A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets

This paper proposes a particle swarm optimization (PSO) approach to support electricity producers for multiperiod optimal contract allocation. The producer risk preference is stated by a utility function (U) expressing the tradeoff between the expectation and variance of the return. Variance estimation and expected return are based on a forecasted scenario interval determined by a price range forecasting model developed by the authors. A certain confidence level is associated to each forecasted scenario interval. The proposed model makes use of contracts with physical (spot and forward) and financial (options) settlement. PSO performance was evaluated by comparing it with a genetic algorithm-based approach. This model can be used by producers in deregulated electricity markets but can easily be adapted to load serving entities and retailers. Moreover, it can easily be adapted to the use of other type of contracts.

A long-term swarm intelligence hedging tool applied to electricity markets

This paper proposes a swarm intelligence long-term hedging tool to support electricity producers in competitive electricity markets. This tool investigates the long-term hedging opportunities available to electric power producers through the use of contracts with physical (spot and forward) and financial (options) settlement. To find the optimal portfolio the producer risk preference is stated by a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance estimation and the expected return are based on a forecasted scenario interval determined by a long-term price range forecast model, developed by the authors, whose explanation is outside the scope of this paper. The proposed tool makes use of Particle Swarm Optimization (PSO) and its performance has been evaluated by comparing it with a Genetic Algorithm (GA) based approach. To validate the risk management tool a case study, using real price historical data for mainland Spanish market, is presented to demonstrate the effectiveness of the proposed methodology.

Risk Analysis and Behavior of Electricity Portfolio Aggregator

The scope of this paper is to adapt the standard mean-variance model of Henry Markowitz theory, creating a simulation tool to find the optimal configuration of the portfolio aggregator, calculate its profitability and risk. Currently, there is a deep discussion going on among the power system society about the structure and architecture of the future electric system. In this environment, policy makers and electric utilities find new approaches to access the electricity market; this configures new challenging positions in order to find innovative strategies and methodologies. Decentralized power generation is gaining relevance in liberalized markets, and small and medium size electricity consumers are also become producers (“prosumers”). In this scenario an electric aggregator is an entity that joins a group of electric clients, customers, producers, “prosumers” together as a single purchasing unit to negotiate the purchase and sale of electricity. The aggregator conducts research on electricity prices, contract terms and conditions in order to promote better energy prices for their clients and allows small and medium customers to benefit improved market prices.

Single period Markowitz portfolio selection, performance gauging and duality: a variation on Luenberger's shortage function

Markowitz portfolio theory (1952) has induced research into the efficiency of portfolio management. This paper studies existing nonparametric efficiency measurement approaches for single period portfolio selection from a theoretical perspective and generalises currently used efficiency measures into the full mean-variance space. Therefore, we introduce the efficiency improvement possibility function (a variation on the shortage function), study its axiomatic properties in the context of Markowitz efficient frontier, and establish a link to the indirect mean-variance utility function. This framework allows distinguishing between portfolio efficiency and allocative efficiency. Furthermore, it permits retrieving information about the revealed risk aversion of investors. The efficiency improvement possibility function thus provides a more general framework for gauging the efficiency of portfolio management using nonparametric frontier envelopment methods based on quadratic optimisation.

The regional electricity generation mix in Scotland: A portfolio selection approach

Standalone levelised cost assessments of electricity supply options miss an important contribution that renewable and non-fossil fuel technologies can make to the electricity portfolio: that of reducing the variability of electricity costs, and their potentially damaging impact upon economic activity. Portfolio theory applications to the electricity generation mix have shown that renewable technologies, their costs being largely uncorrelated with non-renewable technologies, can offer such benefits. We look at the existing Scottish generation mix and examine drivers of changes out to 2020. We assess recent scenarios for the Scottish generation mix in 2020 against mean-variance efficient portfolios of electricity-generating technologies. Each of the scenarios studied implies a portfolio cost of electricity that is between 22% and 38% higher than the portfolio cost of electricity in 2007. These scenarios prove to be “inefficient” in the sense that, for example, lower variance portfolios can be obtained without increasing portfolio costs, typically by expanding the share of renewables. As part of extensive sensitivity analysis, we find that Wave and Tidal technologies can contribute to lower risk electricity portfolios, while not increasing portfolio cost.

Price Transmission and Effects of Exchange Rates on Domestic Commodity Prices via Offshore and Currency Hedging

The framework presents how trading in the foreign commodity futures market and the forward exchange market can affect the optimal spot positions of domestic commodity producers and traders. It generalizes the models of Kawai and Zilcha (1986) and Kofman and Viaene (1991) to allow both intermediate and final commodities to be traded in the international and futures markets, and the exporters/importers to face production shock, domestic factor costs and a random price. Applying mean-variance expected utility, we find that a rise in the expected exchange rate can raise both supply and demand for commodities and reduce domestic prices if the exchange rate elasticity of supply is greater than that of demand. Whether higher volatilities of exchange rate and foreign futures price can reduce the optimal spot position of domestic traders depends on the correlation between the exchange rate and the foreign futures price. Even though the forward exchange market is unbiased, and there is no correlation between commodity prices and exchange rates, the exchange rate can still affect domestic trading and prices through offshore hedging and international trade if the traders are interested in their profit in domestic currency. It illustrates how the world prices and foreign futures prices of commodities and their volatility can be transmitted to the domestic market as well as the dynamic relationship between intermediate and final goods prices. The equilibrium prices depends on trader behaviour i.e. who trades or does not trade in the foreign commodity futures and domestic forward currency markets. The empirical result applying a two-stage-least-squares approach to Thai rice and rubber prices supports the theoretical result.

Extremal properties of certain risk models

Cette thèse s'intéresse à étudier les propriétés extrémales de certains modèles de risque d'intérêt dans diverses applications de l'assurance, de la finance et des statistiques. Cette thèse se développe selon deux axes principaux, à savoir: Dans la première partie, nous nous concentrons sur deux modèles de risques univariés, c'est-à- dire, un modèle de risque de déflation et un modèle de risque de réassurance. Nous étudions le développement des queues de distribution sous certaines conditions des risques commun¬s. Les principaux résultats sont ainsi illustrés par des exemples typiques et des simulations numériques. Enfin, les résultats sont appliqués aux domaines des assurances, par exemple, les approximations de Value-at-Risk, d'espérance conditionnelle unilatérale etc. La deuxième partie de cette thèse est consacrée à trois modèles à deux variables: Le premier modèle concerne la censure à deux variables des événements extrême. Pour ce modèle, nous proposons tout d'abord une classe d'estimateurs pour les coefficients de dépendance et la probabilité des queues de distributions. Ces estimateurs sont flexibles en raison d'un paramètre de réglage. Leurs distributions asymptotiques sont obtenues sous certaines condi¬tions lentes bivariées de second ordre. Ensuite, nous donnons quelques exemples et présentons une petite étude de simulations de Monte Carlo, suivie par une application sur un ensemble de données réelles d'assurance. L'objectif de notre deuxième modèle de risque à deux variables est l'étude de coefficients de dépendance des queues de distributions obliques et asymétriques à deux variables. Ces distri¬butions obliques et asymétriques sont largement utiles dans les applications statistiques. Elles sont générées principalement par le mélange moyenne-variance de lois normales et le mélange de lois normales asymétriques d'échelles, qui distinguent la structure de dépendance de queue comme indiqué par nos principaux résultats. Le troisième modèle de risque à deux variables concerne le rapprochement des maxima de séries triangulaires elliptiques obliques. Les résultats théoriques sont fondés sur certaines hypothèses concernant le périmètre aléatoire sous-jacent des queues de distributions. -- This thesis aims to investigate the extremal properties of certain risk models of interest in vari¬ous applications from insurance, finance and statistics. This thesis develops along two principal lines, namely: In the first part, we focus on two univariate risk models, i.e., deflated risk and reinsurance risk models. Therein we investigate their tail expansions under certain tail conditions of the common risks. Our main results are illustrated by some typical examples and numerical simu¬lations as well. Finally, the findings are formulated into some applications in insurance fields, for instance, the approximations of Value-at-Risk, conditional tail expectations etc. The second part of this thesis is devoted to the following three bivariate models: The first model is concerned with bivariate censoring of extreme events. For this model, we first propose a class of estimators for both tail dependence coefficient and tail probability. These estimators are flexible due to a tuning parameter and their asymptotic distributions are obtained under some second order bivariate slowly varying conditions of the model. Then, we give some examples and present a small Monte Carlo simulation study followed by an application on a real-data set from insurance. The objective of our second bivariate risk model is the investigation of tail dependence coefficient of bivariate skew slash distributions. Such skew slash distributions are extensively useful in statistical applications and they are generated mainly by normal mean-variance mixture and scaled skew-normal mixture, which distinguish the tail dependence structure as shown by our principle results. The third bivariate risk model is concerned with the approximation of the component-wise maxima of skew elliptical triangular arrays. The theoretical results are based on certain tail assumptions on the underlying random radius.

The association between drinking motives and alcohol-related consequences - room for biases and measurement issues?

Aims  To investigate whether the predominant finding of generalized positive associations between self-rated motives for drinking alcohol and negative consequences of drinking alcohol are influenced by (i) using raw scores of motives that may weight inter-individual response behaviours too strongly, and (ii) predictor-criterion contamination by using consequence items where respondents attribute alcohol use as the cause. Design  Cross-sectional study within the European School Survey Project on Alcohol and other Drugs (ESPAD). Setting  School classes. Participants  Students, aged 13-16 (n = 5633). Measurements  Raw, rank and mean-variance standardized scores of the Drinking Motives Questionnaire-Revised (DMQ-R); four consequences: serious problems with friends, sexual intercourse regretted the next day, physical fights and troubles with the police, each itemized with attribution ('because of your alcohol use') and without. Findings  As found previously in the literature, raw scores for all drinking motives had positive associations with negative consequences of drinking, while transformed (rank or Z) scores showed a more specific pattern: external reinforcing motives (social, conformity) had negative and internal reinforcing motives (enhancement, coping) had non-significant or positive associations with negative consequences. Attributed consequences showed stronger associations with motives than non-attributed ones. Conclusion  Standard scoring of the Drinking Motives Questionnaire (Revised) fails to capture motives in a way that permits specific associations with different negative consequences to be identified, whereas use of rank or Z-scores does permit this. Use of attributed consequences overestimates the association with drinking motives.

Portfolio choice and the effects of liquidity

This paper shows how to introduce liquidity into the well known mean-variance framework of portfolio selection. Either by estimating mean-variance liquidity constrained frontiers or directly estimating optimal portfolios for alternative levels of risk aversion and preference for liquidity, we obtain strong effects of liquidity on optimal portfolio selection. In particular, portfolio performance, measured by the Sharpe ratio relative to the tangency portfolio, varies significantly with liquidity. Moreover, although mean-variance performance becomes clearly worse, the levels of liquidity onoptimal portfolios obtained when there is a positive preference for liquidity are much lower than on those optimal portfolios where investors show no sign of preference for liquidity.

Understanding portfolio efficiency with conditioning information

We show that unconditionally efficient returns do not achieve the maximum unconditionalSharpe ratio, neither display zero unconditional Jensen s alphas, when returns arepredictable. Next, we define a new type of efficient returns that is characterized by thoseunconditional properties. We also study a different type of efficient returns that is rationalizedby standard mean-variance preferences and motivates new Sharpe ratios and Jensen salphas. We revisit the testable implications of asset pricing models from the perspective ofthe three sets of efficient returns. We also revisit the empirical evidence on the conditionalvariants of the CAPM and the Fama-French model from a portfolio perspective.

Bartlett-Lewis of rectangular pulse modified model: estimate of parameters for simulation of precipitation in sub-hourly duration

The Bartlett-Lewis Rectangular Pulse Modified (BLPRM) model simulates the precipitous slide in the hourly and sub-hourly and has six parameters for each of the twelve months of the year. This study aimed to evaluate the behavior of precipitation series in the duration of 15 min, obtained by simulation using the model BLPRM in situations: (a) where the parameters are estimated from a combination of statistics, creating five different sets; (b) suitability of the model to generate rain. To adjust the parameters were used rain gauge records of Pelotas/RS/Brazil, which statistics were estimated - mean, variance, covariance, autocorrelation coefficient of lag 1, the proportion of dry days in the period considered. The results showed that the parameters related to the time of onset of precipitation (λ) and intensities (μx) were the most stable and the most unstable were ν parameter, related to rain duration. The BLPRM model adequately represented the mean, variance, and proportion of the dry period of the series of precipitation lasting 15 min and, the time dependence of the heights of rain, represented autocorrelation coefficient of the first retardation was statistically less simulated series suitability for the duration of 15 min.

Chasing performance persistence of hedge funds

The thesis examines the performance persistence of hedge funds using complement methodologies (namely cross-sectional regressions, quantile portfolio analysis and Spearman rank correlation test). In addition, six performance ranking metrics and six different combinations of selection and holding periods are compared. The data is gathered from HFI and Tremont databases covering over 14,000 hedge funds and time horizon is set from January 1996 to December 2007. The results suggest that there definitely exists performance persistence among hedge funds and the strength and existence of persistence vary among fund styles. The persistence depends on the metrics and combination of selection and prediction period applied. According to the results, the combination of 36-month selection and holding period outperforms other five period combinations in capturing performance persistence within the sample. Furthermore, model-free performance metrics capture persistence more sensitively than model-specific metrics. The study is the first one ever to use MVR as a performance ranking metric, and surprisingly MVR is more sensitive to detect persistence than other performance metrics employed.

Hedge-rahastojen menestyksen pysyvyyden empiirinen tarkastelu sijoittajan näkökulmasta

This study examines performance persistence of hedge funds from investor's point of view and look at the methods by which an investor could choose the successful hedge funds to the portfolio. This study was used the data from HFI & Tremont databases on period 1998-2007. In this study used the 36-month combination (24-month selection and 12-month prediction periods). As the research methods used the Sharpe index, raw returns, MVR (mean variance ratio), GSC-clustering, the SDI index and the new combination of metrics. The evaluation criterions of the results used the volatility, excess returns and the Sharpe index. This study compared different results from the 7 time series with each other, and commenting the problems on a portfolio loss of funds.

Diversification benefits for a Finnish non-industrial private forest investor: Real Estate Investment Trusts in the USA

This thesis examined both domestic and international forest investment options for a Finnish non-industrial private forest investor. The focus was on forest-based investment instruments. The influence of movements of currency exchange rates on foreign returns were also taken into account. Annual data from 1995 to 2011 was used. The main portfolio optimization model in this study was the Mean-Variance model but the results were also validated by using the Value at Risk and Expected Shortfall models. In addition, the exchange rate risk hedging was established by using one-week-maturity forward contracts. The results suggested that 75 % of the total wealth should be invested in Finnish private forests and the rest, 25 %, to a US REIT, in this case Rayonier. With hedging, the total return on the portfolio was 7.21 % (NIPF 5.3%) with the volatility of 6.63 % (NIPF 7.9%). Taxation supported US investments in this case. As a conclusion, a Finnish private forest investor may, as evidenced, benefit in diversifying a portfolio using REITs in the US.

Hedge-rahastojen menestyksen pysyvyys ja suhteellinen suoriutuminen sijoittajan näkökulmasta

The purpose of the thesis is to examine the long-term performance persistence and relative performance of hedge funds during bear and bull market periods. Performance metrics applied for fund rankings are raw return, Sharpe ratio, mean variance ratio and strategy distinctiveness index calculated of the original and clustered data correspondingly. Four different length combinations for selection and holding periods are employed. The persistence is examined using decile and quartile portfolio formatting approach and on the basis of Sharpe ratio and SKASR as performance metrics. The relative performance persistence is examined by comparing hedge portfolio returns during varying stock market conditions. The data is gathered from a private database covering 10,789 hedge funds and time horizon is set from January 1990 to December 2012. The results of this thesis suggest that long-term performance persistence of the hedge funds exists. The degree of persistence also depends on the performance metrics employed and length combination of selection and holding periods. The best results of performance persistence were obtained in the decile portfolio analysis on the basis of Sharpe ratio rankings for combination of 12-month selection period and the holding period of equal length. The results also suggest that the best performance persistence occurs in the Event Driven and Multi strategies. Dummy regression analysis shows that a relationship between hedge funds and stock market returns exists. Based on the results, Dedicated Short Bias, Global Macro, Managed Futures and Other strategies perform well during bear market periods. The results also indicate that the Market Neutral strategy is not absolutely market neutral and the Event Driven strategy has the best performance among all hedge strategies.