Financial Risk Measurement for Financial Risk Management

Current practice largely follows restrictive approaches to market risk measurement, such as historical simulation or RiskMetrics. In contrast, we propose flexible methods that exploit recent developments in financial econometrics and are likely to produce more accurate risk assessments, treating both portfolio-level and asset-level analysis. Asset-level analysis is particularly challenging because the demands of real-world risk management in financial institutions - in particular, real-time risk tracking in very high-dimensional situations - impose strict limits on model complexity. Hence we stress powerful yet parsimonious models that are easily estimated. In addition, we emphasize the need for deeper understanding of the links between market risk and macroeconomic fundamentals, focusing primarily on links among equity return volatilities, real growth, and real growth volatilities. Throughout, we strive not only to deepen our scientific understanding of market risk, but also cross-fertilize the academic and practitioner communities, promoting improved market risk measurement technologies that draw on the best of both.Market risk, volatility, GARCH

Multimodal Planning under Uncertainty: Task-Motion Planning and Collision Avoidance

openIn this thesis we investigate the problem of motion planning under environment uncertainty. Specifically, we focus on Task-Motion Planning (TMP) and probabilistic collision avoidance which are presented as two parts in this thesis. Though the two parts are largely self-contained, collision avoidance is an integral part of TMP or any robot motion planning problem in general. The problem of TMP which is the subject of Part I is by itself challenging and hence in Part I, collision computation is not the main focus and is addressed with a deterministic approach. Moreover, motion planning is performed offline since we assume static obstacles in the environment. Online TMP, incorporating dynamic obstacles or other environment changes is rather difficult due to the computational challenges associated with updating the changing task domain. As such, we devote Part II entirely to the field of online probabilistic collision avoidance motion planning. Of late, TMP for manipulation has attracted significant interest resulting in a proliferation of different approaches. In contrast, TMP for navigation has received considerably less attention. Autonomous robots operating in real-world complex scenarios require planning in the discrete (task) space and the continuous (motion) space. In knowledge-intensive domains, on the one hand, a robot has to reason at the highest-level, for example, the objects to procure, the regions to navigate to in order to acquire them; on the other hand, the feasibility of the respective navigation tasks have to be checked at the execution level. This presents a need for motion-planning-aware task planners. In Part I of this thesis, we discuss a probabilistically complete approach that leverages this task-motion interaction for navigating in large knowledge-intensive domains, returning a plan that is optimal at the task-level. The framework is intended for motion planning under motion and sensing uncertainty, which is formally known as Belief Space Planning (BSP). The underlying methodology is validated in simulation, in an office environment and its scalability is tested in the larger Willow Garage world. A reasonable comparison with a work that is closest to our approach is also provided. We also demonstrate the adaptability of our method by considering a building floor navigation domain. Finally, we also discuss the limitations of our approach and put forward suggestions for improvements and future work. In Part II of this thesis, we present a BSP framework that accounts for the landmark uncertainties during robot localization. We further extend the state-of-the-art by computing an exact expression for the collision probability under Gaussian motion and perception uncertainties. Existing BSP approaches assume that the landmark locations are well known or are known with little uncertainty. However, this might not be true in practice. Noisy sensors and imperfect motions compound to the errors originating from the estimate of environment features. Moreover, possible occlusions and dynamic objects in the environment render imperfect landmark estimation. Consequently, not considering this uncertainty can result in wrongly localizing the robot, leading to inefficient plans. Our approach incorporates the landmark uncertainty within the Bayes filter framework. We also analyze the effect of considering this uncertainty and delineate the conditions under which it can be ignored. Furthermore, we also investigate the problem of safe motion planning under Gaussian motion and sensing uncertainties. Existing approaches approximate the collision probability using upper-bounds that can lead to overly conservative estimate and thereby suboptimal plans. We formulate the collision probability process as a quadratic form in random variables. Under Gaussian distribution assumptions, an exact expression for collision probability is thus obtained which is computable in real-time. Further, we compute a tight upper bound for fast online computation of collision probability and also derive a collision avoidance constraint to be used in an optimization setting. We demonstrate and evaluate our approach using a theoretical example and simulations in single and multi-robot settings using mobile and aerial robots. A comparison of our approach to different state-of-the-art methods are also provided.openXXXIII CICLO - BIOINGEGNERIA E ROBOTICA - BIOENGINEERING AND ROBOTICSThomas, Anton

Saddle-point approach: backtesting VaR models in the presence of extreme losses

The Basel Committee for Banking Supervision requires every financial institution to carry out efficient Risk Management practices, so that these are able to face adverse days in the market and, thus, avoid another potential meltdown of the financial system, such as the 'Black Monday' in 1987 or the 'Subprime' crisis in 2007. To do so, traditional backtesting techniques assess the quality of commercial banks’ risk forecasts based on the number of the exceedances. However, these backtests are not sensitive to the size of the exceedances, which could lead to inaccurate risk models to be accepted. This way, this dissertation presents the Saddle-point backtest, a size-based procedure developed by Wong (2008) that evaluates risk models through the Tail-Risk-of-VaR. This approach is believed to constitute a reliable size counterpart to the Basel II Agreements, hence deserving an important role in backtesting. However, the Saddle-point backtest shows some drawbacks regarding its application to non-parametric risk models, which is explored throughout this dissertation’s empirical analysis.O Comité de Basileia para a Supervisão Bancária requer a todas as instituições financeiras que levem a cabo práticas de Gestão de Risco eficientes, de modo a que estas sejam capazes de enfrentar dias adversos no mercado e, desta forma, evitar outro eventual colapso do sistema financeiro, tal como a 'Segunda-feira Negra' em 1987 ou a crise do 'Subprime' em 2007. Para tal, as técnicas tradicionais de avaliação de modelos de risco aferem a qualidade das previsões dos bancos com base no número de excedências. No entanto, estes métodos não são sensíveis ao tamanho das excedências, o que pode levar a que modelos de risco pouco fiáveis sejam aceites. Assim sendo, esta dissertação apresenta o teste de Saddle-point, um procedimento baseado no tamanho das excedências desenvolvido por Wong (2008), que avalia modelos de risco através do Risco-da-Cauda do Valor em Risco. Crê-se que esta abordagem baseada no tamanho das excedências constitui uma fiável contraparte dos Acordos de Basileia II, merecendo, portanto, desempenhar um papel importante na avaliação de modelos de risco. No entanto, o teste de Saddle-point apresenta algumas falhas no que toca à sua aplicação a modelos de risco não paramétricos, algo que é explorado no decorrer da análise empírica desta dissertação

Disoriented Chiral Condensate: Theory and Experiment

It is thought that a region of pseudo-vacuum, where the chiral order parameter is misaligned from its vacuum orientation in isospin space, might occasionally form in high energy hadronic or nuclear collisions. The possible detection of such disoriented chiral condensate (DCC) would provide useful information about the chiral structure of the QCD vacuum and/or the chiral phase transition of strong interactions at high temperature. We review the theoretical developments concerning the possible DCC formation in high-energy collisions as well as the various experimental searches that have been performed so far. We discuss future prospects for upcoming DCC searches, e.g. in high-energy heavy-ion collision experiments at RHIC and LHC.Comment: 120 pages, 52 figures. Uses elsart.cls. To appear in Physics Reports. Minor corrections, references adde