of Adrian and Brunnermeier (2011) with the unconditional ones for the sixteen stock used by Almeida et al. (2012) unconditionally estimates. The results show low and not statistically significant correlation with Almeida\'s estimates. For the sample used here, comparing the conditional and the unconditional methodologies suggests a great similarity for the stress test, but not so close results for the systemic risk.

Blockchain technology has received tremendous attention during the last decade. Huge investments incentives have been made into Blockchain technology and companies worldwide are adapting the new modern innovation. Advocates for Blockchain technology claims that the safe and transparent distributed decentralized ledger has the potential to transform entire industries. One of the biggest operational risks for financial institutions is risks associated with cyber security and cybercrimes. It is argued that Blockchain technology should reduce possibilities for cyber-attacks, increase transparency, and reduce risk. No previous research has been found to confirm this research proposition with perspective to stock return. Still, there remain uncertainties regarding how Blockchain technology affects individual businesses, operational activities and stock behaviours. This research gap is aimed to be partly bridged with this thesis in a Swedish setting.  The primary purpose with this study is therefore to study if the introduction of Blockchain technology in Swedish corporations have an impact of stock return volatility. The longitudinal research methodology of this thesis is designed to satisfy a deductive, quantitative research design, with objectivist ontological assumptions and epistemological positivist approach to generate axiological value-free results. Multiple Linear Regressions and Panel data regression have been performed as well as t-tests to test two hypotheses with regard to systematic risk and total risk as measurements for historical volatility of returns.   The primary findings show a non-significant slight reduction for total risk of stock return, and a slight increase in the systematic risk of stock return. Using mathematical set theory one can argue that the unsystematic risk of stock return decreases. This has proven to be in line with previous theoretical research suggestions which states that operational risk should be reduced. However, the effects observed through the statistical procedures are quite small. Thus, this could indicate that investors’ perceptions of Blockchain technology are still associated with negative issues.  Financial theories such as asymmetry of information, adverse selection, signalling, risk-return fundamentals and behavioural aspects of finance are applied to describe the results, together with previous research, to use the theoretical framework in a coherent way. More research is emphasized to further explore this phenomenon, in order to draw generalizable, significant conclusions though different geographical contexts and markets.  <img src="blob:https://umu.diva-portal.org/2dd6dec1-d82a-4a8e-8093-18a24087fcb2" />

This thesis investigates and compares the performance and characteristics of defensive and cyclical sectors on the Swedish stock market during 2003-2020 and the financial crisis in2007-2008, taking monthly price developments from nine sectors. The purpose is to examine the differences in sector performances based on the estimations of systematic risk. Using the relationship between risk and return, we aim to find the most beneficial investment strategy for investors with a long-term investment horizon and provide knowledge to investors who may want to change investment schemes during stock market crises to protect their portfolios from risk. To determine the sectors' classifications, the beta coefficient from CAPM is used. Moreover, alpha and Sharpe ratios are used as performance measures with the aim to find evidence of differences in performance between the classifications. The results show that beta is inconstant over time, and sectors behave differently depending on their dependence to business conditions, demonstrated by different patterns in beta for the two different classifications when comparing the crisis to the full period. The empirical evidence indicates that a defensive investment strategy is beneficial when considering the relationship between risk and return.

We develop a framework to quantify credit risks of non-traditional mortgage products (NMPs). Ex ante probabilities of default are caused by willingness-to-pay and ability-to-pay problems and the high default rates for NMPs confirm that payment shock is a critical default risk indicator. Monte Carlo simulations are conducted using three correlated stochastic variables (mortgage interest rate, home price, and household income) under normal and stressed economies. Results confirm that the default risk of 2/28 and option ARM contracts requiring a minimum monthly interest payment have a greater probability of default than other mortgage products in all economic scenarios. Additionally, the credit risk of NMPs is primarily systematic risk, suggesting that these products should require higher risk-based capital. Due to the non-linear distribution of credit risk, even the advanced internal-based rating approach of the Basle II framework can understate the risk involved in these NMPs.

We develop a framework to quantify credit risks of non-traditional mortgage products (NMPs). Ex ante probabilities of default are caused by willingness-to-pay and ability-to-pay problems and the high default rates for NMPs confirm that payment shock is a critical default risk indicator. Monte Carlo simulations are conducted using three correlated stochastic variables (mortgage interest rate, home price, and household income) under normal and stressed economies. Results confirm that the default risk of 2/28 and option ARM contracts requiring a minimum monthly interest payment have a greater probability of default than other mortgage products in all economic scenarios. Additionally, the credit risk of NMPs is primarily systematic risk, suggesting that these products should require higher risk-based capital. Due to the non-linear distribution of credit risk, even the advanced internal-based rating approach of the Basle II framework can understate the risk involved in these NMPs.

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The stock market is a highly complex adaptive system as different entities interact, operate and change states due to a specific trading behavior they follow. For that reason, the dynamics that can be found there change over time due to these actions. However, when systematic risks like COVID-19 take place these dynamics are altered. In this master thesis, an agent-based modelis constructed that simulates the interaction between investors and five companies with the purpose of investigating how its dynamics are affected. This agent-based model is constructed by using the environment of Python. Additionally, another purpose of this study is to compare a financial index like OMSX30 to a constructed financial index given by the agent-based model. Lastly, according to the findings of this study, a constructed index made with the corresponding model does not fully capture the dynamics of OMSX30. However, there are some similarities in terms of trends.

Corporate governance practices of fintech companies have caused to increase in risk or caused to decrease in the risks. This study is mainly focused to identify the impact of corporate governance mechanisms, especially board structure and ownership structure, on the market-based risk of fintech companies. We have employed several corporate governance mechanisms such as, board size, board independence, board expertise on fintech, CEO duality, risk committee functioning, institutional ownership, and managerial ownership of the fintech companies. Total risk, systematic risk, market risk,and insolvency risk are employed as our dependent variables to examine this phenomenon. We have selected 46 listed fintech companies that are listed in any stock market of the world. Data is collected through 2012-2022 period. We have conducted our analysis using 369 unbalanced panel datasets. Our purpose was to emphasize the importance of better corporate governance mechanisms to risk management in fintech companies. From the management point of view, investors’ point of view, or directors’ point of view, what changes should do to better risk management of the company and also their personal benefit? In the recent past, two bluechip fintech companies have bankrupt due to corporate governance mispractices and risk management issues. Our results show that, corporate governance is one of the key factors in determining risk of the fintech companies. We have identified that the best practices caused to decrease risk while mispractices caused to increase risk.

Investing has become increasingly popular among individuals in recent years,this has led to multiple investing strategies formalizing. One of them being factorinvesting, a strategy where investors search for companies with certain strongfirm specific financial metrics through screening. Many researchers try to findwhich these metrics are, and which of them has an effect on the cross-sections ofstock returns. This study examines the relationship between the three metrics,earnings-to-price, dividend yield, debt over equity and the European stock marketbetween January 2010 to December 2022. This is done by using the two-stageregression model suggested by Fama and Macbeth (1973). Our results show thatthere is an anomaly in the European stock market and that there is a firmcharacteristic risk associated with these metrics. This suggests that when lookingat individual firms, investors are willing to pay a premium for the metrics studiedin this paper and it is therefore important to take them into account whenscreening for individual companies. As the previous research is mainly focusedon the American stock market and emerging markets in Europe, our thesis fills agap by providing a view on factor premiums in the European market as a whole.

I propose a global risk factor – Currency Traded Risk (CTR). This risk factor is the first to identify the directional link between currencies and equities. CTR captures the genesis of financial globalization, and contains the greatest predictive ability to date for monthly returns on a global stock portfolio. Theoretically, return expectation is intimately linked to time-varying risk premia. Due to the intrinsic scope of currency values in integrating the world’s financial markets, information on time-varying risk premia prices into currencies at greater speed, scale, and global consensus, relative other asset classes. High interest rate currencies proxy as a risk-on asset class. Low interest rate currencies proxy as a risk-off asset class. Innovations in these currencies’ values summarize global risk premia and forecast equity market returns. CTR measures two sources of global risk premia; the difference between averaged spot returns of high interest rate currencies and low interest rate currencies, and the difference between implied and realized volatility of high interest rate currencies. Using recursive regressions, CTR predicts monthly MSCI World Index© returns out of sample, with R2’s consistent at 10% from 2008 to 2017. Currencies track global risk premia, whereas equities respond to it.