No description available.

The main objective of this thesis is to provide an empirical assessment of the popular methodologies for modelling the underlying spot price dynamics in energy markets. After a brief introduction in the alternative forms of derivation that may be used for speculative and risk management purposes in energy markets, we assess the performance of the standard Black's framework in modelling energy prices. For the first time in the literature we use a powerful and realistic data set which covers oil, gas and electricity markets and tests the appropriateness of the Geometric Brownian Motion process to explain the observed dynamics of the spot prices in these markets. We also provide spreadsheet based computer algorithms to price popular energy derivatives based on the Geometric Brownian Motion specifications. In Chapter-3 we try to accommodate observed stylised facts in the spot price behaviour, namely mean reversion and jumps. For the first time in the literature we test a jump diffusion model, and a mean reversion jump diffusion model against our broad data set and compare the findings to the Black's Geometric Brownian Motion specifications. In Chapter-4 we use a forward curve approach as an alternative-modelling framework to the spot price models. Based upon an almost proprietary data set of historical forward curves, we determine the number of independent factors that are needed to model the forward curve's dynamic evolution. After carrying out principal component analysis on historical forward data a threefactor-model emerges as the most appropriate for energy markets in general. The first factor being the volatility (level effect), the second the smile and the third sesonality. Finally in Chapter-5 of the thesis we compare the ability of spot models (Jump Diffusion and Mean Reversion Jump Diffusion Model) and forward curve based models to price WTI options. The results show that the Jump Diffusion Model is the best model as the option prices given are very accurate in comparison with the other models and closest to the market observed options prices.

With the growth of alternative investment vehicles such as hedge funds and the resulting search for "new" asset classes, the interest in the commodity market has been growing within the financial sector. The commodity futures markets have been successfully providing a platform for investors and industrial participants as an alternative investment vehicle and a tool for risk management. The storable commodity futures markets are characterised by two distinct market conditions: backwardation and contango, which are directly linked to market fundamentals such as inventory levels and thus influence the price dynamics and functioning of the commodity futures market. While there exists a large body of research in the area of commodity derivatives, research on the linkage between market dynamics and the market conditions as determined by fundamentals is very limited. Accordingly, this thesis aims to investigate the different market dynamics of metal futures markets under these two conditions. The issues under examination include the futures price discovery function, the forecasting performance of the futures price, the long-run cost-of-carry equilibrium and short-run time-varying adjustment, and the price volatility and its relationship with inventory levels and trading volume. The empirical findings suggest, for the first time, that the price discovery function depends on the state of the storable commodity markets: futures prices are found to be upward biased predictors of the future spot prices when the market is in contango and are downward biased when the market is in backwardation. Nonparametric bootstrap simulations confirm that the forecast errors are negative in a backwardation market and are positive in a contango market, and moreover the forecast errors are larger under the former market condition than the latter. The empirical results also show that the price volatility is higher in a backwardation market than in a contango market as indicated by the negative relationship between price volatility and inventory levels. We also show that the spot volatility is generally higher than the futures price volatility and the difference is greater when the inventory level is low. Moreover, the impact of trading volume on the futures price volatility is found to be stronger when the market is in backwardation in some of the markets. In short, the empirical findings in this thesis suggest that the functioning of the metal spot and futures market is dependent on market conditions of which the inventory level is an important indictor as implied by the theory of storage. The empirical findings have strong implications for practitioners (particularly, trading houses, funds and banks) who could potentially form different trading strategies based on the distinct market behaviour under the two market conditions.