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The intensity and duration of hot weather and the number of extreme weather events, such as heatwaves, are increasing, leading to a growing need for space cooling energy demand. Together with the building stock’s low energy performance, this phenomenon may also increase households’ energy consumption. On the other hand, the low level of ownership of cooling equipment can cause low energy consumption, leading to a lack of indoor thermal comfort and several health-related problems, yet increasing the risk of energy poverty in summer. Understanding future temperature variations and the associated impacts on building cooling demand will allow mitigating future issues related to a warmer climate. In this respect, this paper analyses the effects of change in temperatures in the residential sector cooling demand in 2050 for a case study of nineteen cities across seven countries: Cyprus, Finland, Greece, Israel, Portugal, Slovakia, and Spain, by estimating cooling degree days and hours (CDD and CDH). CDD and CDH are calculated using both fixed and adaptive thermal comfort temperature thresholds for 2020 and 2050, understanding their strengths and weaknesses to assess the effects of warmer temperatures. Results suggest a noticeable average increase in CDD and CDH values, up to double, by using both thresholds for 2050, with a particular interest in northern countries where structural modifications in the building stock and occupants’ behavior should be anticipated. Furthermore, the use of the adaptive thermal comfort threshold shows that the projected temperature increases for 2050 might affect people’s capability to adapt their comfort band (i.e., indoor habitability) as temperatures would be higher than the maximum admissible values for people’s comfort and health.
Raúl Castaño-Rosa; Roberto Barrella; Carmen Sánchez-Guevara; Ricardo Barbosa; Ioanna Kyprianou; Eleftheria Paschalidou; Nikolaos Thomaidis; Dusana Dokupilova; João Gouveia; József Kádár; Tareq Hamed; Pedro Palma. Cooling Degree Models and Future Energy Demand in the Residential Sector. A Seven-Country Case Study. Sustainability 2021, 13, 2987 .
AMA StyleRaúl Castaño-Rosa, Roberto Barrella, Carmen Sánchez-Guevara, Ricardo Barbosa, Ioanna Kyprianou, Eleftheria Paschalidou, Nikolaos Thomaidis, Dusana Dokupilova, João Gouveia, József Kádár, Tareq Hamed, Pedro Palma. Cooling Degree Models and Future Energy Demand in the Residential Sector. A Seven-Country Case Study. Sustainability. 2021; 13 (5):2987.
Chicago/Turabian StyleRaúl Castaño-Rosa; Roberto Barrella; Carmen Sánchez-Guevara; Ricardo Barbosa; Ioanna Kyprianou; Eleftheria Paschalidou; Nikolaos Thomaidis; Dusana Dokupilova; João Gouveia; József Kádár; Tareq Hamed; Pedro Palma. 2021. "Cooling Degree Models and Future Energy Demand in the Residential Sector. A Seven-Country Case Study." Sustainability 13, no. 5: 2987.
This paper addresses the modeling and optimization of resource availability in car parks, serving different priority classes of customers. The authors examine various formulations of the problem concerning two general objectives: a) increasing the availability for high priority customers and b) maximizing the aggregate service level. In the current context, priority classes are specified according to different space reservation options provided by the parking management company (monthly parking, hourly parking, parking on demand, etc.). Based on actual historical traffic data and under certain methodological assumptions, they calculate the arrival and service rates for each class of customers. These are subsequently used as inputs in a Markov model that describes the evolution of the number of free parking spaces in time, given that some spaces are reserved for higher priority classes. Optimization techniques and OR heuristics are applied to deal with numerical aspects of the associated reservation planning issues.
Christoforos Salagaras; Vasilis P. Koutras; Nikos S. Thomaidis; Vassilios Vassiliadis; Agapios N. Platis; Georgios Dounias; Constantine Kyriazis. Resource Availability Modeling and Optimization in a Car Park Management Problem. International Journal of Operations Research and Information Systems 2017, 8, 56 -77.
AMA StyleChristoforos Salagaras, Vasilis P. Koutras, Nikos S. Thomaidis, Vassilios Vassiliadis, Agapios N. Platis, Georgios Dounias, Constantine Kyriazis. Resource Availability Modeling and Optimization in a Car Park Management Problem. International Journal of Operations Research and Information Systems. 2017; 8 (2):56-77.
Chicago/Turabian StyleChristoforos Salagaras; Vasilis P. Koutras; Nikos S. Thomaidis; Vassilios Vassiliadis; Agapios N. Platis; Georgios Dounias; Constantine Kyriazis. 2017. "Resource Availability Modeling and Optimization in a Car Park Management Problem." International Journal of Operations Research and Information Systems 8, no. 2: 56-77.
Optimal siting of wind farms based on a pre-assessment of the spatiotemporal variability of wind resources is considered a suitable method for reducing fluctuations in the delivered output. In this study, we explore the potential for balancing wind energy generation in the Iberian Peninsula using Principal Component Analysis (PCA). This technique permits the discovery of possibly new promising locations for wind power harvesting and an evaluation of the existing wind farm network in terms of reliability in energy generation. Data input to the PCA consists of hourly wind capacity factor in a 5-km spatial resolution grid covering the entire peninsula. These data are derived from an equivalent wind farm power curve fed by modeled wind speed data from 80 m above ground level. PCA reveals three significant balancing patterns prevailing over the IP, where half of the currently operating wind farms in Spain are placed. Hence, among the many constituents of the existing wind farm network, these spots offer the best opportunity for stable power supply. The paper concludes by making proposals on an optimum wind capacity allocation based on the idea of equally distributing installed power between positive/negative dipoles emerging from balancing principal components.
F.J. Santos-Alamillos; N.S. Thomaidis; Samuel Quesada-Ruiz; José A. Ruiz-Arias; David Pozo-Vazquez. Do current wind farms in Spain take maximum advantage of spatiotemporal balancing of the wind resource? Renewable Energy 2016, 96, 574 -582.
AMA StyleF.J. Santos-Alamillos, N.S. Thomaidis, Samuel Quesada-Ruiz, José A. Ruiz-Arias, David Pozo-Vazquez. Do current wind farms in Spain take maximum advantage of spatiotemporal balancing of the wind resource? Renewable Energy. 2016; 96 ():574-582.
Chicago/Turabian StyleF.J. Santos-Alamillos; N.S. Thomaidis; Samuel Quesada-Ruiz; José A. Ruiz-Arias; David Pozo-Vazquez. 2016. "Do current wind farms in Spain take maximum advantage of spatiotemporal balancing of the wind resource?" Renewable Energy 96, no. : 574-582.
This paper presents a portfolio-based approach to the harvesting of renewable energy (RE) resources. Our examined problem setting considers the possibility of distributing the total available capacity across an array of heterogeneous RE generation technologies (wind and solar power production units) being dispersed over a large geographical area. We formulate the capacity allocation process as a bi-objective optimization problem, in which the decision maker seeks to increase the mean productivity of the entire array while having control on the variability of the aggregate energy supply. Using large-scale optimization techniques, we are able to calculate - to an arbitrary degree of accuracy - the complete set of Pareto-optimal configurations of power plants, which attain the maximum possible energy delivery for a given level of power supply risk. Experimental results from a reference geographical region show that wind and solar resources are largely complementary. We demonstrate how this feature could help energy policy makers to improve the overall reliability of future RE generation in a properly designed risk management framework
Nikolaos S. Thomaidis; Francisco J. Santos-Alamillos; David Pozo-Vazquez; Julio Usaola. Optimal management of wind and solar energy resources. Computers & Operations Research 2016, 66, 284 -291.
AMA StyleNikolaos S. Thomaidis, Francisco J. Santos-Alamillos, David Pozo-Vazquez, Julio Usaola. Optimal management of wind and solar energy resources. Computers & Operations Research. 2016; 66 ():284-291.
Chicago/Turabian StyleNikolaos S. Thomaidis; Francisco J. Santos-Alamillos; David Pozo-Vazquez; Julio Usaola. 2016. "Optimal management of wind and solar energy resources." Computers & Operations Research 66, no. : 284-291.
International audienceThis paper presents an overview of the state of the art on the research on Dynamic Line Rating forecasting. It is directed at researchers and decision-makers in the renewable energy and smart grids domain, and in particular at members of both the power system and meteorological community. Its aim is to explain the details of one aspect of the complex interconnection between the environment and power systems. The ampacity of a conductor is defined as the maximum constant current which will meet the design, security and safety criteria of a particular line on which the conductor is used. Dynamic Line Rating (DLR) is a technology used to dynamically increase the ampacity of electric overhead transmission lines. It is based on the observation that the ampacity of an overhead line is determined by its ability to dissipate into the environment the heat produced by Joule effect. This in turn is dependent on environmental conditions such as the value of ambient temperature, solar radiation, and wind speed and direction. Currently, conservative static seasonal estimations of meteorological values are used to determine ampacity. In a DLR framework, the ampacity is estimated in real time or quasi-real time using sensors on the line that measure conductor temperature, tension, sag or environmental parameters such as wind speed and air temperature. Because of the conservative assumptions used to calculate static seasonal ampacity limits and the variability of weather parameters, DLRs are considerably higher than static seasonal ratings. The latent transmission capacity made available by DLRs means the operation time of equipment can be extended, especially in the current power system scenario, where power injections from Intermittent Renewable Sources (IRS) put stress on the existing infrastructure. DLR can represent a solution for accommodating higher renewable production whilst minimizing or postponing network reinforcements. On the other hand, the variability of DLR with respect to static seasonal ratings makes it particularly difficult to exploit, which explains the slow take-up rate of this technology. In order to facilitate the integration of DLR into power system operations, research has been launched into DLR forecasting, following a similar avenue to IRS production forecasting, i.e. based on a mix of statistical methods and meteorological forecasts. The development of reliable DLR forecasts will no doubt be seen as a necessary step for integrating DLR into power system management and reaping the expected benefits
Andrea Michiorri; Huu-Minh Nguyen; Stefano Alessandrini; John Bjørnar Bremnes; Silke Dierer; Enrico Ferrero; Bjørn-Egil Nygaard; Pierre Pinson; Nikolaos Thomaidis; Sanna Uski. Forecasting for dynamic line rating. Renewable and Sustainable Energy Reviews 2015, 52, 1713 -1730.
AMA StyleAndrea Michiorri, Huu-Minh Nguyen, Stefano Alessandrini, John Bjørnar Bremnes, Silke Dierer, Enrico Ferrero, Bjørn-Egil Nygaard, Pierre Pinson, Nikolaos Thomaidis, Sanna Uski. Forecasting for dynamic line rating. Renewable and Sustainable Energy Reviews. 2015; 52 ():1713-1730.
Chicago/Turabian StyleAndrea Michiorri; Huu-Minh Nguyen; Stefano Alessandrini; John Bjørnar Bremnes; Silke Dierer; Enrico Ferrero; Bjørn-Egil Nygaard; Pierre Pinson; Nikolaos Thomaidis; Sanna Uski. 2015. "Forecasting for dynamic line rating." Renewable and Sustainable Energy Reviews 52, no. : 1713-1730.
We examine the presence of liquidity commonality in the order-driven Athens Stock Exchange (ASE). Unlike the majority of liquidity commonality studies that focus on the bid–ask spread, our analysis extends deeper in the Limit Order Book, providing insight on the price impact of both small and large trades. We utilize a 6-month FTSE/ATHEX-20 intraday data set to estimate the liquidity factor model of Chordia et al. (2000). To this end, we conduct single-equation analysis as well as panel data analysis with the use of two-way clustered errors, correcting for simultaneous firm and time correlations. Moreover, we apply standard principal component analysis on stock liquidities to extract the marketwide liquidity component. We find that liquidity commonality is low at the bid–ask spread, whereas it increases deeper in the book; consequently, large traders face liquidity risks associated with both individual stock and marketwide illiquidity. Moreover, our empirical evidence hints that liquidity commonality is asynchronous, suggesting that the ASE trading process includes various levels of information speed. Our analysis contributes to the understanding of liquidity commonality in order-driven trading, especially in emerging markets like the ASE where trading activity is limited and information speed is low.
Panagiotis Anagnostidis; George Papachristou; Nikos S. Thomaidis. Liquidity commonality in order-driven trading: evidence from the Athens Stock Exchange. Applied Economics 2015, 48, 1 -15.
AMA StylePanagiotis Anagnostidis, George Papachristou, Nikos S. Thomaidis. Liquidity commonality in order-driven trading: evidence from the Athens Stock Exchange. Applied Economics. 2015; 48 (22):1-15.
Chicago/Turabian StylePanagiotis Anagnostidis; George Papachristou; Nikos S. Thomaidis. 2015. "Liquidity commonality in order-driven trading: evidence from the Athens Stock Exchange." Applied Economics 48, no. 22: 1-15.
We investigate the application of cointegration techniques in designing trading portfolios that outperform a market benchmark. Of particular interest is the situation of enhanced indexation with incomplete portfolios, that is, by imposing a limit on the maximum number of assets included in the portfolio. We present a technique for solving cardinality-constrained portfolio selection problems using cointegration analysis. We investigate the empirical performance of cointegration-based trading strategies in the context of benchmarking portfolios relative to a common stock market index.
Nikolaos S. Thomaidis. On the application of cointegration analysis in enhanced indexing. Applied Economics Letters 2013, 20, 391 -396.
AMA StyleNikolaos S. Thomaidis. On the application of cointegration analysis in enhanced indexing. Applied Economics Letters. 2013; 20 (4):391-396.
Chicago/Turabian StyleNikolaos S. Thomaidis. 2013. "On the application of cointegration analysis in enhanced indexing." Applied Economics Letters 20, no. 4: 391-396.
Commonly used metaheuristic optimisation techniques imbed stochastic elements into the selection of the initial population or/and into the solution-search strategy. Introducing randomness is often a means of escaping from local optima when searching for the global solution. However, depending on the ruggedness of the optimisation landscape and the complexity of the problem at hand, this practice leads to a dispersion of the reported solutions. Instead of relying on the best solution found in a set of runs, as is typical in many optimisation exercises, it is essential to get an indication of the expected dispersion of results by estimating the probability of converging to a “good” solution after a certain number of generations. We apply a range of statistical techniques for estimating the success probability and the convergence rate of popular evolutionary optimisation heuristics in the context of portfolio management. We show how this information can be utilised by a researcher to obtain a deeper understanding of algorithmic behaviour and to evaluate the relative performance of competitive optimisation schemes.
Nikos S. Thomaidis; Vassilios Vassiliadis. Stochastic Convergence Analysis of Metaheuristic Optimisation Techniques. Computational Intelligence 2013, 285, 343 -357.
AMA StyleNikos S. Thomaidis, Vassilios Vassiliadis. Stochastic Convergence Analysis of Metaheuristic Optimisation Techniques. Computational Intelligence. 2013; 285 ():343-357.
Chicago/Turabian StyleNikos S. Thomaidis; Vassilios Vassiliadis. 2013. "Stochastic Convergence Analysis of Metaheuristic Optimisation Techniques." Computational Intelligence 285, no. : 343-357.
This paper assesses the risk inherited in wind turbine investments that rely on a power market in order to determine the selling price of generated power. Using
Petros Katsoulis; Nikos S. Thomaidis; Jan Jantzen. Risk Evaluation of Wind Turbine Investments. SSRN Electronic Journal 2013, 1 .
AMA StylePetros Katsoulis, Nikos S. Thomaidis, Jan Jantzen. Risk Evaluation of Wind Turbine Investments. SSRN Electronic Journal. 2013; ():1.
Chicago/Turabian StylePetros Katsoulis; Nikos S. Thomaidis; Jan Jantzen. 2013. "Risk Evaluation of Wind Turbine Investments." SSRN Electronic Journal , no. : 1.
An integral part of econometric practice is to test the adequacy of model specifications. If a model is adequately specified, it should not leave interesting features of the data-generating process in the errors. Despite the common tradition, the importance of diagnostic checking as a safeguard against mis-specification has only recently been recognized by neural network (NN) practitioners, possibly because this type of semi-parametric methodology was not originally designed for economic and financial applications. The purpose of this paper is to compare a number of analytical statistical testing procedures suitable to diagnostic checking on a neural network regression model. We present the standard Lagrange multiplier (LM) testing framework designed under the assumption of identically distributed disturbances and also examine two modifications that are robust to heteroskedasticity in errors. One modification also gives the researcher an opportunity to incorporate information concerning the volatility structure of the data-generating process in the testing procedure. By means of a Monte Carlo simulation, we investigate the performance of these tests under GARCH-type heteroskedasticity in errors and various distributional assumptions. The results show that although the primary concern of the researcher may be to design a regression model that accurately captures relations in the mean of the conditional distribution, developing a good approximation of the underlying volatility structure generally increases the efficiency of tests in detecting non-adequacy of a NN model.
Nikos S. Thomaidis; Georgios D. Dounias. A comparison of statistical tests for the adequacy of a neural network regression model. Quantitative Finance 2012, 12, 437 -449.
AMA StyleNikos S. Thomaidis, Georgios D. Dounias. A comparison of statistical tests for the adequacy of a neural network regression model. Quantitative Finance. 2012; 12 (3):437-449.
Chicago/Turabian StyleNikos S. Thomaidis; Georgios D. Dounias. 2012. "A comparison of statistical tests for the adequacy of a neural network regression model." Quantitative Finance 12, no. 3: 437-449.
This paper discusses applications of nature-inspired computational techniques in optimisation problems encountered in portfolio selection and applied econometrics. By means of an empirical study, we show how particle swarm intelligence can be effectively used in the estimation of a GARCH and an EGARCH model, two popular econometric parametrisations for the volatility of financial prices. We discuss several issues emerging from the application of nature-inspired techniques in financial optimisation
Nikolaos S. Thomaidis; George D. Dounias; Magdalene Marinaki; Ioannis Marinakis. Optimisation of Complex Financial Models Using Nature-Inspired Techniques. SSRN Electronic Journal 2012, 1 .
AMA StyleNikolaos S. Thomaidis, George D. Dounias, Magdalene Marinaki, Ioannis Marinakis. Optimisation of Complex Financial Models Using Nature-Inspired Techniques. SSRN Electronic Journal. 2012; ():1.
Chicago/Turabian StyleNikolaos S. Thomaidis; George D. Dounias; Magdalene Marinaki; Ioannis Marinakis. 2012. "Optimisation of Complex Financial Models Using Nature-Inspired Techniques." SSRN Electronic Journal , no. : 1.
Nikos S. Thomaidis; George D. Dounias. On Detecting the Optimal Structure of a Neural Network Model Under Strong Statistical Features in Errors. SSRN Electronic Journal 2011, 1 .
AMA StyleNikos S. Thomaidis, George D. Dounias. On Detecting the Optimal Structure of a Neural Network Model Under Strong Statistical Features in Errors. SSRN Electronic Journal. 2011; ():1.
Chicago/Turabian StyleNikos S. Thomaidis; George D. Dounias. 2011. "On Detecting the Optimal Structure of a Neural Network Model Under Strong Statistical Features in Errors." SSRN Electronic Journal , no. : 1.
Nikos S. Thomaidis; Efthimios I. Roumpis; Vassilios N. Karavas. Quantification of Risk and Return for Portfolio Optimization. Nonlinear Financial Econometrics: Forecasting Models, Computational and Bayesian Models 2011, 74 -96.
AMA StyleNikos S. Thomaidis, Efthimios I. Roumpis, Vassilios N. Karavas. Quantification of Risk and Return for Portfolio Optimization. Nonlinear Financial Econometrics: Forecasting Models, Computational and Bayesian Models. 2011; ():74-96.
Chicago/Turabian StyleNikos S. Thomaidis; Efthimios I. Roumpis; Vassilios N. Karavas. 2011. "Quantification of Risk and Return for Portfolio Optimization." Nonlinear Financial Econometrics: Forecasting Models, Computational and Bayesian Models , no. : 74-96.
Hybrid intelligent algorithms, especially those who combine nature-inspired techniques, are well known for their searching abilities in complex problem domains and their performance. One of their main characteristic is that they manage to escape getting trapped in local optima. In this study, two hybrid intelligent schemes are compared both in terms of performance and convergence ability in a complex financial problem. Particularly, both algorithms use a type of genetic algorithm for asset selection and they differ on the technique applied for weight optimization: the first hybrid uses a numerical function optimization method, while the second one uses a continuous ant colony optimization algorithm. Results indicate that there is great potential in combining characteristics of nature-inspired algorithms in order to solve NP-hard optimization problems.
Vassilios Vassiliadis; Nikolaos Thomaidis; George Dounias. On the Performance and Convergence Properties of Hybrid Intelligent Schemes: Application on Portfolio Optimization Domain. Transactions on Petri Nets and Other Models of Concurrency XV 2011, 6625, 131 -140.
AMA StyleVassilios Vassiliadis, Nikolaos Thomaidis, George Dounias. On the Performance and Convergence Properties of Hybrid Intelligent Schemes: Application on Portfolio Optimization Domain. Transactions on Petri Nets and Other Models of Concurrency XV. 2011; 6625 ():131-140.
Chicago/Turabian StyleVassilios Vassiliadis; Nikolaos Thomaidis; George Dounias. 2011. "On the Performance and Convergence Properties of Hybrid Intelligent Schemes: Application on Portfolio Optimization Domain." Transactions on Petri Nets and Other Models of Concurrency XV 6625, no. : 131-140.
We propose an integrated and interactive procedure for designing an enhanced indexation strategy with predetermined investment goals and risk constraints. It is based on a combination of soft computing techniques for dealing with practical and computation aspects of this problem. We deviate from the main trend in enhanced indexation by considering a) restrictions on the total number of tradable assets and b) non-standard investment objectives, focusing e.g. on the probability that the enhanced strategy under-performs the market. Fuzzy set theory is used to handle the subjectivity of investment targets, allowing a smooth variation in the degree of fulfilment with respect to the value of performance indicators. To deal with the inherent complexity of the resulting cardinality-constraint formulations, we apply three nature-inspired optimisation techniques: simulated annealing, genetic algorithms and particle swarm optimisation. Optimal portfolios derived from “soft” optimisers are then benchmarked against the American Dow Jones Industrial Average (DJIA) index and two other simpler heuristics for detecting good asset combinations: a Monte Carlo combinatorial optimisation method and an asset selection technique based on the capitalisation and the beta coefficients of index member stocks.
Nikos S. Thomaidis. A Soft Computing Approach to Enhanced Indexation. Econometrics for Financial Applications 2011, 380, 61 -77.
AMA StyleNikos S. Thomaidis. A Soft Computing Approach to Enhanced Indexation. Econometrics for Financial Applications. 2011; 380 ():61-77.
Chicago/Turabian StyleNikos S. Thomaidis. 2011. "A Soft Computing Approach to Enhanced Indexation." Econometrics for Financial Applications 380, no. : 61-77.
Nikos S. Thomaidis; George D. Dounias. On detecting the optimal structure of a neural network under strong statistical features in errors. Journal of Time Series Analysis 2010, 32, 204 -222.
AMA StyleNikos S. Thomaidis, George D. Dounias. On detecting the optimal structure of a neural network under strong statistical features in errors. Journal of Time Series Analysis. 2010; 32 (3):204-222.
Chicago/Turabian StyleNikos S. Thomaidis; George D. Dounias. 2010. "On detecting the optimal structure of a neural network under strong statistical features in errors." Journal of Time Series Analysis 32, no. 3: 204-222.
We present a framework for designing optimal allocation strategies for large stock portfolios using dynamic factor models and multivariate volatility parametrisations. We attempt to elaborate on the fundamental structure of the Fama and French (FF) factor model with a special focus on the time variation in risk and correlation between stocks returns and systematic factors. For this reason, variants of the multivariate GARCH models are employed to capture the dynamics in means, variances and covariances of the FF factors structure. Based on these models, we derive optimal capital allocation strategies in the framework of Markowitz's mean-variance portfolio theory. We outline and compare the out-of-sample performance of these mean-variance allocations with those obtained using simpler techniques, such as sample historical and exponentially weighted moving average (EWMA) estimates.
Nikos S. Thomaidis; Efthimios Roumpis; Nick Kondakis. Optimal portfolio allocation strategies with dynamic factor models. International Journal of Financial Markets and Derivatives 2010, 1, 352 .
AMA StyleNikos S. Thomaidis, Efthimios Roumpis, Nick Kondakis. Optimal portfolio allocation strategies with dynamic factor models. International Journal of Financial Markets and Derivatives. 2010; 1 (4):352.
Chicago/Turabian StyleNikos S. Thomaidis; Efthimios Roumpis; Nick Kondakis. 2010. "Optimal portfolio allocation strategies with dynamic factor models." International Journal of Financial Markets and Derivatives 1, no. 4: 352.
This paper considers the task of forming a portfolio of assets that outperforms a benchmark index, while imposing a constraint on the tracking error volatility. We examine three alternative formulations of active portfolio management. The first one is a typical setup in which the fund manager myopically maximizes excess return. The second formulation is an attempt to set a limit on the total risk exposure of the portfolio by adding a constraint that forces a priori the risk of the portfolio to be equal to the benchmark's. In this paper, we also propose a third formulation that directly maximizes the efficiency of active portfolios, while setting a limit on the maximum tracking error variance. In determining optimal active portfolios, we incorporate additional constraints on the optimization problem, such as a limit on the maximum number of assets included in the portfolio (i.e. the cardinality of the portfolio) as well as upper and lower bounds on asset weights. From a computational point of view, the incorporation of these complex, though realistic, constraints becomes a challenge for traditional numerical optimization methods, especially when one has to assemble a portfolio from a big universe of assets. To deal properly with the complexity and the "roughness" of the solution space, we use particle swarm optimization, a population-based evolutionary technique. As an empirical application of the methodology, we select portfolios of different cardinality that actively reproduce the performance of the FTSE/ATHEX 20 Index of the Athens Stock Exchange. Our empirical study reveals important results concerning the efficiency of common practices in active portfolio management and the incorporation of cardinality constraints.
Nikos S. Thomaidis; Timotheos Angelidis; Vassilios S Vassiliadis; Georgios Dounias. ACTIVE PORTFOLIO MANAGEMENT WITH CARDINALITY CONSTRAINTS: AN APPLICATION OF PARTICLE SWARM OPTIMIZATION. New Mathematics and Natural Computation 2009, 5, 535 -555.
AMA StyleNikos S. Thomaidis, Timotheos Angelidis, Vassilios S Vassiliadis, Georgios Dounias. ACTIVE PORTFOLIO MANAGEMENT WITH CARDINALITY CONSTRAINTS: AN APPLICATION OF PARTICLE SWARM OPTIMIZATION. New Mathematics and Natural Computation. 2009; 5 (3):535-555.
Chicago/Turabian StyleNikos S. Thomaidis; Timotheos Angelidis; Vassilios S Vassiliadis; Georgios Dounias. 2009. "ACTIVE PORTFOLIO MANAGEMENT WITH CARDINALITY CONSTRAINTS: AN APPLICATION OF PARTICLE SWARM OPTIMIZATION." New Mathematics and Natural Computation 5, no. 3: 535-555.
We present a framework for designing optimal allocation strategies for large stock portfolios using dynamic factor models and multivariate volatility parametrisations. We attempt to elaborate on the fundamental structure of the Fama and French (FF) factor model with a special focus on the time variation in risk and correlation between stocks returns and systematic factors. For this reason, variants of the multivariate GARCH models are employed to capture the dynamics in means, variances and covariances of the FF factors structure. Based on these models, we derive optimal capital allocation strategies in the framework of Markowitz’s mean-variance portfolio theory. We outline and compare the out-of-sample performance of these mean-variance allocations with those obtained using simpler techniques, such as sample historical and Exponentially Weighted Moving Average (EWMA) estimates.
Nikolaos S. Thomaidis; Efthymios Roumpis; Nick Kondakis. Optimal Portfolio Allocation Strategies with Dynamic Factor Models. SSRN Electronic Journal 2009, 1 .
AMA StyleNikolaos S. Thomaidis, Efthymios Roumpis, Nick Kondakis. Optimal Portfolio Allocation Strategies with Dynamic Factor Models. SSRN Electronic Journal. 2009; ():1.
Chicago/Turabian StyleNikolaos S. Thomaidis; Efthymios Roumpis; Nick Kondakis. 2009. "Optimal Portfolio Allocation Strategies with Dynamic Factor Models." SSRN Electronic Journal , no. : 1.
We present a hybrid intelligent trading system that combines artificial neural networks (ANN) and particle swarm optimisation (PSO) to generate optimal trading decisions. A PSO algorithm is used to train ANNs using objective functions that are directly linked to the performance of the trading strategy rather than statistical measures of forecast error (e.g. mean squared error). We experiment with several objective measures that quantify the return/risk associated with the trading system. First results from the application of this methodology to real data show that the out-of-sample performance of trading models is fairly consistent with respect to the objective function they derive from.
Nikos S. Thomaidis; Georgios D. Dounias. A Hybrid Neural Network-Based Trading System. Computer Vision 2009, 5572, 694 -701.
AMA StyleNikos S. Thomaidis, Georgios D. Dounias. A Hybrid Neural Network-Based Trading System. Computer Vision. 2009; 5572 ():694-701.
Chicago/Turabian StyleNikos S. Thomaidis; Georgios D. Dounias. 2009. "A Hybrid Neural Network-Based Trading System." Computer Vision 5572, no. : 694-701.