Cyriel de Jong

Gas Storage Valuation Using a Monte Carlo Method

Storage in the energy business is a major issue. Electricity can hardly be stored at all, while crude oil is best “stored” by not pumping it out of the ground in the first place. Natural gas occupies a kind of middle position. In the natural gas market, storage facilities which permit smoothing over time of the gas they supply to the market are an important component of the supply chain. Optimal management of a storage facility entails dealing with both physical constraints and fluctuating demand. This, in turn, gives rise to contingencies that can be thought of as American options. The valuation of the facility as an investment entails solving for the expected present value of future profits, assuming optimal management. In this article, the authors show how to address this difficult valuation problem by adapting the numerical methods for pricing American options using Least Squares Monte Carlo simulation to the technology of gas storage

The Nature of Power Spikes: A Regime-Switch Approach

Due to its non-storable nature, electricity is a commodity with probably the most volatile spot prices, exemplified by occasional spikes. Appropriate pricing, portfolio, and risk management models have to incorporate these characteristics, and the spikes in particular. We investigate the nature of power spikes in a number of different markets. We test what time-series model is best able to capture the dynamics of these disruptive spot prices. We use regime-switching models to infer whether the price spikes should be treated as abnormal and independent deviations from the ‘normal’ price dynamics or whether they form an integral part of the price process. We test the time-series models on day-ahead markets in Europe and the US. We find that regimeswitch models are better able to capture the market dynamics than a GARCH(1,1) or Poisson jump model. We also find clear differences between the markets and attribute part of the differences to the share of hydro-power in the total supply stack: hydro-power serves as an indirect means to store electricity, which has a dampening effect on spikes.Due to its non-storable nature, electricity is a commodity with probably the most volatile spot prices, exemplified by occasional spikes. Appropriate pricing, portfolio, and risk management models have to incorporate these characteristics, and the spikes in particular. We investigate the nature of power spikes in a number of different markets. We test what time-series model is best able to capture the dynamics of these disruptive spot prices. We use regime-switching models to infer whether the price spikes should be treated as abnormal and independent deviations from the ‘normal’ price dynamics or whether they form an integral part of the price process. We test the time-series models on day-ahead markets in Europe and the US. We find that regimeswitch models are better able to capture the market dynamics than a GARCH(1,1) or Poisson jump model. We also find clear differences between the markets and attribute part of the differences to the share of hydro-power in the total supply stack: hydro-power serves as an indirect means to store electricity, which has a dampening effect on spikes.

Stock Market Quality in the Presence of a Traded Option

We use a controlled economic experiment to examine the implications of asymmetric information for informational linkages between a stock market and a traded call option on that stock. The setting is based on the Kyle model and Back (1993). We find that an insider trades aggressively in both the stock and the option, and that this leads to important feedback effects between the two markets: price discovery in the stock market also occurs in the option market and vice versa. The time series properties of the stock price depend directly on the intrinsic value of the option: when the intrinsic value of the option is positive, informational efficiency is higher in the market for the stock, and volatility is lower. We argue that this provides new insights into how the introduction of a traded option improves the market quality of the underlying asset.

Gas storage valuation using a multifactor price process

In this paper we discuss an extension to a popular gas storage valuation method called the spot approach. Least-Squares Monte Carlo, which is the basis for the spot approach, allows for multi-factor price processes. Such price processes can capture more realistically the actual price behavior present in energy markets. In this paper we demonstrate the application of multi-factor Least-Squares Monte Carlo to gas storage valuation. We study the impact of using multi-factor price processes on different aspects of the valuation such as convergence, average storage value and distribution of storage values in a numerical example. We find a counter example to the idea that an increase in market volatility leads to an increase in storage value. As well, we find a counter example to the idea that the natural hedging strategy of the spot approach is no hedge: a simple static financial hedge can reduce the inherent risk of the spot approach. Finally, we study the impact of model error related to the price process.