Essays in international finance

This Ph.D. thesis contains 3 essays in international finance with a focus on foreign exchange market from the perspectives of empirical asset pricing (Chapter 2 and Chapter 3), forecasting and market microstructure (Chapter 4). In Chapter 2, I derive the position-unwinding likelihood indicator for currency carry trade portfolios in the option pricing model, and show that it represents the systematic crash risk associated with global liquidity imbalances and also is able to price the cross-section of global currency, sovereign bond, and equity portfolios; I also explore the currency option-implied sovereign default risk in Merton’s framework, and link the sovereign CDS-implied credit risk premia to currency excess returns that it prices the cross section of currency carry, momentum, and volatility risk premium portfolios. In Chapter 3, I investigate the factor structure in currency market and identify three important properties of global currencies – overvalued (undervalued) currencies with respect to equilibrium exchange rates tend to be crash sensitive (insensitive) measured by copula lower tail dependence, relatively cheap (expensive) to hedge in terms of volatility risk premium, and exposed to high (low) speculative propensity gauged by skew risk premium. I further reveal that these three characteristics have rich asset pricing and asset allocation implications, e.g. striking crash-neutral and diversification benefits for portfolio optimization and risk management purposes. In Chapter 4, I examine the term structure of exchange rate predictability by return decomposition, incorporate common latent factors across a range of investment horizons into the exchange rate dynamics with a broad set of predictors, and handle both parameter uncertainty and model uncertainty. I demonstrate the time-varying term-structural effect and model disagreement effect of exchange rate determinants and the projections of predictive information over the term structure, and utilize the time-variation in the probability weighting from dynamic model averaging to identify the scapegoat drivers of customer order flows. I further comprehensively evaluate both statistical and economic significance of the model allowing for a full spectrum of currency investment management, and find that the model generates substantial performance fees

Essays in international finance

This Ph.D. thesis contains 3 essays in international finance with a focus on foreign exchange market from the perspectives of empirical asset pricing (Chapter 2 and Chapter 3), forecasting and market microstructure (Chapter 4). In Chapter 2, I derive the position-unwinding likelihood indicator for currency carry trade portfolios in the option pricing model, and show that it represents the systematic crash risk associated with global liquidity imbalances and also is able to price the cross-section of global currency, sovereign bond, and equity portfolios; I also explore the currency option-implied sovereign default risk in Merton’s framework, and link the sovereign CDS-implied credit risk premia to currency excess returns that it prices the cross section of currency carry, momentum, and volatility risk premium portfolios. In Chapter 3, I investigate the factor structure in currency market and identify three important properties of global currencies – overvalued (undervalued) currencies with respect to equilibrium exchange rates tend to be crash sensitive (insensitive) measured by copula lower tail dependence, relatively cheap (expensive) to hedge in terms of volatility risk premium, and exposed to high (low) speculative propensity gauged by skew risk premium. I further reveal that these three characteristics have rich asset pricing and asset allocation implications, e.g. striking crash-neutral and diversification benefits for portfolio optimization and risk management purposes. In Chapter 4, I examine the term structure of exchange rate predictability by return decomposition, incorporate common latent factors across a range of investment horizons into the exchange rate dynamics with a broad set of predictors, and handle both parameter uncertainty and model uncertainty. I demonstrate the time-varying term-structural effect and model disagreement effect of exchange rate determinants and the projections of predictive information over the term structure, and utilize the time-variation in the probability weighting from dynamic model averaging to identify the scapegoat drivers of customer order flows. I further comprehensively evaluate both statistical and economic significance of the model allowing for a full spectrum of currency investment management, and find that the model generates substantial performance fees

PEER-TO-PEER VIDEO CONTENT DELIVERY OPTIMIZATION SERVICE IN A DISTRIBUTED NETWORK

Η δυναμικά προσαρμοζόμενη ροή βίντεο μέσω HTTP (DASH) παρέχει βελτιώσεις στην ποιότητα της εμπειρίας χρήσης (QoE) κατά την αναπαραγωγή βίντεο σε δίκτυα παλαιότερα των δικτύων 5ης γενιάς (5G). Ωστόσο, οι εφαρμογές τύπου νέφους τις οποίες μπορεί να παρέχει η αρχιτεκτονική δικτύων 5ης γενιάς, σε συνδυασμό με την υλοποίηση υπολογιστικών υποδομών νέφους στο άκρο του δικτύου και κοντά στους τελικούς χρήστες, μπορεί να βελτιώσει σημαντικά τόσο την ποιότητα της προσφερόμενης υπηρεσίας (QoS) όσο και την εμπειρία χρήσης λόγω της δυνατότητας προσωρινής αποθήκευσης περιεχομένου βίντεο στο άκρο του δικτύου, λόγω της δυνατότητας παροχής προσωρινής αποθήκευσης μέρους του βίντεο στο άκρο του δικτύου. Επιπροσθέτως, εκτός της αποθήκευσης στο και διανομής βίντεο από το άκρο του δικτύου προς τους τελικούς χρήστες, οι νέες υποδομές βίντεο θα παρέχουν τη δυνατότητα διανομής περιεχομένου βίντεο απευθείας από συσκευή σε συσκευή (D2D). Αξιοποιώντας τις τεχνολογίες αυτές, μπορούν να υλοποιηθούν καινοτόμες υπηρεσίες ροής βίντεο, οι οποίες μπορούν όχι μόνο να βελτιώσουν την εμπειρία χρήσης των τελικών χρηστών κατά την αναπαραγωγή βίντεο, αλλά και να μειώσουν το συνολικό κόστος διανομής βίντεο καθώς και την συμφόρηση των δικτύων, άρα και την καθυστέρηση από άκρο σε άκρο και τη συμφόρηση στα δίκτυα διανομής περιεχομένου (CDN) των παρόχων υπηρεσιών διανομής και ροής βίντεο. Στην παρούσα διπλωματική εργασία μελετούμε την επίπτωση που έχουν διάφοροι συνδυασμοί τεχνικών προσωρινής αποθήκευσης, διανομής, καθώς και επιλογής ανάλυσης, σε περιεχόμενο βίντεο, πάνω στην ποιότητα της προσφερόμενης υπηρεσίας και στην εμπειρία των τελικών χρηστών που βρίσκονται στο άκρο του δικτύου, οι οποίες μπορούν να αξιοποιηθούν στη δημιουργία μιας καινοτόμας υπηρεσίας που βελτιστοποιεί τη διανομή περιεχομένου βίντεο μεταξύ ομότιμων κόμβων (P2P) σε ένα κατανεμημένο δίκτυο.Dynamtic Adaptive Streaming over HTTP (DASH) has yield several improvements in the video playback Quality of Experimence (QoE) for the end users in pre-fifth generation (5G) networks. However, cloud applications that 5G networks enable, combined with cloud infrastructures at the edge of the network and in close vicinity to the end users, can offer significant improvements in both the offered Quality of Service (QoS) and QoE because of the video content caching capabilities at the edge of the network that the edge cloud can offer. Furthermore, in addition to edge caching and edge video streaming to the end users, new video infrastructures can offer Device-to-Device (D2D) video content exchange and delivery. Taking advantage of these technologies, innovative video streaming services can be developed which not only improve the video playback QoE for the end users but also reduce the video delivery costs and generated network traffic, which also means reduced end-to-end latency and reduced overhead in video content providers’ Content Delivery Network (CDN). In this thesis we study the impact of using different combinations of distinct video caching techniques, video segment request and streaming algorithms and video resolution selection logics on the QoS and the QoE of end users at the network edge, which can be used in developing an innovative Peer-to-Peer (P2P) video content delivery optimization service in a distributed network

Mid-Price Movement Prediction in Limit Order Books Using Feature Engineering and Machine Learning

The increasing complexity of financial trading in recent years revealed the need for methods that can capture its underlying dynamics. An efficient way to organize this chaotic system is by contracting limit order book ordering mechanisms that operate under price and time filters. Limit order book can be analyzed using linear and nonlinear models. The thesis develops novelmethods for the identification of limit order book characteristics which provide traders and market makers an information edge in their trading. A good proxy for traders and market makers is the prediction of mid-price movement, which is the main target of this thesis. The contributions of this thesis are categorized chronologically into three parts. The first part refers to the introduction in the literature of the first publicly available limit order book dataset for high-frequency trading for the task of mid-price movement prediction. This dataset comes together with the development of an experimental protocol that utilizes methods inspired by ridge regression and a single layer feed-forward neural network as classifiers. These classifiers use state-of-the-art limit order book features as inputs for the target task. The next contribution of this thesis is the use and development of a wide range of technical and quantitative indicators for the task of mid-price movement prediction via an extensive feature selection process. This feature selection process identifies which features improve predictability performance. The results suggest that the newly introduced quantitative feature based on an adaptive logistic regression model for online learning was selected first according to several criteria. These criteria operate according to entropy, linear discriminant analysis, and least mean square error. The third contribution is the introduction of econometric features as inputs to deep learning models for the task of mid-price movement prediction. An extensive comparison against other state-of-the-art hand-crafted features and fully automated feature extraction processes is provided. Furthermore, a new experimental protocol is developed for the task of mid-price prediction, to overcome the problem of time irregularities, which characterizes high-frequency data. Results suggest that advanced hand-crafted features such as econometric indicators can predict movements of proxies, such as mid-price

Calcium sensor proteins in hearing and sight. Biochemical investigation of diseases-associated variants.

Calcium is a cation which plays a pivotal role as second messenger, thus its concentration in cells needs to be finely regulated. Many systems work for that purpose, including Ca2+ sensor proteins, which undergo conformational changes upon Ca2+ coordination via EF-hands. Ca2+ sensors can be ubiquitous or tissue specific. Examples in this sense are represented by Guanylate Cyclase Activating Protein 1 (GCAP1) and Calcium- and Integrin-Binding Protein 2 (CIB2), involved in sight and hearing respectively. Missense point mutations in GCAP1 and CIB2 were found to be associated with genetic diseases characterized by retinal dystrophies and/or deafness. During my PhD, I focused my attention on the characterization of two point mutations namely p.Glu111Val (E111V) in GCAP1, leading to Cone/Rod dystrophy in an Italian family, and p.Glu64Asp (E64D) in CIB2, linked to Usher syndrome type 1J (USH1J), a rare disease characterized by the copresence of blindness and deafness. In particular, I spent the first part of the PhD investigating the role of CIB2 which is still under debate, finding that it is per se uncapable to work as a Ca2+ sensor under physiological conditions and that the conservative mutation linked to USH1J perturbs an allosteric communication between pseudo-EF1 and EF3, thus blocking the protein in an unfunctional conformation. Then, I characterized E111V GCAP1, finding that it is incapable of regulating its molecular target (Guanylate Cyclase), leading to a constitutive active enzyme and thus a progressively high concentrations of Ca2+ and cGMP in cells, which may explain the pathological phenotype. Looking for a potential therapeutic approach for Cone-Rod dystrophies, we found that the well-established Ca2+-relay model, explaining the gradual activation of Guanylate Cyclase by multiple GCAP molecules following gradual changes in intracellular Ca2+ concentrations, seems to be species-specific, since it apparently does not work in the same way in humans as in mouse and bovine photoreceptors. Finally, we identified a general method for the characterization of the interaction between a ubiquitous Ca2+ sensor protein (calmodulin) and inorganic CaF2 nanoparticles, suggesting their suitability as devices for nanomedicine applications

Entendendo o efeito das condições da rede na qualidade de experiência do usuário

Foi previsto que, até 2022, aproximadamente 82% do tráfego na Internet será tráfego de vídeo (CISCO, 2019). A expectativa é de que as pessoas assistam os vídeos em diferentes equipamentos, como celulares, smart TVs, computadores e tablets. Ao mesmo tempo, os usuários têm se tornado cada vez mais exigentes quanto à qualidade dos vídeos. Nesse contexto, torna-se crucial que provedores de internet entendam como condições de rede afetam a qualidade dos vídeos, visto que isso impacta diretamente na qualidade de experiência (QoE) do usuário. O objetivo principal deste trabalho é estudar a relação entre o tamanho do buffer do driver WiFi e a QoE percebida, fazendo uso de métodos interpretativos. A análise é baseada em experimentos que consistem na coleta de dados de uma aplicação de vídeo que é transmitida em uma rede monitorada. Coleto métricas de vídeos do YouTube usando uma extensão do Google Chrome, implementada em javascript. Mais especificamente, foram coletados dados que permitem a obtenção de: latência inicial, taxa do vídeo, mudanças na taxa do vídeo e ocorrência e duração de rebufferizações. Essas métricas servem como proxies para a QoE percebida pelo usuário. Para entender como as métricas de QoE se comportam com mudanças no desempenho da rede, vario as condições de rede, como, por exemplo, a taxa de perda de pacotes e, crucialmente, o tamanho do buffer do driver de WiFi do roteador de modo a analisar como as métricas de QoE se comportam sujeitas a essas variações. No futuro experimentos serão realizados com clientes voluntários de um provedor de internet para a criação de um modelo de inferência de métricas de QoE a partir de métricas de rede e o tamanho do buffer do driver WiFi