Cross-sectional and Time-series Momentum in Equity and Futures Markets: Trading Strategies and the Role of Correlation Risk

The purpose of the thesis is to investigate momentum trading strategies in equity and futures markets and to explore the links between momentum profitability and the equity market correlation of the economy. The first topic focuses on cross-sectional equity momentum patterns by modeling a stock’s price path as the interaction between a long-term growth component and a number of fluctuating price components that oscillate around the long-term trend at various distinct frequencies. Based on this specification, the dependence of momentum profitability on the asset price response to oscillations at various frequencies is explored. The evidence is consistent with a behavioural overreaction-to-private-information and underreaction-to-public-information explanation of the momentum patterns. Cross-sectional momentum profitability is found to be robust to realistic transaction costs and is shown to be optimized in terms of minimising the effects of transaction costs for a 6-month holding horizon. Simple stop-loss rules are shown to improve the performance of strategies with long-term holding horizon by discarding big and growth stocks, which achieve higher levels of price efficiency and therefore realise their momentum potential faster than small and value stocks. The second topic focuses on the source of profitability for cross-sectional momentum portfolios and other commonly used long-short zero-cost factor-mimicking portfolios and investigates whether these abnormal premia are justified as compensation for bearing correlation risk. Using a novel dataset on correlation swaps and building on the fact that large equity market declines are accompanied by increases in stock correlations, it is shown that correlation risk is priced in the cross-section of stock returns even after including conventional risk factors. Moreover, it is documented that a significant part of long-short portfolios’ return premia is explained by exposure to correlation risk. Interestingly, the inflow of capital into long-short hedge fund strategies coincides with increases in the realized equity market correlation, and consequently with decreases in the price of insurance against unexpected correlation surprises. Finally, the profitability and the mechanics of time-series momentum strategies in futures markets are explored. A time-series momentum strategy involves the volatility-adjusted aggregation of univariate strategies and therefore relies heavily on the efficiency of the volatility estimator and on the quality of the momentum trading signal. The evidence shows that trading signals generated by fitting a linear trend on the asset price path maximise the out-of-sample performance while minimising the portfolio turnover. The momentum patterns are found to be strong at the monthly frequency of rebalancing, relatively strong at the weekly frequency and relatively weak at the daily frequency. In fact, significant reversal effects are documented at the very short-term horizon. Regarding the volatility-adjusted aggregation of univariate strategies, the Yang-Zhang range estimator constitutes the optimal choice for volatility estimation in terms of maximising efficiency and minimising the bias and the ex-post portfolio turnover

Foreign direct investment in OECD countries: a special focus in the case of Greece

Foreign Direct Investment (FDI) is considered as an important instrument for economic development all over the world. As a result, a growing competition for FDI among the majority of all countries has reached to high level. The aim of this paper is to examine the FDI inflows determinants for 24 OECD countries i.e. Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, Korea, Mexico, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Turkey, United Kingdom and United States. To this end we employ annual data from 1980 to 2012 for a series of potential FDI determinants that have been identified as the most important by the relevant literature. Our empirical strategy employs both the standard fixed effects panel as well as a dynamic panel approach. The empirical findings highlight the importance of market size, trade openness, unit labor cost, schooling, taxation, gross capital formation, institutional variables, and ROA/ROE as significant FDI determinants. In the case of the dynamic panel model those FDI inflows determinants are not uniform for all country groups. Additionally, the results indicate that corporate tax rates clearly affect FDI attractiveness. This finding is robust when testing different countries subgroups. The present study has important policy implications indicating the factors that host economies should place emphasis on in order to attract FDI inflows. Policy makers should not only pay attention to the corporate tax rate level but they should also design a simple, stable and transparent taxation system that minimizes the relevant business risk

Software Performance Engineering using Virtual Time Program Execution

In this thesis we introduce a novel approach to software performance engineering that is based on the execution of code in virtual time. Virtual time execution models the timing-behaviour of unmodified applications by scaling observed method times or replacing them with results acquired from performance model simulation. This facilitates the investigation of "what-if" performance predictions of applications comprising an arbitrary combination of real code and performance models. The ability to analyse code and models in a single framework enables performance testing throughout the software lifecycle, without the need to to extract performance models from code. This is accomplished by forcing thread scheduling decisions to take into account the hypothetical time-scaling or model-based performance specifications of each method. The virtual time execution of I/O operations or multicore targets is also investigated. We explore these ideas using a Virtual EXecution (VEX) framework, which provides performance predictions for multi-threaded applications. The language-independent VEX core is driven by an instrumentation layer that notifies it of thread state changes and method profiling events; it is then up to VEX to control the progress of application threads in virtual time on top of the operating system scheduler. We also describe a Java Instrumentation Environment (JINE), demonstrating the challenges involved in virtual time execution at the JVM level. We evaluate the VEX/JINE tools by executing client-side Java benchmarks in virtual time and identifying the causes of deviations from observed real times. Our results show that VEX and JINE transparently provide predictions for the response time of unmodified applications with typically good accuracy (within 5-10%) and low simulation overheads (25-50% additional time). We conclude this thesis with a case study that shows how models and code can be integrated, thus illustrating our vision on how virtual time execution can support performance testing throughout the software lifecycle

The combined use of weather radar and geographic information system techniques for flood forecasting

A distributed rainfall-runoff model capable for real time flood forecasting utilizing highly spatial and time resolution data was developed. The study region is located under the WSR-74 S-band 100 km radar umbrella and is equipped with a number of rain gauge recording stations, a permanent installation for flow measurement and a stage recorder. The entire basin was digitized to 2×2 km² grid squares by implying GIS techniques. A series of rainfall events recorded producing floods were analyzed and processed. The linear channel parameter assigned to each grid-square is based on its location measured by the centroid of the grid square along the channel network. The estimation of the hill-slope and the stream velocity are calculated based on the Geographic Information System (GIS) procedures

Use of radiobiological modeling in treatment plan evaluation and optimization of prostate cancer radiotherapy

There are many tools available that are used to evaluate a radiotherapy treatment plan, such as isodose distribution charts, dose volume histograms (DVH), maximum, minimum and mean doses of the dose distributions as well as DVH point dose constraints. All the already mentioned evaluation tools are dosimetric only without taking into account the radiobiological characteristics of tumors or OARs. It has been demonstrated that although competing treatment plans might have similar mean, maximum or minimum doses they may have significantly different clinical outcomes (Mavroidis et al. 2001). For performing a more complete treatment plan evaluation and comparison the complication-free tumor control probability (P+) and the biologically effective uniform dose (D ) have been proposed (Källman et al. 1992a, Mavroidis et al. 2000). The D concept denotes that any two dose distributions within a target or OAR are equivalent if they produce the same probability for tumor control or normal tissue complication, respectively (Mavroidis et al. 2001)..

Dynamic Stability with Artificial Intelligence in Smart Grids

Environmental concerns are among the main drives of the energy transition in power systems. Smart grids are the natural evolution of power systems to become more efficient and sustainable. This modernization coincides with the vast and wide integration of energy generation and storage systems dependent on power electronics. At the same time, the low inertia power electronics, introduce new challenges in power system dynamics. In fact, the synchronisation capabilities of power systems are threatened by the emergence of new oscillations and the displacement of conventional solutions for ensuring the stability of power systems. This necessitates an equal modernization of the methods to maintain the rotor angle stability in the future smart grids. The applications of artificial intelligence in power systems are constantly increasing. The thesis reviews the most relevant works for monitoring, predicting, and controlling the rotor angle stability of power systems and presents a novel controller for power oscillation damping

Dynamic stability with artificial intelligence in smart grids

Environmental concerns are among the main drives of the energy transition in power systems. Smart grids are the natural evolution of power systems to become more efficient and sustainable. This modernization coincides with the vast and wide integration of energy generation and storage systems dependent on power electronics. At the same time, the low inertia power electronics, introduce new challenges in power system dynamics. In fact, the synchronisation capabilities of power systems are threatened by the emergence of new oscillations and the displacement of conventional solutions for ensuring the stability of power systems. This necessitates an equal modernization of the methods to maintain the rotor angle stability in the future smart grids. The applications of artificial intelligence in power systems are constantly increasing. The thesis reviews the most relevant works for monitoring, predicting, and controlling the rotor angle stability of power systems and presents a novel controller for power oscillation damping

Investment In The Greek Processing Of Agricultural Products And Food: A Panel Data Approach

                The aim of this research is to quantify Greek investors’ behaviour in the industry of processing agricultural products and food at the aggregate level for all sectors and all regions over the period 1981-1999. The focus of this study is to investigate empirically the effectiveness of the EU and national policies concerning investment incentives in the processing of Greek agricultural products. Investors’ behaviour is examined by employing a synthesized traditional model. The dynamic model is estimated using alternative dynamic panel data methods, GMM and ML techniques. EU and national subsidies are instrumental in stimulating agricultural investments in the past decades, followed by investors’ expectations and, in most of the cases, investment lagged by one year. The empirical results obtained by ML by GMM are very similar, with the exception that the t-statistics are higher in the former case. Moreover, there is no evidence for autocorrelation or dynamic misspecification

Essays in Efficiency and Stability of the Banking Sector

This thesis contributes via the concept of efficiency in four distinct fields of the fi nancial economics and banking literature: technological heterogeneity, liquidity creation, profitability, and stability of banks. In Chapter 1 we motivate the analysis by presenting the main developments that have been taking place in the banking sector as far as these four elds are concerned and highlight their importance to the appropriate functioning of the nancial system and of the economy overall. In Chapter 2 we address the issue that conventional surveys on bank efficiency draw conclusions based on the assumption that all banks in a sample use the same production technology. However, efficiency estimates can be severely distorted if the existence of unobserved differences in technological regimes is not taken into consideration. We estimate the unobserved heterogeneity in banking technologies using a latent class stochastic frontier model. In order to arrive at a policy implication that is valid across time and markets, we present two applications of the model using separately data from the UK and Greek banking sector over the periods 1987-2011 and 1993-2011 respectively. To increase the precision of our inferences, we adopt two distinct empirical methodologies: a panel data method and a pooled cross-section modelling strategy. Our results reveal that bank-heterogeneity in both banking sectors can be controlled for two technological regimes. We find a trade-o¤ between the level of sophistication within a fi nancial system and its level of aggregate efficiency. Consistency among the results is established under both methodologies. Further, we propose a methodology with regard to M&As activity of UK and Greek banks within a latent class context. We examine numerous potential M&A scenarios among banks that belong to different technological regimes, and we test whether there is a transition of the new banks to a more efficient technological class resulting from this M&A activity. We find strong evidence that new financial institutions can be better equipped to withstand potential adverse economic conditions. Finally, we cast doubt on what the true motivation for M&A activity is and we extract important policy inferences in terms of social welfare. In Chapter 3 we introduce the "Cost Efficiency - Liquidity Creation Hypothesis" (CELCH) according to which a rise in a bank s cost efficiency level increases its level of liquidity creation. By employing a novel stress test scenario under a PVAR methodology, we test the CELCH and the direction of causality among liquidity creation and cost efficiency variables in the UK and Greek banking sector. Moreover using new measures of liquidity creation (Berger and Bouwman, 2009) we address the question of whether potential M&As can enhance liquidity creation and create additional credit channels in the economy. We evaluate and compare the robustness of potential consolidation scenarios by employing half - life measures (Chortareas and Kapetanios 2013). We show a positive impact of cost efficiency on liquidity creation in line with CELCH. The empirical evidence further suggests that potential consolidation activity can enhance the ow of credit in the economy. Bank shocks seem to be the most persistent on both liquidity creation and cost efficiency and the UK banking system is found to withstand more effectively adverse economic conditions. Finally, we cast doubts on the strategy followed by policy authorities regarding the recent wave of M&As in the Greek banking sector. In Chapter 4, we attempt to shed light on the trade-o¤ between fi nancial stability and efficiency. We highlight that current tests of banking efficiency do not take into account whether banks managers are taking too much or too little risk relative to the value maximising amount. We assume that moving from an intermediary bank type balance sheet to an investment bank type not only changes the risk-return combination of the balance sheet but also increases the banks degree of instability, that is the probability of insolvency when adverse effects occur. To this extent, we propose a new efficiency measure which incorporates all the aforementioned ambiguous points. An empirical investigation of US commercial banks between 2003-2012 suggests that our proposed risk-adjusted index has superior explanatory power with respect to banks profi tability and gives better predictions compared to conventional banking efficiency measures. This holds after various robustness checks. Chapter 5 summarizes the main findings of all three distinct studies and concludes by highlighting the importance and the contributing points of the thesis in the banking and financial economics literature