A vast amount of literature on hedonic price modelling has been formulated on overseas property markets. Very little currently exists in South Africa and this poses a risk for sellers and estate agents of a residential property when listing it on the open market, as this could result in an extended list period, reducing the original asking price. This paper seeks to examine Gauteng’s West Rand residential property market and formulate a multi-variate regression model to best predict property prices, determined by a property’s structural characteristic. The research tracks residential sales from 1996 to 2009, a thirteen-year sample period from which a composite property index, to account for inflation and real house price growth, has been formalised. Correlation and regression analysis was used to interpret the data at the relevant significance level. In order to account for locational attributes present in property values, the data set was divided into locational quadrants and run as dummy variables. A further regression was run on a screened data set to create an ordinary least squares equation that could be used to show the relationship between property values and structural characteristics. The results indicated a good fit with an R2 of 69.5%. This regression was then applied practically to predict property prices for houses that have transacted in the West Rand property market, and plotted along a value/price graph using the 45-degree true value frontier line. The relevant results were then interpreted, and recommendations given.

In this thesis the Black Scholes and the Heston stock prices are investigated and the models are compared. The Black Scholes model assumes that the volatility is constant, while the Heston model allows stochastic volatility which is more flexible and can perform better with empirical data. Both models are analysed and simulated, and the parameters are estimated based on empirical data of S&P 500. Results are based on simulations and characteristic functions which are presented with figures of probability density functions.

In September 2017, Luleå Energi introduced a new price model on district heating for their enterprise customers with the intention to create stronger incentives for energy efficiency measures, and provide price signals that better correspond to the underlying production costs. The purpose of this thesis is to investigate whether the intentions of Luleå Energi has been fulfilled, not the least in the form of improved energy efficiency among the affected enterprises. This thesis also provides a theoretical assessment of the new price model through conducting a literature review. The empirical analysis builds on theories addressing issues such as bounded rationality, profit maximizing firms, uncertainty, and imperfect information. The results show that the new price model may have been hindered by bounded rationality amongst the enterprise customers, and the overall low price level of district heating services in Luleå. Results also show that even if the new price model is fairly good at creating incentives, it could probably become more effective if the effect price component were based on enterprise customers current effect usage rather than the enterprise customers effect usage during the previous winter season. / I september 2017 introducerade Luleå Energi en ny prismodell för deras fjärrvärme tjänster gentemot företagskunder med intentionen att skapa starkare incitament för energieffektiviseringsåtgärder och förse marknaden med prissignaler som motsvarar de underliggande produktionskostnaderna. Syftet med denna uppsats är att undersöka huruvida Luleå Energi’s intentioner har blivit uppfyllda, inte minst i form av energieffektiviseringsåtgärder. Studien kommer också att, med hjälp av en litteraturstudie, genomföra en teoretisk bedömning av den nya prismodellen. Studien applicerar teorier som begränsad rationalitet, vinstmaximerande företag, osäkerhet och ofullständig information. Resultaten visar att den nya prismodellen eventuellt hindras av begränsad rationalitet hos kunderna, och den överlag låga prisnivån på fjärrvärme i Luleå kommun. Resultaten visar också att även om den nya prismodellen är någorlunda bra på att skapa incitament så skulle den troligen kunna bli effektivare genom att basera effektpriskomponenten på företagskundernas nuvarande effektanvändning istället för företagskundernas effektanvändning under den föregående vintersäsongen.

The thesis is focused on selected risks and theirs management in power sector. In introduction it is analysed power sector in the context of other energy commodities, hereafter it is analysed the approach to power price modelling. Furthermore are analysed the aspects of regulatory risk in Czech Republic. Finally it is evaluated the risk management system of a company in power- utility sector.

The Autonomic Power System (APS) grand challenge was a multi-disciplinary EPSRC-funded research project that examined novel techniques that would enable the transition between today's and 2050's highly uncertain and complex energy network. Being part of the APS, this thesis reports on the sub-project 'RR2: Avoiding High-Impact Low Probability events'. The goal of RR2 is to develop new algorithms for controlling risk exposure to high-impact low probability (Hi-Lo) events through the provision of appropriate risk-sensitive control strategies. Additionally, RR2 is concerned with new techniques for identifying and modelling risk in future energy networks, in particular, the risk of Hi-Lo events. In this context, this thesis investigates two distinct problems arising from energy risk management. On the one hand, we examine the problem of finding managerial strategies for exercising the operational flexibility of energy assets. We look at this problem from a risk perspective taking into account non-linear risk preferences of energy asset managers. Our main contribution is the development of a risk-sensitive approach to the class of optimal switching problems. By recasting the problem as an iterative optimal stopping problem, we are able to characterise the optimal risk-sensitive switching strategies. As byproduct, we obtain a multiplicative dynamic programming equation for the value function, upon which we propose a numerical algorithm based on least squares Monte Carlo regression. On the other hand, we develop tools to identify and model the risk factors faced by energy asset managers. For this, we consider a class of models consisting of superposition of Gaussian and non-Gaussian Ornstein-Uhlenbeck processes. Our main contribution is the development of a Bayesian methodology based on Markov chain Monte Carlo (MCMC) algorithms to make inference into this class of models. On extensive simulations, we demonstrate the robustness and efficiency of the algorithms to different data features. Furthermore, we construct a diagnostic tool based on Bayesian p-values to check goodness-of-fit of the models on a Bayesian framework. We apply this tool to MCMC results from fitting historical electricity and gas spot price data- sets corresponding to the UK and German energy markets. Our analysis demonstrates that the MCMC-estimated models are able to capture not only long- and short-lived positive price spikes, but also short-lived negative price spikes which are typical of UK gas prices and German electricity prices. Combining together the solutions to the two problems above, we strive to capture the interplay between risk, uncertainty, flexibility and performance in various applications to energy systems. In these applications, which include power stations, energy storage and district energy systems, we consistently show that our risk management methodology offers a tradeoff between maximising average performance and minimising risk, while accounting for the jump dynamics of energy prices. Moreover, the tradeoff is achieved in such way that the benefits in terms of risk reduction outweigh the loss in average performance.