Modelling spot prices, risk management, and investment strategies for the energy markets

This thesis addresses the topics of spot price modelling, risk management, and investment applications in the energy markets. Eight of the most important energy markets that trade futures contracts on NYMEX, and one Spot Energy Index (SEI) proposed for the first time in this thesis, are investigated. A new modelling approach is proposed for optimally capturing the behaviour of the energy spot prices, combining a mean-reverting and a spike model that incorporate two different speeds of mean reversion, and time-varying volatility modelled as a GARCH and an EGARCH process. The aforementioned modelling approach is also evaluated in terms of its ability to quantify energy spot price risk by accurately calculating Value-at-Risk (VaR) and Expected Shortfall (ES) measures. A number of commonly used VaR methodologies are evaluated along with various Monte Carlo (MC) simulations based models and a Hybrid Monte Carlo with Historical Simulation (MC-HS) approach, introduced in this thesis for the first time. This thesis also delves into index investment applications for the energy markets that have recently attracted a lot of attention. To that end, the index tracking problem is addressed by applying equity algorithmic trading using two innovative Evolutionary Algorithms (EAs), aiming to replicate the performance of a direct energy commodity investment which is proxied by the constructed spot energy index. The empirical evidence in this thesis shows that the proposed modelling approach can effectively capture the behaviour of the energy spot prices examined, and that it is the most reasonable, efficient, and consistent approach for calculating the VaR of spot energy prices and the SEI, for both long and short positions. Hence, it can be successfully applied for forecasting, risk management, derivatives pricing, and policy development and monitoring purposes. Finally, it is shown that energy commodities, proxied by the SEI, can have equity-like returns as they can be effectively tracked with stock portfolios selected by the investment methodology proposed in this thesis. The latter investment approach can be used by fund managers to set-up energy Exchange Traded Funds that would track the performance of the SEI, giving them the full flexibility of any investment style, long or short, that equities can provide

Can the information content of share repurchases improve the accuracy of equity premium predictions?

We adjust the dividend–price ratio for share repurchases and investigate whether predictive power can be improved when constructing forecasts of the UK and French equity premia. Regulations in the two largest European stock markets allow us to employ actual repurchase data in our predictive regressions. Hence, we are able to overcome problems associated with markets characterised by less stringent disclosure requirements, where investors might have to rely on proxies for measuring repurchase activity. We find that predictability does not improve either in a statistical or in an economically significant sense once actual share repurchases are considered. Furthermore, we employ a proxy measure of repurchases which can be easily constructed in international markets and demonstrate that its predictive content is not in line with that of the actual repurchase data

Freight Derivatives Pricing for Decoupled Mean-Reverting Diffusion and Jumps

We develop an accurate valuation setup for freight options, featuring an exponential meanreverting model for the freight rate with distinct reversion scales for its jump and diffusion components. We calibrate to Baltic option prices and analyze the freight rate dynamics. More specifically, we observe that jumps dissipate faster than the diffusive deviations about the equilibrium level. We benchmark against practitioners’ model of choice, i.e., the lognormal model and variants, and find that our approach reduces the pricing error while preserving analytical tractability and computational competence. We also find that neglecting fast mean-reverting jumps leads to nontrivial option mispricings

Wealth and risk implications of the Dodd-Frank Act on the U.S. financial intermediaries

We contribute to the current regulatory debate by examining the wealth and risk effects of the Dodd- Frank Act on U.S. financial institutions. We measure the effects of key legislative events of the Act by means of a multivariate regression model using the seemingly unrelated regression (SUR) framework. Our results indicate a mixed reaction by financial institutions during the various stages of the Act’s legislative process. Further tests reveal that any positive reactions are driven by small and/or low risk institutions, while negative ones are consistent across subsets; except for investment banks. We also find market risk increases for most financial institutions that are dominated by small and/or low risk firms. The cross-section results reveal that large institutions fare better than their smaller counterparts and that large investment banks gain value at the expense of others. Overall, the Dodd-Frank Act may have redistributed value among financial institutions, while not necessarily reducing the industry’s riskiness

Portfolio optimization and index tracking for the shipping stock and freight markets using evolutionary algorithms

This paper reproduces the performance of an international market capitalization shipping stock index and two physical shipping indexes by investing only in US stock portfolios. The index-tracking problem is addressed using the differential evolution algorithm and the genetic algorithm. Portfolios are constructed by a subset of stocks picked from the shipping or the Dow Jones Composite Average indexes. To test the performance of the heuristics, three different trading scenarios are examined: annually, quarterly and monthly rebalancing, accounting for transaction costs where necessary. Competing portfolios are also assessed through predictive ability tests. Overall, the proposed investment strategies carry less risk compared to the tracked benchmark indexes while providing investors the opportunity to efficiently replic ate the performance of both the stock and physical shipping indexes in the most cost-effective way

Information demand and stock return predictability

Recent theoretical work suggests that signs of asset returns are predictable given that their volatilities are. This paper investigates this conjecture using information demand, approximated by the daily internet search volume index (SVI) from Google. Our results reveal that incorporating the SVI variable in various GARCH family models significantly improves volatility forecasts. Moreover, we demonstrate that the sign of stock returns is predictable contrary to the levels, where predictability has proven elusive in the US context. Finally, we provide novel evidence on the economic value of sign predictability and show that investors can form profitable investment strategies using the SVI

Relative equity market valuation conditions and acquirers’ gains

We examine whether the relative equity market valuation conditions (EMVCs) in the merging firms countries help acquirers’ managers to time the announcements of domestic and foreign target acquisitions. After controlling for several deal- and merging firms-specific features we find that acquisition activity, as well as acquirers gains, are significantly higher during periods of high-EMVCs at home, irrespective of the domicile of the target. We also find that the higher foreign acquirers’ gains that reaped during periods of high-EMVCs at home are realized by deals of targets based in the RoW (=World-G7), rather than G6 (=G7-UK) countries, which is due to the low correlation of EMVCs between the U.K. (home) and the RoW countries. Moreover, acquisition of targets domiciled in the RoW (G6) countries yield higher (lower) gains than domestic targets during periods of high-EMVCs at home. This suggests that the relative EMVCs between the merging firms’ countries allow acquirers’ managers to time the market and acquire targets at a discount, particularly in countries in which acquirers’ stocks are likely to be more overvalued than the targets’ stocks