Sjur Westgaard

Default Probabilities in a Corporate Bank Portfolio: A Logistic Model Approach

Anaysis and management of credit risk has taken on an increased importance in recent years. New regulations force banks and other financial institutions to make a credible effort to chart and manage risk associated with their client portfolio. Increased competation in the financial market has also improved the motivation of monitoring the risk/reward relationship on various clients. This article present a method of estimating default probabilities for a retail bank portfolio. The analysis is based on a logistic regression model where financial variables as well as other firm characteristics affect the default probability.

Capital Structure Across Industries

Abstract The purpose of this article is to examine the capital structure across different industries for companies quoted on a stock exchange and headquartered in the United States. The paper demonstrates significant difference in the capital structure depending on the industry where the company operates. The debt ratio sensitivities to the explanatory variables differ significantly between the five industries studied. Almost every significant coefficient obtained in our regressions is in accordance with capital structure theory and other studies. Debt ratio is negatively related to profitability, growth, and age, while asset structure and company size are positively related. However, the debt ratio of the 50 largest companies in the sample is negatively related to company size, which gives support to a currency hedging hypothesis.

Investigating the Capital Structure of UK Real Estate Companies

This paper investigates determinants of capital structure in 308 UK real estate companies. The data panel consists of accounting data from the fiscal years 1998–2006. By using panel data regression we find the significant factors influencing the capital structure of the selected companies. Profitability, tangibility and size are positively related to leverage, while asset turnover and earnings variability are negatively related. The significant positive relation of profitability contradicts major findings in the capital structure literature. Both the static trade‐off theory and the pecking order theory are supported by the signs of the determinants, however the former corresponds most. A supplementary finding is that UK real estate companies face large adjustment costs.

Renewable energy and electricity prices: indirect empirical evidence from hydro power

Many countries have introduced policies to stimulate the production of electricity in a sustainable or renewable way. Theoretical and simulation studies provide evidence that the introduction of renewable energy promotion policies lead to lower electricity prices as sustainable energy supply as wind and solar have very low or even zero marginal costs. Empirical support for this result is relatively scarce. The motivation for this study is to provide additional empirical evidence on how the growth of low marginal costs renewable energy supply such as wind and solar influences power prices. We do so indirectly studying Nord Pool market prices where hydro power is the dominant supply source. We argue that the marginal costs of hydro production varies depending on reservoir levels that determine hydro production capacity. Hydro power producers have an option to produce or to delay production and the value of the option to delay increases when the reservoir levels decrease and the option to delay decreases in value when reservoir levels increase and producers face the risk of spillovers. Hence, an increase in reservoir levels mimics the situation of an increase of low marginal costs renewable energy in a market. Our results show that higher reservoir levels, more hydro capacity, lead to significant lower power prices. From this we conclude that an increase in low marginal costs renewable power supply reduces the power prices. The second contribution of this paper is that we develop a market clearing price model by modelling the supply curve of power that varies over time depending on fundamentals such as hydro capacity and the prices of alternative power sources and that deals with maximum prices which apply to all power markets that we know. With our result, we strengthen support for the view that an increase in wind and solar supply lowers the power price. This is good news for consumers, but it increases the costs of sustainable energy policies such as feed-in tariffs and at the same time lowers revenues and profits for power producers in case governments would abandon such policies. This effect makes the economic and policy support for renewable energy less sustainable. Policy makers have to account for this if they want to stimulate a sustainable growth of sustainable energy supply.

Time-varying dependency in European energy markets: an analysis of Nord Pool, European Energy Exchange and Intercontinental Exchange energy commodities

In this paper we investigate the extent to which the price of Nordic electricity derivatives correlates with European Energy Exchange (EEX) and Intercontinental Exchange (ICE) electricity contracts. We also include their price correlation with ICE gas, Brent crude oil, coal and carbon emission contracts. Using multivariate generalized autoregressive conditional heteroskedasticity models, we find significant time-varying relationships between all of the energy commodities included in the analysis, with the exception of oil. This suggests that pricing models based on constant correlation may be misleading. We also find that Nordic energy futures exhibit the strongest relationship with German electricity futures contracts traded in the EEX, and there appears to be a stronger relationship between longer maturity contracts in all markets.

Prediction of extreme price occurrences in the German day-ahead electricity market

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 electricity markets.

The Forecasting Power of Medium-Term Futures Contracts

This study investigates whether weekly futures prices, covering the time period 1996-2013, are unbiased predictors of future spot price in the Nordic power market. The results give no clear evidence of bias in the futures prices, except for during the winter period from 2003-2009. In this period the futures prices overshoot the spot price, resulting in a positive risk premium. We find a significant premium during winter and fall when analyzing the whole sample. There is no evidence of a premium during summer. Dividing the sample into two sub periods, 1996-2005 and 2006-2013, we find the highest and most significant risk premium during winter in the first sub period. In the latter period, there is less evidence of a significant risk premium.

Hedge Fund Performance in Bull and Bear Markets: Alpha Creation and Risk Exposure

This article investigates whether hedge funds create abnormal risk-adjusted returns, during both bull and bear markets. The model applied is an extended multi-factor model, using a dataset consisting of hedge fund return series with data from a 15-year period ranging from 1994 to 2009. The whole set, as well as bull and bear subperiods, has been analyzed. The authors apply a slightly different model from that used in previous studies and use a comprehensive database that includes the recent financial crisis. A limited amount of the hedge fund literature distinguishes between performance during bull markets and bear markets. The authors find that most hedge fund strategies reduce their exposure to the equity markets during adverse market conditions and invest more in commodities. The inclusion of global macro and managed futures funds in an investor’s portfolio offers a good hedge for bear market conditions.

Modelling day ahead Nord Pool forward price volatility: Realized volatility versus GARCH models

Traditionally, and still within electricity futures/forward markets, daily data has been utilized as the unit of analyses when modelling and making predictions of volatility. However, over the recent past it is argued that better volatility estimates can be obtained by using standard time series techniques on non-parametric volatility measures constructed from high-frequency intradaily returns. Liquidity in financial electricity markets has increased rapidly over the recent years, which make it possible to apply these relatively new methods for measuring market volatility. In this paper high-frequency data and the concept of realized volatility is utilized to make day ahead predictions of Nord Pool forward price volatility. Such short term volatility predictions are especially important for operators and other participants in the electricity sector. We compare the results obtained from standard time-series techniques with the more traditional GARCH-framework which utilizes daily returns. Additionally, we examine whether different approaches of decomposing the total variation into a continuous — and jump measure improves the model fit or not. The paper provides new insights to how the financial electricity market at Nord Pool works, and how we efficiently can model and make predictions of the price movements in this market.

Forecasting gas component prices with multivariate structural time series models

Predicting gas component prices over different horizons is important for both energy producers and consumers. In this study, we model and predict the joint dynamics of butanes, propane and naphtha traded in the north European market. Our approach is to use multivariate time series with unobservable components. We applied monthly data over a 10-year period, from 1995 to 2006, and tested the predictive power of fitted models using various hold out samples. The in-sample and out-of-sample results indicated that gas component prices follow stochastic processes with trend and autoregressive effects that continuously change over time while the seasonal patterns seem to be stationary. The prediction results were compared with random walk for one-step and multi-step forecasts in each of the out-of sample periods. The results are promising and indicate that our model can be used for short-/medium term forecasting of gas component prices.