Jaroslava Hlouskova

Forecasting electricity spot-prices using linear univariate time-series models

This paper studies the forecasting abilities of a battery of univariate models on hourly electricity spot prices, using data from the Leipzig Power Exchange. The specifications studied include autoregressive models, autoregressive-moving average models and unobserved component models. The results show that specifications, where each hour of the day is modelled separately present uniformly better forecasting properties than specifications for the whole time-series, and that the inclusion of simple probabilistic processes for the arrival of extreme price events can lead to improvements in the forecasting abilities of univariate models for electricity spot prices.

NATURAL DISASTERS AS CREATIVE DESTRUCTION? EVIDENCE FROM DEVELOPING COUNTRIES

Recent studies found a robust positive correlation between the frequency of natural disasters and the long-run economic growth after conditioning for other determinants. This result is interpreted as evidence that disasters provide opportunities to update the capital stock and adopt new technologies, thus acting as some type of Schumpeterian creative destruction. The results of cross-country and panel data regressions indicate that the degree of catastrophic risk tends to have a negative effect on the volume of knowledge spillovers between industrialized and developing countries. Only countries with relatively high levels of development benefit from capital upgrading through trade after a natural catastrophe.

The Performance of Panel Cointegration Methods. Results from a Large Scale Simulation Study

This article presents results concerning the performance of both single equation and system panel cointegration tests and estimators. The study considers the tests developed in Pedroni (1999, 2004), Westerlund (2005), Larsson et al. (2001), and Breitung (2005) and the estimators developed in Phillips and Moon (1999), Pedroni (2000), Kao and Chiang (2000), Mark and Sul (2003), Pedroni (2001), and Breitung (2005). We study the impact of stable autoregressive roots approaching the unit circle, of I(2) components, of short-run cross-sectional correlation and of cross-unit cointegration on the performance of the tests and estimators. The data are simulated from three-dimensional individual specific VAR systems with cointegrating ranks varying from zero to two for fourteen different panel dimensions. The usual specifications of deterministic components are considered.

The efficient frontier for bounded assets

Abstract. This paper develops a closed form solution of the mean-variance portfolio selection problem for uncorrelated and bounded assets when an additional technical assumption is satisfied. Although the assumption of uncorrelated assets is unduly restrictive, the explicit determination of the efficient asset holdings in the presence of bound constraints gives insight into the nature of the efficient frontier. The mean-variance portfolio selection problem considered here deals with the budget constraint and lower bounds or the budget constraint and upper bounds. For the mean-variance portfolio selection problem dealing with lower bounds the closed form solution is derived for two cases: a universe of only risky assets and a universe of risky assets plus an additional asset which is risk free. For the mean-variance portfolio selection problem dealing with upper bounds, the results presented are for a universe consisting only of risky assets. In each case, the order in which the assets are driven to their bounds depends on the ordering of their expected returns.

An algorithm for portfolio optimization with transaction costs

We consider the problem of maximizing an expected utility function of n assets, such as the mean-variance or power-utility function. Associated with a change in an assets holdings from its current or target value is a transaction cost. This cost must be accounted for in practical problems. A straightforward way of doing so results in a 3n-dimensional optimization problem with 3n additional constraints. This higher dimensional problem is computationally expensive to solve. We present a method for solving the 3n-dimensional problem by solving a sequence of n-dimensional optimization problems, which accounts for the transaction costs implicitly rather than explicitly. The method is based on deriving the optimality conditions for the higher-dimensional problem solely in terms of lower-dimensional quantities. The new method is compared to the barrier method implemented in Cplex in a series of numerical experiments. With small but positive transaction costs, the barrier method and the new method solve problems in roughly the same amount of execution time. As the size of the transaction cost increases, the new method outperforms the barrier method by a larger and larger factor.

Portfolio Selection and Transactions Costs

This paper deals with the portfolio selection problem of risky assets with a diagonal covariance matrix, upper bounds on all assets and transactions costs. An algorithm for its solution is formulated which terminates in a number of iterations that is at most three times the number of assets. The efficient portfolios, under appropriate assumptions, are shown to have the following structure. As the risk tolerance parameter increases, an assets holdings increases to its target, then stays there for a while, then increases to its upper bound, reaches it and stays there. Then the holdings of the asset with the next highest expected return proceeds in a similar way and so on.

An integrated CVaR and real options approach to investments in the energy sector

The objective of this paper is to combine a real options framework with portfolio optimization techniques and to apply this new framework to investments in the electricity sector. In particular, a real options model is used to assess the adoption decision of particular technologies under uncertainty. These technologies are coal-fired power plants, biomassfired power plants and onshore wind mills, and they are representative of technologies based on fossil fuels, biomass and renewables, respectively. The return distributions resulting from this analysis are then used as an input to a portfolio optimization, where the measure of risk is the Conditional Value-at-Risk (CVaR).

Forecasting the Euro exchange rate using vector error correction models

Forecasting the Euro Exchange Rate Using Vector Error Correction Models. — This paper presents an exchange rate model for the Euro exchange rates of four major currencies, namely the US dollar, the British pound, the Japanese yen and the Swiss franc. The model is based on the monetary approach of exchange rate theory which uses fundamental macroeconomic variables to explain the exchange rate. A crucial point when using such a model is its proper estimation through cointegration analysis. The euro exchange rate model is therefore estimated in the form of a Vector Autoregressive (VAR) model with cointegrating vectors (VECM). We find that when cointegration analysis is undertaken properly, the naive random walk prediction can be out-performed for the US dollar, the British pound and the Japanese yen, but not for the Swiss franc.ZusammenfassungVektor-Fehlerkorrektur-Modelle zur Vorhersage von Euro-Wechselkursen. — Dieser Artikel entwickelt ein Wechselkursmodell für die Euro-Wechselkurse von vier wichtigen WÄhrungen, nÄmlich des US Dollar, des britischen Pfund, des japanischen Yen und des Schweizer Frankens. Das Modell basiert auf der monetÄren Theorie des Wechselkurses, welche fundamentale makroökonomische Variablen benutzt, um den Wechselkurs zu erklÄren. Entscheidend bei der Verwendung des monetÄren Wechselkursmodells ist dessen richtige SchÄtzung mit Hilfe der Kointegrationsanalyse. Das Euro-Wechselkursmodell wird hier deshalb geschÄtzt in Form eines vektorautoregressiven Modells (VAR) mit Kointegrationsvektoren (VECM). Wir beobachten, dass bei richtiger Anwendung der Kointegrationsanalyse das naive Random-Walk-Modell im Fall des US Dollar, des britischen Pfund und des japanischen Yen, aber nicht im Fall des Schweizer Frankens übertroffen wird.

Multistep Predictions from Multivariate ARMA-GARCH: Models and their Value for Portfolio Management

In this paper we derive the closed form solution for multistep predictions of the conditional means and their covariances from multivariate ARMA-GARCH models. These are useful e.g. in mean variance portfolio analysis when the rebalancing frequency is lower than the data frequency. In this situation the conditional mean and covariance matrix of the sum of the higher frequency returns until the next rebalancing period is required as input in the mean variance portfolio problem. The closed form solution for this quantity is derived as well. We assess the empirical value of the result by evaluating and comparing the performance of quarterly and monthly rebalanced portfolios using monthly MSCI index data across a large set of ARMA-GARCH models. The results forcefully demonstrate the substantial value of multistep predictions for portfolio management.

The determinants of long-run economic growth: A conceptually and computationally simple approach

SummaryIn this paper we use principal components augmented regressions (PCARs), partly in conjunction with model averaging, to determine the variables relevant for economic growth. The use of PCARs allows to effectively tackle two major problems that the empirical growth literature faces: (i) the uncertainty about the relevance of variables and (ii) the availability of data sets with the number of variables of the same order as the number of observations. The use of PCARs furthermore implies that the computational cost is, compared to standard approaches used in the literature, negligible. The proposed methodology is applied to three data sets, including the Sala-i-Martin, Doppelhofer, and Miller (2004) and Fernandez, Ley, and Steel (2001) data as well as an extended version of the former. Key economic variables are found to be significantly related to economic growth, which demonstrates the relevance of the proposed methodology for empirical growth research.