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Energy consumption in the transport sector is expected to increase substantially over the coming decades. The uncertainty in the forecasts are relatively high regarding the size of the increase but reports from the US Energy Information Administration (EIA) points to an increase of 56 percent between 2010 and 2040 (IEO2013, 2013, p. 9). World Energy Council forecasts an increase between 30 and 85 percent, depending on the impact of various factors such as market regulation, population growth, urbanization, and the availability of fossil energy (Global Transportation Scenarios 2050 (TSG 2050), 2011, p. 4-5). Some of this energy could come from advanced renewable fuels such as advanced renewable biodiesel. The commercialization of advanced renewable biofuels has however been slow even though the technology has long been considered mature for large scale production. External market factors that are frequently blamed for the lack of commercialization are lack of political support, low crude oil prices, high raw material prices, and notable profitability for producers of first generation biofuels. Previous studies further suggest that advanced biofuels are expensive to produce (Demirbas, 2010; Milbrandt et al., 2013) and that the companies operating in the industry hold high financial risks (Miller et al., 2013). This study examines the systemic financial risks as well as the estimated returns that the market places on companies active in the emerging advanced biodiesel industry. The results from the study indicate, contrary to previous studies and current external market factors that the systematic risks are not considerable higher for the advanced biodiesel industry than the market average or the established biofuel industry. This is despite the fact that oil prices have been low, raw materials prices high and that the uncertainty surrounding the political forms of support for the industry during the studied period have increased. This should have resulted in increasing rather than decreasing financial risk in relation to the previous studies of the advanced biofuel sector. Important factors that contribute to the results obtained in this study are circumstances that previous studies have completely disregarded, factors that may be beneficial for the studied companies. The studied firms are showing noteworthy profitability, access to substantial working capital, relatively low ratios between stock prices and cash flows. Furthermore, the analyzed companies have a business structure that other studies so far have completely ignored, e.g. they are structured as "biorefineries". This means the studied firms, similarly to conventional petroleum refineries, are producing and trading various products produced from the same raw material. The difference being that the analyzed firms use renewable raw materials rather than crude oil to produce the commodities. The firms thus possess an "option-based" diversification strategy which may be perceived by the market as a future real option. In contrast to focused firms these firms may simply change their business focus based on changes in prevailing external market factors or decreasing profitability in any part of the company. In accordance with the theories of the effective market hypothesis theory and random walk the market has access to all this available knowledge regarding these firms’ specific factors, pricing the risks on this knowledge these as well as prevailing external market factors. The results of the study suggest that the firm-specific factors in the studied companies may be more important than some of the considered external market factors in the pricing of financial systemic risks.

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This thesis examines the relationship between the development of the financial system and economic growth in the United Kingdom, using a time series econometric methodology. It extends the existing literature in three ways. First, it applies a disaggregated approach, testing the relationship not only at the aggregate level, but also for the manufacturing and service sectors of the UK. This allows the modeling to be driven by the financial characteristics of each sector, thereby providing a firmer foundation for policy recommendations. Second, `fmance-augmented' production functions are estimated throughout, thus yielding coefficients that are theoretically consistent and interpretable. The empirical results suggest that the aggregate economy faces decreasing returns to scale, the manufacturing sector exhibits increasing returns to scale while the service sector appears to display either constant or decreasing returns. Third, both these innovations mean that the study is also able to make a contribution to the on-going sectoral productivity and policy debates in the UK, emphasising the role of finance in this process. The study finds evidence that the evolution of the finance-output relationship in the UK is sector-specific, in that the development of the stock market is positively associated with long-run output, both at the aggregate level and for the manufacturing sector, whereas banking sector development is found to be important for service sector output.

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The main objective of this thesis is to implement stochastic correlation into the existing structural credit risk models. There are two stochastic models suggested for the covariance matrix of the assets' prices. In our first model, to induce the stochasticity into the structure of the correlation, we assume that the eigenvectors of the covariance matrix are constant but the eigenvalues are driven by independent Cox-Ingersoll-Ross processes. To price equity options on this framework we first transform the calculations from the pricing domain to the frequency domain. Then we derive a closed formula for the Fourier transform of the Green's function of the pricing PDE. Finally we use the method of images to find the price of the equity options. The same method is used to find closed formulas for marginal probabilities of defaults and CDS prices. In our second model, the covariance of the assets follows a Wishart process, which is an extension of the CIR model to dimensions greater than one. The popularity of the Heston model, which uses the CIR process to model the stochastic volatility, could be a promising point for using Wishart process to model stochastic correlation. We give closed form solutions for equity options, marginal probabilities of defaults, and some other major financial derivatives. For the calculation of our pricing formulas we make a bridge between two recent trends in pricing theory; from one side, pricing of barrier options by Lipton (2001) and Sepp (2006) and from other side the development of Wishart processes by Bru (1991), Gourieroux (2005) and Fonseca et al. (2006, 2007a, 2007b). After obtaining the mathematical results above, we then estimate the parameters of the two models we have developed by an evolutionary algorithm. We prove a theorem which guarantees the convergence of the evolutionary algorithm to the set of optimizing parameters. After estimating the parameters of the two stochastic correlation models, we conduct a comparative analysis of our stochastic correlation models. We give an approximation formula for the joint and marginal probabilities of default for General Motors and Ford. For the marginal probabilities of default, a closed formula is given and for the joint probabilities of default an approximation formula is suggested. To show the convergence properties of this approximation method, we perform the Monte Carlo simulation in two forms: a full and a partial Monte Carlo simulation. At the end, we compare the marginal and joint probabilities with full and partial Monte Carlo simulations.