The dynamics of hourly electricity prices

The dynamics of hourly electricity prices in day-ahead markets is an important element of competitive power markets that were only established in the last decade. In electricity markets, the market microstructure does not allow for continuous trading, since operators require advance notice in order to verify that the schedule is feasible and lies within transmission constraints. Instead agents have to submit their bids and offers for delivery of electricity for all hours of the next day before a specified market closing time. We suggest the use of dynamic semiparametric factor models (DSFM) for the behavior of hourly electricity prices. We find that a model with three factors is able to explain already a high proportion of the variation in hourly electricity prices. Our analysis also provides insights into the characteristics of the market, in particular with respect to the driving factors of hourly prices and their dynamic behavior through time.Power Markets, Dynamic Semiparametric Factor Models, Day-ahead Electricity Prices

The Dependence Structure between Carbon Emission Allowances and Financial Markets - A Copula Analysis

This paper applies different copulas in order to investigate the complex dependence structure between EU emission allowance (EUA) futures returns and those of other commodities, equity and energy indices. The analysis yields important insights into the relationship between carbon, commodities and financial markets. First of all, we find a significant relationship between EUA returns and those of the other considered variables that is most appropriately modeled by a Gaussian and Student-t copula. These results contradict some earlier studies that report no statistically significant or even negative correlations between returns of emission allowances and other financial variables. Secondly, considering time-varying copulas shows that the estimated copula parameters are not constant over time. We find in particular that the dependence is stronger during the period of the financial crisis. In a Value-at-Risk (VaR) analysis, finally, we further illustrate the advantages of copula methods. In particular the Student-t copula provides an appropriate quantification of VaR at different confidence levels while other models fail to specify the risk correctly. This analysis shows that ignoring the actual nature of dependence might lead to an underestimation of the risk for portfolios combining EUAs with commodities or equity investments.CO2 emission trading, commodity markets, copula models, dependence structure

Convenience Yields for CO2 Emission Allowance Futures Contracts

In January 2005 the EU-wide CO2 emissions trading system (EU-ETS) has formally entered into operation. Within the new trading system, the right to emit a particular amount of CO2 becomes a tradable commodity - called EU Allowances (EUAs) - and affected companies, traders and investors will face new strategic challenges. In this paper we investigate the nature of convenience yields for CO2 emission allowance futures. We conduct an empirical study on price behavior, volatility term structure and correlations in different CO2 EUA contracts. Our findings are that the market has changed from initial backwardation to contango with significant convenience yields in future contracts for the Kyoto commitment period starting in 2008. A high fraction of the yields can be explained by the price level and volatility of the spot prices. We conclude that the yields can be interpreted as market expectation on the price risk of CO2 emissions allowance prices and the uncertainty of EU allocation plans for the Kyoto period.CO2 Emission Trading, Commodity Markets, Spot and Futures Prices, Convenience Yields.

Estimation of operational value-at-risk in the presence of minimum collection threshold: An empirical study

The recently finalized Basel II Capital Accord requires banks to adopt a procedure to estimate the operational risk capital charge. Under the Advanced Measurement Approaches, that are currently mandated for all large internationally active US banks, require the use of historic operational loss data. Operational loss databases are typically subject to a minimum recording threshold of roughly $10,000. We demonstrate that ignoring such thresholds leads to biases in corresponding parameter estimates when the threshold is ignored. Using publicly available operational loss data, we analyze the effects of model misspecification on resulting expected loss, Value-at-Risk, and Conditional Value-at-Risk figures and show that underestimation of the regulatory capital is a consequence of such model error. The choice of an adequate loss distribution is conducted via in-sample goodness-of-fit procedures and backtesting, using both classical and robust methodologies. --

Evaluation of investment options mitigating catastrophic losses under the impacts of climate change

It is of significant concern that climate change will exaggerate the frequency and severity of extreme events such as: floods, storms, droughts and bushfires. As the value of properties under risk increases due to economic growth, also the probability of catastrophic events may be amplified by climate change impacts. Thus, there is a need for local governments to invest in adaptation measures in order to reduce potential losses from these catastrophic events. However, economic models that help local governments to evaluate those investment projects are currently lacking. Two challenges are faced when evaluating these projects. First, it is difficult to quantify the risk due to the lack of observations on catastrophic events at the local level. Second, investment costs are often lumpy and the investment decisions are irreversible, so that the investment strategy based on the net present value (NPV) criterion is not optimal. Under the uncertain growth of the stock of assets and the uncertain impacts of climate change, the optimal timing of investments into adaptation strategies that reduce catastrophic risks is of major importance. This paper presents a simple economic framework to quantify climate change risks and a real option approach to illustrate the optimal timing of investment strategies for local governments.7 page(s

Modelling and Forecasting Volatility in the Gold Market

We investigate the volatility dynamics of gold markets. While there are a number of recent studies examining volatility and Value-at-Risk (VaR) measures in financial and commodity markets, none of them focuses on the gold market. We use a large number of statistical models to model and then forecast daily volatility and VaR. Both in-sample and out-of-sample forecasts are evaluated using appropriate evaluation measures. For in-sample forecasting, the class of TARCH models provide the best results. For out-of-sample forecasting, the results were not that clear-cut and the order and specification of the models were found to be an important factor in determining model’s performance. VaR for traders with long and short positions were evaluated by comparing failure rates and a simple AR as well as a TARCH model perform best for the considered back-testing period. Overall, most models outperform a benchmark random walk model, while none of the considered models perform significantly better than the rest with respect to all adopted criteria

Estimation of operational value-at-risk in the presence of minimum collection threshold: An empirical study

61 page(s

The Term Structure of Credit Spreads and Credit Default Swaps - an Empirical Investigation

42 page(s