We investigate the relationship between advertising and research and development (R&D) expenditures with the firm¡¦s systematic and unsystematic risks. Our data covers from January 1981 to December 2007 with more than two thousand publicly listed firms in the New York Stock Exchange. In addition to classical least squares approach, we utilize quantile regression model to examine whether the estimated slope parameters vary across different quantiles of the conditional distribution of the firm¡¦s systematic risk and unsystematic risk. We generate six empirical generalizations. (1) Advertising is significantly associated with lower systematic risk for firms with lower, median and higher systematic risk, but with no significant effects on the firms with extremely low systematic risk. (2) R&D is significantly associated with higher systematic risk for firms with median and higher systematic risk, with no significant effect for those with lower systematic risk. (3) Advertising is significantly associated with lower unsystematic risk for firms with higher unsystematic risk, but with no significant effects for those with median and lower unsystematic risk. (4) R&D is significantly associated with higher unsystematic risk for firms with median and higher unsystematic risk, with no significant effect for those with lower unsystematic risk. (5) Our evidence shows that both advertising and R&D have a stronger effect on firms with higher systematic risk (unsystematic risk) than on those with lower systematic risk (unsystematic risk). (6) Moreover, our evidence suggests that advertising and R&D tests resoundingly support our hypothesis that the coefficients vary across the quantiles.

Utilizing the industry portfolio classifications that Fama and French provide in their data library, I analyze the specific effects that the 1933 Bank Holiday has on various industries. My empirical results go beyond what Silber (2009) determines to be significantly positive abnormal market returns on March 15, 1933, which is the day after the Bank Holiday and the largest ever one-day increase in the stock market. I use the CAPM and the Fama-French 3-factor Model to find significant systematic risk decreases after the Bank Holiday in the Coal and Transportation industries, as well as systematic risk increases in Consumer Goods and Apparel. To determine the driving factors behind these changes in systematic risk and abnormal returns, I test the correlation between industry leverage ratios and differences in systematic risk changes after the Bank Holiday. The Bank Holiday helps stabilize the economy and the nation’s banking system, which I expect industries with larger debt obligations will benefit more after the Bank Holiday. Inconsistent with my expectations, I don’t find significant evidence that the systematic risks of highly leveraged industries decreases more than industries with lower leverage ratios. I develop my argument to leave room for changes in the model used to estimate systematic risks in order to identify the variables that are the true drivers of the systematic risk changes that I observe.

Asset pricing models such as the CAPM calls for the estimation of beta as a measure of the systematic risk. Using historical betas as an input to portfolio analysis requires the assumption of beta stationarity. The existing literature on beta dynamics suggest a somewhat high dispersion of the beta persistence across stocks. In previously unexplored territory, this study aims to investigate factors associated with the degree of beta persistence. By using a sample of 237 U.S. stocks with daily returns observed over the period 1984 to 2015, yearly stock betas were estimated using a GARCH / Maximum Likelihood framework. Autocorrelation properties of these beta series was then crosssectionally regressed on five hypothesized determining variables. Product type as well as the absolute value of beta was found to have a significant effect on the first-order autocorrelation of beta.

The purpose of this paper is to analyse the acquiror’s post M&A systematic risk using the stock performance evaluation by determining the change in beta (post M&A and Pre-M&A). and further determine the impact and significancy of M&A characteristics from the previous studies that have an explanatory value that may lead to a change in the systematic risk.

This research assessed market efficiency, volatility behaviour, asset pricing, and oil price risk exposure of the oil and gas companies quoted on the London Stock Exchange with the aim of providing fresh evidence on the pricing dynamics in this sector. In market efficiency analysis, efficient market hypothesis (EMH) and random walk hypothesis were tested using a mix of statistical tools such as Autocorrelation Function, Ljung-Box Q-Statistics, Runs Test, Variance Ratio Test, and BDS test for independence. To confirm the results from these parametric and non-parametric tools, technical trading and filter rules, and moving average based rules were also employed to assess the possibility of making abnormal profit from the stocks under study. In seasonality analysis, stock returns were tested for the day-of-the-week and month-of-the-year effects. Volatility processes, estimation, and forecasting were undertaken using both asymmetric and symmetric volatility models such as GARCH (1,1) and Threshold ARCH or TARCH (1,1,1) to investigate the volatility behaviour of stock returns. To determine the effect of an exogenous variable on volatility, Brent crude oil price was used in the models formulated as a variance regressor for the assessment of its impact on volatility. The models were then used to forecast the price volatility taking note of the forecasting errors for the determination of the most effective forecasting model. International oil price risk exposure of the oil and gas sector was measured using a multi-factor asset pricing model similar to that developed by Fama and French (1993). Factors used in the asset pricing model are assessed for statistical significance and relevance in the pricing of oil and gas stocks. Data used in the study were mainly the adjusted daily closing prices of oil and gas companies quoted on the exchange. Five indices of FTSE All Share, FTSE 100, FTSE UK Oil and Gas, FTSE UK Oil and Gas Producers, and FTSE AIM SS Oil and Gas were also included in the analysis. Our findings suggest that technical trading rules cannot be used to gain abnormal returns, which could be regarded as a sign for weak form market efficiency. The results from seasonality analysis have not shown any day-of-the-week or monthly effect in stock returns. The pattern of stock returns’ volatility can be estimated and forecasted, although the relationship between risk and return cannot be generalised. On a similar note, the relationship between volatility attributes and the efficient market hypothesis cannot be clearly established. However, we have established that volatility modelling can significantly measure the quantum of risk in the oil and gas sector. Market risk, oil price risk, size and book-to-market related factors in asset pricing models were found to be relevant in the determination of asset prices of the oil and gas companies.

The recent U.S. subprime mortgage crisis rapidly spread throughout the world and put the global financial system under extraordinary pressure. The main implication of the recent crisis is that complex banking regulations failed to adequately identify and limit riskiness of banking systems at both domestic and international levels. In spite of a large empirical literature on the causes and remedies of the recent crisis, there remains substantial uncertainty on (i) how risk measuring models performed during crisis, (ii) how systematic factors such as house prices affected the financial system, and (iii) how effectively government policy responses resolved the financial crisis. This thesis seeks to narrow this gap in the literature by offering three empirical essays. The first essay investigates the performance of alternative parametric VaR models in forecasting riskiness of international equity portfolios. Notably, alternative univariate VaR models are compared to multivariate conditional volatility models with special focus given to conditional correlation models. Conditional correlation models include the constant conditional correlation (CCC), dynamic conditional correlation (DCC), and asymmetric DCC (ADCC) models. Various criteria are then applied for backtesting VaR models and to evaluate their one-day-ahead forecasting ability in a wide range of countries and during different global financial conditions. It is found that most VaR models have satisfactory performance with small number of violations during pre-crisis period. However, the number of violations, mean deviation of violations, and maximum deviation of violations dramatically increase during crisis period. Furthermore, portfolio models incur lower number of violations compared to univariate models while DCC and ADCC models perform better than CCC models during crisis period. From risk management perspective, most single index models fail to pass Basel criteria for internal VaR models during crisis period, whereas empirical evidence on the choice between CCC, DCC, and ADCC models is mixed. The recent crisis also raised serious concerns about factors that can systematically destabilise the whole banking system. In particular, the collapse of house prices in the United States triggered the recent subprime mortgage crisis, which was associated with a sharp increase in the number of nonperforming loans and bank failures. This in turn demonstrates the key role that house prices play in systematically undermining the whole banking system. The second essay investigates the determinants of nonperforming loans (NPL) with a special focus on house price fluctuations as a key systematic factor. Using a panel of U.S. banking institutions from 1999 to 2012, the analysis is carried out across different loan categories, different types of banks, and different bank size. It is found that house price fluctuations have a significant impact on the evolution of nonperforming loans, while the magnitude of their impact varies across loan categories, institution types, and between large and small banks. Also, the impact of house price fluctuations on nonperforming loans is more pronounced during crisis period. The last essay of this thesis investigates the effectiveness of the U.S. government strategy to combat the crisis. As a comprehensive response to the recent financial crisis, the US government created the Troubled Asset Relief Program (TARP). The Capital Purchase Program (CPP) was launched as an initial program under the TARP. The CPP was designed to purchase preferred stocks or equity warrants from viable financial institutions. Using a large panel of the U.S. commercial banks over the period 2007Q1 to 2012Q4, survival analysis is used to investigate the impact of TARP funds on the likelihood of survival in the recipient banks. It is found that larger recipient banks are more likely to avoid regulatory closure, while receiving capital assistance does not effectively help banks to avoid technical failure. This implies that governmental capital assistance serves larger banks much better than their smaller counterparts. In addition, TARP recipients are more likely to be acquired, regardless of their size and financial health. In summary, the empirical findings reveal that capital infusions do not enhance the survival likelihood of the recipient banking institutions.

The airline industry is a major user of jet fuel and this constitutes a large component of the operating costs and is a risk coefficient for airlines. Several studies have been conducted on how oil price volatility affect stock prices and cash flows as well as how, in general, firms that uses derivatives experience lower stock returns volatility and stock s .The impact of oil price volatility on airline stock s and the impact of hedging on airline stock s have not been adequately examined, this paper fills this gap. By gathering daily frequency of oil spot prices to access the quarterly oil price volatility and stock s from 16 European airlines, we correlate quarterly oil price volatility to quarterly airline stock s as well as stock s and hedging percentages between 2010-2015, we reject the hypothesis that oil price volatility has an impact on airline stock s and that hedging reduces stock s. These findings therefore suggest that oil price volatility do not have a large impact on systematic risks or that hedging offset systematic risks. The findings are of interest to investors who want to make well informed investment decisions based on non-diversifiable equity risk since it has become popular for management recently to implement hedging policies to signal competency in risk management in order to attract investments.

This paper analyzes the return connectedness between the biotechnology sector and other financial assets for 1 January 2000 to 31 December 2018, using an empirical approach from both time- and frequency-domain. The results reveal that the connectedness between the biotechnology sector and other financial assets are decreasing with time, entailing high diversification opportunities in the long-run. Our results also suggest that the spillover effect from the biotechnology sector is higher than the spillover effect to the biotechnology sector, proposing that the sector affects other financial assets to a greater extent than they affect the biotechnology sector. Concurrently, we found that the net directional connectedness is negative for the sector, which means that it does not transmit shocks to others since it is not subject to significant return or volatility shocks. This implies that the systematic risk connected to the biotechnology sector is lower than previous studies argue for. Thus, our main finding is that investments in the sector has safe haven properties, indicating that they are independent towards other sectors. By investing in the biotechnology sector, investors contribute to society and supports the R&D, leading to development of vital drugs. In light of this, we argue that investments in the sector are socially beneficial. Building on these insights, investments in the biotechnology sector are of importance when investing in a prosperous world.

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.

Olja har under det förgångna seklet varit en av industrialiseringens stöttepelare. Idag, med omfattande satsningar inom hållbar utveckling så är inverkan av oljan högt aktuellt och inom en snar framtid kan den se en påtaglig nedbringa även om det har visats att dess relevans kommer kvarstå åtminstone fram till 2040. Tidigare forskning har påvisat att fluktuationer i oljepriset är en bidragsgivare till de systematiska risker företag ställs inför dagligen. Denna studie utvidgade analysområdet genom att välja ut länder med en netto-import av olja och sortera de på den andel relativa oljetillförsel som nationen erhållit gentemot nivån av systematisk risk från oljeprisfluktuationer som företagen ställs inför. Analysen utfördes över 120 Finansiella företag i 12 europeiska länder. Det anträffades utpräglade mönster i studiens resultat som kan antyda en koppling mellan dessa variabler, men resultaten återfinns i majoritet till att inte uppnå statistisk signifikans. Vidare kan studiens modell utgöra en bas för vidare forskning inom området.