The High Frequency Economics of Government Bond Markets

This dissertation is a collection of four essays examining different aspects of government bond markets, with a special focus on the US Treasury securities. The first is a comprehensive study of the microstructure of BrokerTec, the larger of the two electronic interdealer trading platforms for US Treasury securities, providing institutional background essential for subsequent studies. We characterize empirically market activities and the price discovery process. We show that both limit orders and trades affect prices, and that these effects are greater around monetary policy announcements. Contrary to previous findings pertaining to equity markets, we find that iceberg orders, which allow traders to hide liquidity, are not used frequently, even around volatile times. The second essay examines closely a frequently used channel of hidden liquidity -- the workup protocol. We ask whether trading activities during workups contain any private information and leave harmful effects on uninformed traders. We find that workup activities account for a significant portion of market liquidity not ex ante observable, but they tend to be less informative than transparent trades. We show that workups are used more often, but contain relatively less information, around volatile times, indicating that workups tend to be used as a channel to guard against adverse price movements, rather than as a channel to hide private information. In the third essay, we propose a novel model to study jointly the intraday dynamics of liquidity and price risks, two important determinants of bond yields. We show that liquidity declines sharply during the 2008 crisis and on flight--to--safety days, accompanied by increased price volatility. Our model also reveals a negative feedback effect between liquidity and volatility, and that each becomes more persistent during the crisis. The fourth study provides an international perspective by studying the propagation of liquidity and volatility shocks during the 2010-2012 sovereign debt crisis across major euro-area bond markets, namely Belgium, France, Germany, Italy, the Netherlands, and Spain. We show that liquidity is generally the more important source of shocks transmitted across the borders, and this transmission largely originates from Italy and around the Italian crisis.Doctor of Philosoph

Deliverable 6.2: Detailed Policy Impact Model

The SEARCH project targets the analysis of the impact of the European Neighborhood Policy (ENP) on the integration of EU neighboring countries with the EU. The research has focused on four areas, such as trade flows, people mobility, human capital, technological activities, innovation diffusion and institutional environment. Work Package 6 is the policy analysis package of SEARCH. This WP synthesizes research results of earlier work packages in order to present an overview of potential EU policy options for strengthening cohesion across the EU-27 and NC16 in the mid to long term. WP 6 employs different research methods ranging from systematic literature analysis via text mining techniques to Delphi methodology and economic modeling. Economic modeling has the advantage that it opens the possibility of ex ante simulating the likely impacts of different kinds of policies. Thus it provides a platform for the comparison of several policy options. This report provides a detailed description of the economic model that has been developed for estimating the likely impacts of certain policy prescriptions arising from research results of earlier work packages. The specific model construct chosen is the GMR (Geographic Macro and Regional) modeling approach that has been applied earlier for Cohesion policy and EU Framework Program impact analyses at the levels of European regions, the European Union and Hungary. The particular country chosen for impact analysis is Turkey. This choice is motivated by practical reasons: availability and reliability of data for modeling. Though data collection for Turkey is not a process without difficulties the situation in this respect is relatively more advantageous there as compared to other ENP countries (with the exception of Israel which cannot be considered as a typical ENP country for other reasons). Turkey is an accession country but in several respects its economic, social and cultural features make this country reasonably comparable to many of the ENP countries. In this report we introduce GMR-Turkey. Its applications in actual policy analyses will be reported in working papers and in another deliveries. This report has the following structure. The second section provides a general overview of GMR-Turkey. Detailed information about modeling structure is given in Section 3. Sensitivity results are reported in Section 4

Reconstructing Dynamical Systems From Stochastic Differential Equations to Machine Learning

Die Modellierung komplexer Systeme mit einer großen Anzahl von Freiheitsgraden ist in den letzten Jahrzehnten zu einer großen Herausforderung geworden. In der Regel werden nur einige wenige Variablen komplexer Systeme in Form von gemessenen Zeitreihen beobachtet, während die meisten von ihnen - die möglicherweise mit den beobachteten Variablen interagieren - verborgen bleiben. In dieser Arbeit befassen wir uns mit dem Problem der Rekonstruktion und Vorhersage der zugrunde liegenden Dynamik komplexer Systeme mit Hilfe verschiedener datengestützter Ansätze. Im ersten Teil befassen wir uns mit dem umgekehrten Problem der Ableitung einer unbekannten Netzwerkstruktur komplexer Systeme, die Ausbreitungsphänomene widerspiegelt, aus beobachteten Ereignisreihen. Wir untersuchen die paarweise statistische Ähnlichkeit zwischen den Sequenzen von Ereigniszeitpunkten an allen Knotenpunkten durch Ereignissynchronisation (ES) und Ereignis-Koinzidenz-Analyse (ECA), wobei wir uns auf die Idee stützen, dass funktionale Konnektivität als Stellvertreter für strukturelle Konnektivität dienen kann. Im zweiten Teil konzentrieren wir uns auf die Rekonstruktion der zugrunde liegenden Dynamik komplexer Systeme anhand ihrer dominanten makroskopischen Variablen unter Verwendung verschiedener stochastischer Differentialgleichungen (SDEs). In dieser Arbeit untersuchen wir die Leistung von drei verschiedenen SDEs - der Langevin-Gleichung (LE), der verallgemeinerten Langevin-Gleichung (GLE) und dem Ansatz der empirischen Modellreduktion (EMR). Unsere Ergebnisse zeigen, dass die LE bessere Ergebnisse für Systeme mit schwachem Gedächtnis zeigt, während sie die zugrunde liegende Dynamik von Systemen mit Gedächtniseffekten und farbigem Rauschen nicht rekonstruieren kann. In diesen Situationen sind GLE und EMR besser geeignet, da die Wechselwirkungen zwischen beobachteten und unbeobachteten Variablen in Form von Speichereffekten berücksichtigt werden. Im letzten Teil dieser Arbeit entwickeln wir ein Modell, das auf dem Echo State Network (ESN) basiert und mit der PNF-Methode (Past Noise Forecasting) kombiniert wird, um komplexe Systeme in der realen Welt vorherzusagen. Unsere Ergebnisse zeigen, dass das vorgeschlagene Modell die entscheidenden Merkmale der zugrunde liegenden Dynamik der Klimavariabilität erfasst.Modeling complex systems with large numbers of degrees of freedom have become a grand challenge over the past decades. Typically, only a few variables of complex systems are observed in terms of measured time series, while the majority of them – which potentially interact with the observed ones - remain hidden. Throughout this thesis, we tackle the problem of reconstructing and predicting the underlying dynamics of complex systems using different data-driven approaches. In the first part, we address the inverse problem of inferring an unknown network structure of complex systems, reflecting spreading phenomena, from observed event series. We study the pairwise statistical similarity between the sequences of event timings at all nodes through event synchronization (ES) and event coincidence analysis (ECA), relying on the idea that functional connectivity can serve as a proxy for structural connectivity. In the second part, we focus on reconstructing the underlying dynamics of complex systems from their dominant macroscopic variables using different Stochastic Differential Equations (SDEs). We investigate the performance of three different SDEs – the Langevin Equation (LE), Generalized Langevin Equation (GLE), and the Empirical Model Reduction (EMR) approach in this thesis. Our results reveal that LE demonstrates better results for systems with weak memory while it fails to reconstruct underlying dynamics of systems with memory effects and colored-noise forcing. In these situations, the GLE and EMR are more suitable candidates since the interactions between observed and unobserved variables are considered in terms of memory effects. In the last part of this thesis, we develop a model based on the Echo State Network (ESN), combined with the past noise forecasting (PNF) method, to predict real-world complex systems. Our results show that the proposed model captures the crucial features of the underlying dynamics of climate variability

Earth Observations for Addressing Global Challenges

"Earth Observations for Addressing Global Challenges" presents the results of cutting-edge research related to innovative techniques and approaches based on satellite remote sensing data, the acquisition of earth observations, and their applications in the contemporary practice of sustainable development. Addressing the urgent tasks of adaptation to climate change is one of the biggest global challenges for humanity. As His Excellency António Guterres, Secretary-General of the United Nations, said, "Climate change is the defining issue of our time—and we are at a defining moment. We face a direct existential threat." For many years, scientists from around the world have been conducting research on earth observations collecting vital data about the state of the earth environment. Evidence of the rapidly changing climate is alarming: according to the World Meteorological Organization, the past two decades included 18 of the warmest years since 1850, when records began. Thus, Group on Earth Observations (GEO) has launched initiatives across multiple societal benefit areas (agriculture, biodiversity, climate, disasters, ecosystems, energy, health, water, and weather), such as the Global Forest Observations Initiative, the GEO Carbon and GHG Initiative, the GEO Biodiversity Observation Network, and the GEO Blue Planet, among others. The results of research that addressed strategic priorities of these important initiatives are presented in the monograph

Earth resources. A continuing bibliography with indexes, issue 23

This bibliography lists 226 reports, articles, and other documents introduced into the NASA scientific and technical information system between July 1, 1979 and September 30, 1979. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Econometric analysis of financial count data and portfolio choice : a dynamic approach.

This thesis contributes to the econometric literature in two ways. Firstly, it introduces a new multivariate count model that presents advances in several aspects. Our multivariate time series count model can deal with issues of discreteness, overdispersion (variance greater than the mean) and both cross- and serial correlation, all at the same time. We follow a fully parametric approach and specify a marginal distribution for the counts where, conditionally on past observations the means follow a vector autoregressive process (VAR). This enables to attain improved inference on coefficients of exogenous regressors relative to the static Poisson regression, while modelling the serial correlation in a flexible way. The method is also innovative in the use of copulas, which builds the dependence structure between variables with given marginal distributions. This makes it possible to model the contemporaneous correlation between individual series in a very flexible way. Secondly, this thesis introduces a new approach to estimate the multivariate reduced rank regressions when the normality assumption is not satisfied. We propose to use the copula tool to generate multivariate distributions and, we show that this method can be applied in multivariate settings. In terms of financial literature, this thesis provides two contributions. Firstly, with our multivariate count model we analyze diverse market microstructure issues about the submission of different types of orders by traders on stock markets. With this model, we can fully take into account the interactions between submissions of the various types of orders, which represent an advantage with respect to univariate models such as the autoregressive conditional duration model. Secondly, it contributes to portfolio research proposing a new dynamic optimal portfolio allocation model in a Value-at-Risk setup. This model allows for time varying skewness and kurtosis of portfolio distributions and the model parameters are estimated by weighted maximum likelihood in an increasing window setup. This last property allows us to have more accurate portfolio recommendations in terms of the amount to invest in the risk-free interest rate and in the risky portfolio.Copulas; Multivariate count model; Optimal portfolio allocation; Value-at-Risk; Market microstructure;

UAS planning and trajectory generation for safe and long-duration oceanic and coastal missions.

La presente tesis doctoral, muestra el diseño de un sistema para la extensión de la duración de vuelo de sistemas autónomos no tripulados de tamaño pequeño. Este sistema fue diseñado en el contexto de misiones de vigilancia marítima y costera como parte del proyecto europeo MarineUAS. En este contexto, se han identificado tres problemas: 1) la necesidad de la estimación precisa de un campo de viento y la capacidad de identificación de fenómenos como el viento cortante o las ráfagas continuas y discretas para que puedan ser utilizadas potencialmente para la extracción de energía para mejorar la duración de vuelo. 2) La necesidad de generar trayectorias suaves para la extracción de energía considerando la dinámica de las plataformas de vuelo y 3) la habilidad de seguir dichas trayectorias. Para el primer problema, el uso de un método de computación directa permite determinar el campo de viento (velocidad y tasa de cambio de la velocidad de viento) sin la utilización de un estimador óptimo. Sin embargo, también se consideraron varios métodos y a partir de un análisis extenso se presentan diferentes comparativas de estos métodos, en el que se muestran las ventajas y desventajas de los mismos. Adicionalmente, la identificación de distintos fenómenos de viento, cómo las ráfagas, o el viento cortante, se logra a través de un innovador método que ejecuta una serie de pruebas estadísticas basadas en la distribución de Weibull y en distintos modelos dinámicos que consideran no solo la distribución del viento sino la interacción con el océano y la superficie en las respectivas capas límite. Para el segundo problema, una aproximación biomimética permitió el uso de un algoritmo complejo para la réplica de trajectorias de vuelo dinámico de aves. En dicho algoritmo se consideran observaciones presentadas por distintos científicos que permiten generar trayectorias paramétricas que consideran además restricciones cinemáticas de la plataforma en el diseño de las mismas. El tercer problema toma en consideración la curva generada y utiliza la teoría del campo de vectores para diseñar un controlador que permite seguir dicha trayectoria de manera eficiente y en tiempo real, respetando las leyes de control de bajo nivel en el autopiloto y permitiendo flexibilidad. Como complemento a este último sistema, se propone la reconfiguración dinámica de las misiones para mejorar el consumo energético durante el tiempo de vuelo considerando el viento predominante. Uno de los principales objectivos fue integrar, utilizando la metodología de ingeniería de sistemas, las distintias funciones anteriormente mencionadas en el que la ejecución de la misión fuese la prioridad. El principal logro fue haber realizado una extensa campaña experimental que permitió la validación del sistema en diferentes niveles, en el que se combinaron pruebas computacionales de alto y bajo nivel así como pruebas de campo en distintos escenarios y con distintas plataformas, lo cual permitió explorar la versatilidad del sistema. Los resultados muestran que se pueden lograr misiones más eficientes con mejoras de hasta un 20%en consumo de batería para misiones costeras. Finalmente, de los distintos análisis computacionales efectuados se concluye que el tiempo de ejecución de toda la función de extensión del vuelo es lo suficientemente pequeño para permitir la ejecución en tiempo real, lo cual, combinando con el diseño versátil en cuestión de arquitectura computacional, permiten la portabilidad del sistema así como la futura integración de funciones adicionales.In this thesis a system that aims to extend the flight duration of small Unmanned Aerial Systems (UAS) is presented. The system was designed in the context of oceanic and coastal surveillance missions as part of the MarineUAS European project. Three main problems were identified: 1) the need to accurately estimate the wind field and the capability to identify features of interest, such as, wind shear, and gusts that may be suitable to allow energy extraction to improve flight duration. 2) the need to generate smooth trajectories that extract energy, considering the UAS platform dynamics and 3) the ability to follow such paths. For the first problem, the use of a direct computation method allows determining the wind field (wind velocity and wind rate of change) without the use of an optimal estimator. Nevertheless, different wind velocity estimation methods are compared, and the pros and cons of each are exposed; in addition, the identification of features is accomplished with a novel approach that performs a real-time statistical analysis of the distribution of the wind field estimates, allowing the characterization of the shear components and also any other potential features, like continuous and discrete gusts considering complex models that take into account not only the phenomena but the interactions with the ground and ocean through their respective boundary layers. For the second problem, a biomimetic approach is presented, replicating the trajectories of soaring birds by considering observations of these birds and the replication of their swooping maneuvers using smooth parametrized curves. This allows flexibility in the curve design and also the incorporation of dynamic constraints of the platform on it. The solution of the third problem takes into account the smooth curve that was generated and among it, a type 1 Bishop moving frame is generated. Then, a novel adaptive control method based on the vector-field theory approach is proposed to calculate the error equations and the respective control law, which permits the tracking of the designed trajectory for dynamic soaring. Furthermore, an additional step was added, in which the surveillance mission is re-configured on a waypoint-to-waypoint basis for a more efficient flight considering the identified wind field. The result was that the execution of soaring trajectories would not be executed during all the mission, but only in specific legs that fulfill specific characteristics.The primary goal was to design algorithms that implement these functions and to integrate these functionalities in a systems-engineering approach, in which the mission execution is the main priority. An extensive experimental campaign was performed at different levels, in which software-in-the-loop and hardwarein- the-loop tests, together with field tests, were executed to demonstrate the efficiency of the various functions separately and integrated. The field tests and the simulations consider different scenarios and UAS platforms, showing the performance of the system in different conditions. The results showed that the system could execute a more efficient mission, with savings of up to 20% in battery consumption, with the so-called of the Flight-Duration-Enhancement-System (FDES). Finally, the computational analysis showed that the system could be executed in real-time with minimum latency despite the use of sophisticated algorithms; this, together with the chosen software and hardware architectures allows portability to other hardware components and the possibility of incorporating additional functions

What Prompts the Transmission of Exchange Rate Movements into International Prices?

What is the causal effect of exchange rates on international prices over time when the state of the economy is continuously shifting? This thesis demonstrates that the existing reduced-form estimates of exchange rate pass-through are biased, at the very least over the longer term, and raises concerns over the long-standing disconnect between the average causal effect and the dynamic causal effect. A unique methodology is then developed to quantify an unbiased measure of exchange rate transmission at the firm-level from their observed co-movements at the aggregate level. In this framework, exchange rate impact on the transition path of prices from vintages to the inter-temporal optimum is determined by a compromise between economic structure and stochastic innovations. The blueprint builds on a micro-founded multi-country business cycle model that disciplines the structural parameters by the data on macroeconomic fundamentals using the method of moments. The substance of the quantitative predictions are exposited by addressing two broad research questions at the forefront of the policy debate in international economics. Specifically, (i) “What Drives the Terms of Trade Neutrality to Exchange Rates? "; and (ii) “Why Are Import Prices More Elastic To Local Currency Depreciations Than Appreciations?

Natural Time and Crash Risk

The deviation of financial returns from normal distribution is a well‐documented stylized fact. Nonetheless, finance professionals and investors alike pay attention to these deviations almost only when a crisis erases years’ worth of gains. And despite decades’ worth of literature, the culprit for non‐normal distribution of financial returns is still not determined with certainty. In this research, I address the non‐normality of return distributions and financial crashes together. Specifically, I aim to identify the determinants of non‐normality in a high frequency setting and utilize these variables to forecast financial crashes. To this effect, multiple instruments and time horizons are considered. The contribution of this thesis is multifold. The “natural time” approach introduced here, uses order book variables to achieve normally distributed high frequency returns via subordination. In its essence, natural time is a two‐step procedure which uses high frequency order book variables as a gauge for variance while sampling in transaction time. Natural time provides the reader with a new lens to view the financial markets and underscores two important aspects of the high frequency world; sampling frequency affects the distributions we observe and order book variables such as liquidity are the key to heteroscedasticity in asset returns. So much so that subordination with order book variables under transaction time achieves the normal return distribution which underlies numerous financial theories we use today. I further extend the use of these order book variables by introducing the “market heat” metric. Market heat generates successful binary flash crash predictions and its success adds support to the claim that liquidity concerns may be the primary driver of price formation processes. Finally, I expand the findings of this research on high frequency asset returns to a macroeconomic setting by producing currency devaluation predictions for G10 currencies. The early warning systems produced here demonstrate that not only debt related macroeconomic variables but also liquidity related market variables are at play when it comes to currency fluctuations