Florentina Paraschiv

Understanding the mechanisms that drive extreme negative and positive prices in day-ahead electricity prices is crucial for managing risk and market design. In this paper, we consider the problem of understanding how fundamental drivers impact the probability of extreme price occurrences in the German day-ahead electricity market. We develop models using fundamental variables to predict the probability of extreme prices. The dynamics of negative prices and positive price spikes differ greatly. Positive spikes are related to high demand, low supply and high prices the previous days, and mainly occur during the morning and afternoon peak hours. Negative prices occur mainly during the night and are closely related to low demand combined with high wind production levels. Furthermore, we do a closer analysis of how renewable energy sources, hereby photovoltaic and wind power, impact the probability of negative prices and positive spikes. The models confirm that extremely high and negative prices have different drivers, and that wind power is particularly important in relation to negative price occurrences. The models capture the main drivers of both positive and negative extreme price occurrences and perform well with respect to accurately forecasting the probability with high levels of confidence. Our results suggest that probability models are well suited to aid in risk management for market participants in day-ahead

Day-ahead electricity prices are generally used as reference prices for decisions done in energy trading, e.g. purchase and sale strategies are typically based on the day-ahead spot prices. Therefore, well-performing forecast methods for day-ahead electricity prices are essential for energy traders and supply companies. In this paper, a methodology based on artifficial neuronal networks (ANN) is presented to forecast electricity prices. As the performance of an ANN forecast model depends on appropriate input parameter sets, the focus is set on the selection and preparation of fundamental data that has a noticeable impact on electricity prices. This is done with the help of different cluster algorithms, but also by comparing the results of the pre-selected model configurations in combination with different input parameter settings. After the determination of the optimal input parameters, affecting day-ahead electricity prices, and well-performing ANN configuration, the developed ANN model is applied for in-sample and out-of-sample analyses. The results show that the overall methodology leads to well-fitting electricity price forecasts, whereas forecast errors are lower than other forecast models for electricity prices known from the literature.

We propose a novel regime-switching approach for the simulation of electricity spot prices that is inspired by the class of fundamental models and takes into account the relation between spot and forward prices. Additionally the model is able to reproduce spikes and negative prices. Market prices are derived given an observed price forward curve, and spikes may occur with a certain probability. To this end, we distinguish between a base regime and an upper as well as a lower spike regime. The model parameters are calibrated using the historical hourly price forward curves for EEX Phelix and the dynamics of hourly spot prices. We further evaluate different time series models such as ARMA and GARCH that are usually applied for modeling electricity prices and conclude a better performance of the proposed regime-switching model.

In this paper, we perform stress-testing for a portfolio of commodity futures which mimics the dynamics of the DJ-UBS index. We identify extreme events that impacted commodity prices over time and look at correlation structures in a dynamic way, with copula functions. In line with Basel III financial regulations, we derive baseline, historical, and hybrid scenarios and discussed their advantages and shortfalls. We find that the financialization of commodity markets led to an increase in correlations and in the probability for joint extremes. However, we identify structural breaks in commodity markets that temporarily led to a breakdown of expected statistical patterns and of traditional dependence structures among commodities. This fact shows the need for forward-looking stress testing techniques, like hybrid and hypothetical scenarios, as encouraged by financial regulators.

This paper analyzes the reaction of the S&P 500 returns to changes in implied volatility given by the VIX index, using a daily data sample from 1990 to 2012. We found that in normal regimes increases (declines) in the expected market volatility result in lower (higher) subsequent stock market returns. Thus, investors enter into selling positions upon a perception of increased risk for their equity investments, while they enter into long positions when they perceive an improved environment for those investments. However, for extreme regimes investors' reaction to increasing risk is ambiguous. We found that VIX variation significantly influences investment strategies for holding periods up to one month. Additionally we propose an investment rule for short-term oriented investors.

In the present paper, we present a mid-term planning model for thermal power generation which is based on multistage stochastic optimization and involves stochastic electricity spot prices, a mixture of fuels with stochastic prices, the effect of CO 2 emission prices and various types of further operating costs. Going from data to decisions, the first goal was to estimate simulation models for various commodity prices. We apply Geometric Brownian motions with jumps to model gas, coal, oil and emission allowance spot prices. Electricity spot prices are modeled by a regime switching approach which takes into account seasonal effects and spikes. Given the estimated models, we simulate scenario paths and then use a multiperiod generalization of the Wasserstein distance for constructing the stochastic trees used in the optimization model. Finally, we solve a 1-year planning problem for a fictitious configuration of thermal units, producing against the markets. We use the implemented model to demonstrate the effect of CO 2 prices on cumulated emissions and to apply the indifference pricing principle to simple electricity delivery contracts.

In this paper, we analyze the impact of renewable energies, wind and photovoltaic, on the formation of day-ahead electricity prices at EEX. We give an overview of the policy decisions concerning the promotion of renewable energy sources in Germany and discuss their consequences on day-ahead prices. An analysis of electricity spot prices reveals that the introduction of renewable energies enhances extreme price changes. In the frame of a dynamic fundamental model, we show that there has been a continuous electricity price adaption process to market fundamentals. Furthermore, the fundamental drivers of prices differ among hours with different load profiles. Our results imply that renewable energies decrease market spot prices and have implications on the traditional fuel mix for electricity production. However, the prices for the final consumers increased overall because they must pay in addition the feed-in tariffs for the promotion of renewable energy.

Retail banks usually apply simple linear regression models for describing the dynamics of the deposit rates of non-maturing accounts (NMA) like savings deposits. Thus, typical patterns like asymmetry or rigidity that banks follow when adjusting their deposit rates are ignored. This is insofar surprising, as the asymmetric deposit rate adjustment affects the pricing of embedded options for NMA. In this work we contribute to the elimination of these inconsistencies. Based on data for deposit rates from a representative sample of Swiss banks we provide a strong evidence for both asymmetric adjustment and rigidity pattern. Our proposed modeling approaches reveal that the strategies of Swiss banks to adjust deposit rates are regime dependent. In times of market stress, Swiss banks are tight to market rates; however, in normal regimes this is not observed.

In Basel II the regulators stress the importance of finding realistic volumes models for non-maturing accounts (NMAs), given their cash-flow uncer- tainty due to optionality. Focusing on Swiss savings accounts, we identify their seasonal pattern and we derive their sensitivity to market rates and to relevant macroeconomic factors. We derive a realistic volumes model, that allows for cash-flow forecasting, which is essential for liquidity management in banks.