Wolfgang Härdle (Humboldt-Universität zu Berlin)
Title: Pricing Chinese Rain
Abstract: Many industries are exposed to weather risk which they can transfer to financial markets via weather derivatives. Financial investors and weather exposed business are interested in holding a basket of weather derivatives to manage their risks in an optimal way. We develop a model for pricing such baskets of customized WDs on multiple dependent geographical sites. In our setup, market participants account for spatial dependence in the underlying weather indexes. Dynamic portfolio optimization under market clearing and utility indierence of the agents determines equilibrium quantity and price for weather derivatives. Using the concept of association, we compare our multi-site model to its single-site counterpart. We give an example on how to price rainfall derivatives on Chinese provinces in the universe of a financial investor and weather exposed crop insurance companies.
Fred Espen Benth (University of Oslo)
Title: A theory for cointegration in commodity markets
Abstract: According to the arbitrage theory, cointegration of financial assets will not impact pricing of spread options, as the risk-adjusted returns will be given by the risk-free interest rate. We analyse the similar situation in commodity markets, where typically the market consists of spot, forward/futures and options on these. We show that cointegration in the spot will impact the volatility of forward/futures price dynamics, with the results that spread options indeed will depend on cointegration. Our analysis leads to a Heath-Jarrow-Morton approach with cointegration for forward/futures price modelling in commodity markets.
The talk is based on joint work with Steen Koekebakker, University of Agder.
Sjur Westgaard (Norwegian University of Science and Technology, Trondheim)
Title: Value at risk modelling using exponential weighted moving average volatility with quantile regression
- An analysis of ICE, EEX, and Nasdaq OMX Energy Futures Markets -
Abstract: Correct modelling and forecasting of risk for energy futures markets has become an important issue for power companies, industrial companies, and financial institutions exposed to oil, gas, coal, electricity, and carbon markets. As participants in this market have both long and short positions, it is important to model both sides of the tails of the return distributions. It is likely important to take into account how the return distributions changes over time due to changing volatility and market conditions. The problems with existing “standard” risk model such as Riskmetrics (TM) and Historical simulation are that the former do not capture the changes in the return distribution as the conditional volatility changes, and that the latter have the opposite problem capturing the return distribution but not conditional upon volatility. More advanced GARCH models with different error distributions and CaViaR models where the quantiles itself is modelled as an autoregressive process improves the forecast out of sample but are only to a limited extend used by market participants due to the complexity of estimating these models. In this paper we propose a robust and easy to implement approach for Value at Risk estimation based on first running an exponential weighted moving average volatility model (similar to Riskmetrics TM) and then running a linear quantile regression model based with this volatility as input. The model display excellent in-sample and out-of sample performance compare to a set of benchmark models. We use a wide range of European energy futures markets from ICE, EEX; and Nasdaq OMX covering the period 2006-2013. Each of these markets have very distinct features with different volatility dynamics and distributional characteristics that are captured by the method. The method capture the best of the existing approaches (Riskmetrics and Historical Simulation) and are easy to implement for market risk assessment.
Acknowledgements: I would like to thank the ELCABORBONRISK project for financial support to conduct this research as well as helpful comments from academic and industry contacts.
Luca Taschini (London School of Economics)
Title: Pollution permits, Strategic Trading and Dynamic Technology Adoption
Authors: Santiago Moreno-Bromberg and Luca Taschini
Abstract: This paper analyses the dynamic incentives for technology adoption under a transferable permits system, which allows for strategic trading on the permit market. Initially, firms can both invest in low-emitting production technologies and trade permits. In the model, technology adoption and allowance prices are generated endogenously and are inter-dependent. It is shown that the non-cooperative permit trading game possesses a pure-strategy Nash equilibrium, where the allowance value reflects the level of uncovered pollution (demand), the level of unused allowances (supply), and the technological status. These conditions are also satisfied when a price support instrument (dubbed cash-for-permits), which is contingent on the adoption of the new technology, is introduced. Numerical investigation confirms that this policy generates a floating price floor for the allowances, and it restores the dynamic incentives to invest. Given that this policy comes at a cost, a criterion for the selection of a self-financing policy (based on convex risk measures) is proposed and implemented.