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This thesis offers an explanation of the statistical modelling of corporate financial indicators in the context where the life of a company is terminated. Whilst it is natural for companies to fail or close down, an excess of failure causes a reduction in the activity of the economy as a whole. Therefore, studies on business failure identification leading to models which may provide early warnings of impending financial crisis may make some contribution to improving economic welfare. This study considers a number of bankruptcy prediction models such as multiple discriminant analysis and logit, and then introduces survival analysis as a means of modelling corporate failure. Then, with a data set of UK companies which failed, or were taken over, or were still operating when the information was collected, we provide estimates of failure probabilities as a function of survival time, and we specify the significance of financial characteristics which are covariates of survival. Three innovative statistical methods are introduced. First, a likelihood solution is provided to the problem of takeovers and mergers in order to incorporate such events into the dichotomous outcome of failure and survival. Second, we move away from the more conventional matched pairs sampling framework to one that reflects the prior probabilities of failure and construct a sample of observations which are randomly censored, using stratified sampling to reflect the structure of the group of failed companies. The third innovation concerns the specification of survival models, which relate the hazard function to the length of survival time and to a set of financial ratios as predictors. These models also provide estimates of the rate of failure and of the parameters of the survival function. The overall adequacy of these models has been assessed using residual analysis and it has been found that the Weibull regression model fitted the data better than other parametric models. The proportional hazard model also fitted the data adequately and appears to provide a promising approach to the prediction of financial distress. Finally, the empirical analysis reported in this thesis suggests that survival models have lower classification error than discriminant and logit models.

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<p>In the Black-Scholes model, the volatility considered being deterministic and it causes some</p><p>inefficiencies and trends in pricing options. It has been proposed by many authors that the</p><p>volatility should be modelled by a stochastic process. Heston Model is one solution to this</p><p>problem. To simulate the Heston Model we should be able to overcome the correlation</p><p>between asset price and the stochastic volatility. This paper considers a solution to this issue.</p><p>A review of the Heston Model presented in this paper and after modelling some investigations</p><p>are done on the applet.</p><p>Also the application of this model on some type of options has programmed by MATLAB</p><p>Graphical User Interface (GUI).</p>

In the Black-Scholes model, the volatility considered being deterministic and it causes some inefficiencies and trends in pricing options. It has been proposed by many authors that the volatility should be modelled by a stochastic process. Heston Model is one solution to this problem. To simulate the Heston Model we should be able to overcome the correlation between asset price and the stochastic volatility. This paper considers a solution to this issue. A review of the Heston Model presented in this paper and after modelling some investigations are done on the applet. Also the application of this model on some type of options has programmed by MATLAB Graphical User Interface (GUI).

The purpose of this diploma thesis is to clarify partial techniques of real estate income approach valuation. The object of this thesis is to create a complex overview of valuation techniques and methods used in income approach valuation. First, basic methodologies from field of market economy, valuation, financial mathematics and statistics, will be defined. In next section of this thesis, the income approach valuation methods and techniques will be defined as well as contribution of financial modelling to this approach. Following, several properties will be generalized in terms of cash flow inflows and outflows and exact techniques will be applied accordingly.

I takt med ökat intresse för investeringar i vindkraft behöver aktörer på marknaden tillgång till mer information om vilka faktorer som kan påverka dessa investeringars förmåga att generera intäkter. Eftersom intäkter kan genereras genom att strukturera sin försäljning på olika sätt behöver intressenter erhålla bättre förståelse kring varje strukturs förmåga att generera intäkter över tid. I detta examensarbete har vi undersökt aggregerade intäkter och estimerade riskmått över ett år för tre försäljningsstrukturer: försäljning till Spotmarknaden, genom Baseload PPA och genom As-produced PPA. För att göra detta har vi försökt replikera fördelnings- och tidsserie-egenskaper för vindhastigheter, spotpriser och balanseringspriser. Försöken visar lovande replikerbarhet för vindhastigheter, och ganska lovande replikerbarhet för spotpriser och balanseringspriser.  Vi visar att högre förväntade aggregerade intäkter uppvisar även tendens till högre estimerad risk. Den försäljningsstruktur som visar högst förväntade aggregerade intäkter var försäljning till Spotmarknaden, följt av Baseload PPA och lägsta intäkter erhölls genom As-produced PPA. Liknande rangordning sammanfattar estimerade riskmått, där försäljning till Spotmarknaden visade högst Value-at-Risk, följt av Baseload PPA och lägsta risken estimerades i As-produced PPA. Slutligen drar vi även slutsatsen att As-produced PPA är den största riskmitigeraren av de två olika PPA alternativen.  Vår studie har genomförts under många avgränsningar och antaganden som behöver förstås ordentligt innan slutsatser från denna studie används. Några av avgränsningarna är att vi antar stationäritet i spotpriser, tar ingen hänsyn till förluster i kraftproduktion till följd av typ av landyta, undersöker endast ett vindkraftsverk och vi tar inte hänsyn till förluster i transmission eller kostnader från Nord Pool. Dessutom undersöker vi försäljningsprocessen för vindkraftsproducent som säljer all elektricitet själv och som inte har tillgång till handel genom Intradagsmarknaden. En ytterligare begränsning i arbetet är att vi inte har mätdata för historiska vindhastigheter vid en faktiskt planerad vindkraftspark, utan för en mätmast som är inom ett visst avstånd från några vindkraftsparker. Eftersom positionering av vindkraftsturbiner är extremt viktigt påverkar detta våra resultat då historiska vindhastigheter vid mätmasten möjligtvis inte är tillräckligt attraktiva för många investerare. Efter utvärdering av våra replikerade tidsserie rekommenderar vi vidare forskning inom området att ta hänsyn till icke-stationäritet vid skapande av modell för spotpriser. / As interest for wind power investment increases, market participants require access to more information regarding which factors that can affect the income generating capacities of their investments. Since income can be generated through various structures of selling electricity, stakeholders need better understanding regarding the income generating capacity of each structure over time. In this thesis we have examined aggregated income and estimated risk measures over one year for three different methods of selling electricity: selling to the Nord Pool Spot market, selling through Baseload PPA and selling through As-produced PPA. For the examination we have tried to replicate distributional and time series attributes for wind speeds, spot prices and regulating (balancing) prices. Our results demonstrate promising replicability for wind speeds, and quite promising replicability for spot prices and balancing prices.  We show that higher expected aggregated income also implies higher estimated risk. Highest expected aggregated income was generated from selling to the Market, followed by Baseload PPA and lowest income was generated for As-produced PPA. Estimated risk measures followed the same pattern, where selling to the Market demonstrated highest Value-at-Risk, followed by Baseload PPA and the lowest estimated risk was found in As-produced PPA. Finally we also conclude that As-produced PPA is the largest mitigator of risk between the two examined PPA alternatives.  Our study has been conducted under many delimitations and assumptions which need to be thoroughly understood before utilizing conclusions from this study. Some assumptions we take are stationarity in spot prices, no loss in power production due to type of surface surrounding the wind turbine, we only used one turbine and we assumed no losses in transmission nor any costs from Nord Pool. Furthermore, our study examines how electricity is sold from a wind power producer who sells all their electricity by themselves and who also does not have access to trading through the Intraday market. Another limitation in the study is that we do not have measurement data from actual wind farm locations, but data from a publicly available source that is within an undisclosed vicinity of some wind farms. Since positioning of wind power turbines is very important to maximize potential output, this limitation will affect our results since our used wind data might not be attractive enough for many potential investors. We finally recommend further study within the field, especially researching ways to handle lack of stationarity in spot prices.

Since the global financial crisis of 2008, regulatory bodies worldwide have implementedincreasingly stringent requirements for measuring and pricing default risk in financialderivatives. Counterparty Credit Risk (CCR) serves as the measure for default risk infinancial derivatives, and Credit Valuation Adjustment (CVA) is the pricing method used toincorporate this default risk into derivatives prices. To calculate the CVA, one needs the risk-neutral Probability of Default (PD) for the counterparty, which is the centre in this type ofderivative.The traditional method for calculating risk-neutral probabilities of default involves constructingcredit curves, calibrated using the credit derivative Credit Default Swap (CDS). However,liquidity issues in CDS trading present a major challenge, as the majority of counterpartieslack liquid CDS spreads. This poses the difficult question of how to model risk-neutral PDwithout liquid CDS spreads.The current method for generating proxy credit curves, introduced by the Japanese BankNomura in 2013, involves a cross-sectional linear regression model. Although this model issufficient in most cases, it often generates credit curves unsuitable for larger counterpartiesin more volatile times. In this thesis, we introduce two Long Short-Term Memory (LSTM)models trained on similar entities, which use CDS spreads as input. Our introduced modelsshow some improvement in generating proxy credit curves compared to the Nomura model,especially during times of higher volatility. While the result were more in line with the tradedCDS-market, there remains room for improvement in the model structure by using a moreextensive dataset. / Ända sedan 2008 års finanskris har styrande finansiella organ ökat kraven för mätning ochprissättning av konkursrisk inom derivat. Ett område av särskilt högt intresse för detta arbete ärmotpartskreditrisker (CCR). I detta är Kreditvärdesjustering (CVA) den huvudsakliga metodenför prissättning av konkursrisk inom finansiella derivat och för att kunna få fram ett värde avCVA behövs en risk-neutral konkurssannolikhet (PD).En av de traditionella metoderna för att räkna ut denna sannolikhet är genom att skapakreditkurvor som sedan är kalibrerade utifrån CDS:ar. Detta handlade derivat (CDS) finns baraför ett mindre antal företag över hela världen vilket gör att en majoritet av marknaden saknaren tillräckligt handlad CDS. Lösning på detta är att ta fram proxy CDS för ett motsvarande bolag.Idag görs detta framförallt med en tvärsnitts-regressionsmodell som introducerades 2013 avden japanska banken Nomura. Den skapar i många fall rimliga kurvor men ett problem den harär att den oftare gör proxyn lägre än vad den borde vara.I detta arbete introducerar vi istället en LSTM modell som tränas på liknande företag. Resultatetav detta är att vi får en bättre modell i många fall för att skapa en proxy kurva men som delvishar liknande brister som Nomura modellen. Men med fortsatta undersökningar inom områdetsamt med mer data kan detta skapa en mer exakt och säkrare proxy modell.

Today’s carbon constrained world with its increasing demand for cheap energy and a fossil fuel intensive fleet of power producers is making carbon capture and storage (CCS) desirable. Several CCS technologies are under investigation by various research and development groups globally. One of the more promising technologies is oxy-fuel combustion, since it produces a CO2 rich flue gas which requires minor processing to meet storage condition requirements. In this study the economics of an advanced super critical oxy-coal power plant burning lignite, simulated in-house was assessed. A robust and user-friendly financial tool box has been developed with commonly acceptable default parameter settings. Capital, operation and maintenance costs were estimated along with corresponding levelized cost of electricity and CO2 avoidance costs calculated using the detailed financial model developed. A levelized cost of electricity of 131 $/MWhrnet along with a levelized CO2 avoidance cost of 64 $/tonne was estimated for an ASC oxy-coal power plant with CO2 capture. Also a levelized cost of electricity of 83 $/MWhrnet was estimated for an ASC air-fired coal power plant without CO2 capture capabilities as the base plant. The price of electricity was observed to increase from 83 $/MWhrnet to 131 $/MWhrnet translating into a 57% increase. The sensitivity of the overall economics of the process was assessed to several parameters. The overall economics was found sensitive to the choice chemical engineering plant cost index (CEPCI), capacity factor, size of power plant, debt ratio, fuel price, interest rate, and construction duration.

Today???s carbon constrained world with its increasing demand for cheap energy and a fossil fuel intensive fleet of power producers is making carbon capture and storage (CCS) desirable. Several CCS technologies are under investigation by various research and development groups globally. One of the more promising technologies is oxy-fuel combustion, since it produces a CO2 rich flue gas which requires minor processing to meet storage condition requirements. In this study the economics of an advanced super critical oxy-coal power plant burning lignite, simulated in-house was assessed. A robust and user-friendly financial tool box has been developed with commonly acceptable default parameter settings. Capital, operation and maintenance costs were estimated along with corresponding levelized cost of electricity and CO2 avoidance costs calculated using the detailed financial model developed. A levelized cost of electricity of 131 $/MWhrnet along with a levelized CO2 avoidance cost of 64 $/tonne was estimated for an ASC oxy-coal power plant with CO2 capture. Also a levelized cost of electricity of 83 $/MWhrnet was estimated for an ASC air-fired coal power plant without CO2 capture capabilities as the base plant. The price of electricity was observed to increase from 83 $/MWhrnet to 131 $/MWhrnet translating into a 57% increase. The sensitivity of the overall economics of the process was assessed to several parameters. The overall economics was found sensitive to the choice chemical engineering plant cost index (CEPCI), capacity factor, size of power plant, debt ratio, fuel price, interest rate, and construction duration.