Forecasting volatility using GARCH models

Dissertação de mestrado em FinançasEsta dissertação tem como ponto central a previsão da volatilidade usando vários modelos GARCH (General autoregressive conditional heteroeskedasticity) de modo a testar qual tem a melhor capacidade de previsão. O foco desta dissertação é o estudo do mercado dos EUA.Os dados usados para este estudo são cotações do NASDAQ-100, de 1986 até 2016. Neste estudo são considerados três períodos de estimação para os modelos GARCH: 500 dias, 1000 dias e 2000 dias de modo a minimizar a possível presença de mudanças na estrutura dos dados. Regressões lineares (Mincer-Zarnowitz) foram efectuadas de forma a avaliar a performance individual de cada modelo GARCH. Depois disso, de forma a detectar qual o melhor modelo para prever a volatilidade, o teste de SPA de Hansen and Lunde (2005) foi utilizado. Os resultados são conclusivos de que os modelos são semelhantes no que toca à previsão da volatilidade condicional do dia seguinte, com a possível excepção do modelo IGARCH. O modelo GJR não apresenta resultados satisfatórios quando a janela de estimação utilizada na estimação dos modelos é de 1000 dias.The purpose of these research is to forecast volatility using different GARCH (General autoregressive conditional heteroeskedasticity) models in order to test which model has best forecasting ability. The focus of this research is the US market. The data is composed by NASDAQ-100 quotations from 1986 to 2016. The study considers three estimation periods for the GARCH family models: 500 days, 1000 days and 2000 days in order to minimize structure changes that might be present in the data. A series of Mincer-Zarnowitz regressions were completed in order to assess the performance of each GARCH model. Afterwards, the SPA test from Hansen and Lunde (2005) is used in order to detect which is the best model. The empirical results show that the GARCH models produce similar results in what comes to forecasting next day conditional volatility, with the possible exception of the IGARCH model. There is also reason to believe that the GJR model does not provide good estimations of volatility when the rolling window used in the estimation of the models is 1000 days

Remote boot manager for Raspberry Pi cluster

Dissertação de mestrado em Engenharia Eletrónica Industrial e ComputadoresThe increasing applicability and integration of interconnected embedded systems (clusters) in bigger products and systems has been contributing for an increase in efficiency and utility of the later, due to the clusters’ fast processing and multitasking abilities, and even their low power consumption. With the use of those clusters, data acquirement, communication and other small yet important tasks are executed faster and more efficiency. Given this, it has become obvious that being able to supervise, manage and control these clusters is essential to ensure the proper functioning of the whole system. After doing a thorough research on papers and products that aim to manage and communicate with multiple microcontrollers, the conclusion taken is that none fulfil the requirements proposed in thisMaster’s thesis, which are to communicate, detect boot errors and burn a desired OS at any time in each of the cluster’s Raspberry Pi. The aim of this Master’s thesis was to develop a Central Monitoring System for Raspberry Pi clusters which takes into account mainly these three requirements. A permanent TCP/IP connection with each of the cluster’s Raspberry Pi was established, for data and command exchanging. A GUI was also developed, which displays updated information about each of the Raspberry Pi and allows for a easy management of each of them individually or all together. The GUI also makes it possible to upload and download any OS to an FTP server, to later be burned to a Raspberry Pi. The integration of this Monitoring System in already existing products can have very good implications and improve performance and efficiency, as the work, cost and time of maintenance have been reduced. The whole system becomes more versatile, as the cluster can change its role, by burning a different OS on demand.A crescente aplicabilidade e integração de sistemas embebidos interconectados (clusters) em produtos e sistemas maiores tem vindo a contribuir para um aumento da eficiência e utilidade dos últimos, devido à rapidez de processamento e capacidade de fazer várias tarefas ao mesmo tempo, e até ao seu baixo consumo de energia. Com o uso destesclusters, aquisição de data, comunicação e outras tarefas pequenas mas importantes são executadas mais rapidamente e com mais eficácia. Dado isto, tornou-se óbvio que ser capaz de supervisionar, gerir e controlar esses clusters é essencial para assegurar o bom funcionamento de todo o sistema. Depois de fazer uma pesquisa intensiva em papers e produtos que visam gerir e comunicar com vários microcontroladores, a conlusão a que se chega é que nenhum cumpre os requisitos propostos nesta Dissertação, que são comunicar, detetar a ocorrência de erros de arranque e instalar qualquer sistema operativo, a qualquer momento, em cada Raspberry Pi do cluster. O objetivo desta Dissertação foi desenvolver um Sistema de Monitorização Central para clusters de Raspberry Pi que tem em conta principalmente estes três requisitos. Foi estabelecida uma conexão TCP/IP permanente com cada Raspberry Pi do cluster, para troca de dados e comandos. Também foi desenvolvida uma Interface Gráfica do Utilizador, que mostra informação atualizada sobre todas as Raspberry PI do cluster e permite uma gestão individual ou coletiva fácil. A Interface Gráfica do Utilizador também faz com que seja possível fazer o upload e download de qualquer Sistema Operativo para um servidor FTP, para mais tarde ser instalado em qualquer Raspberry PI. A integração deste Sistema de Monitorização em produtos já existentes pode ter implicações muito positivas e melhorar eficácia e eficiência, uma vez que o trabalho, tempo e custo de manutenção foram reduzidos. O sistema completo torna-se mais versátil, uma vez que o cluster pode mudar a sua função, ao instalar um Sistema Operativo quando solicitado

The Forward- and the Equity-Premium Puzzles: Two Symptoms of the Same Illness?

We build a pricing kernel using only US domestic assets data and checkwhether it accounts for foreign markets stylized facts that escape consumptionbased models. By interpreting our stochastic discount factor as the projection ofa pricing kernel from a fully specified model in the space of returns, our results indicatethat a model that accounts for the behavior of domestic assets goes a longway toward accounting for the behavior of foreign assets. We address predictabilityissues associated with the forward premium puzzle by: i) using instrumentsthat are known to forecast excess returns in the moments restrictions associatedwith Euler equations, and; ii) by pricing Lustig and Verdelhan (2007)'s foreigncurrency portfolios. Our results indicate that the relevant state variables that explainforeign-currency market asset prices are also the driving forces behind U.S.domestic assets behavior.

The forward- and the equity-premium puzzles: two symptoms of the same illness?

Using information on US domestic financial data only, we build a stochastic discountfactor—SDF— and check whether it accounts for foreign markets stylized factsthat escape consumption based models. By interpreting our SDF as the projection ofa pricing kernel from a fully specified model in the space of returns, our results indicatethat a model that accounts for the behavior of domestic assets goes a long waytoward accounting for the behavior of foreign assets prices. We address predictabilityissues associated with the forward premium puzzle by: i) using instruments that areknown to forecast excess returns in the moments restrictions associated with Eulerequations, and; ii) by pricing Lustig and Verdelhan (2007)’s foreign currency portfolios.Our results indicate that the relevant state variables that explain foreign-currencymarket asset prices are also the driving forces behind U.S. domestic assets behavior.

Attitude change in arbitrarily large organizations

The alignment of collective goals and individual behavior has been extensively studied by economists under a principal-agent framework. Two main solutions have been presented: explicit incentive contracts and monitoring. These solutions correspond to changes in the objective situation faced by individuals. However, an extensive literature in social psychology provides evidence that behavior is influenced, not only by situational constraints, but also by attitudes. Therefore, an important aspect of organization is to choose the structures and procedures that best contribute to the dissemination of the desired attitudes throughout the organization. This paper studies how the initial configuration of attitudes and the size of the organization affect the optimal organizational structure and the timing of information flows when the objective is to align the members' attitudes. We identify and characterize three factors that affect the optimal organizational structures and procedures and the degree of alignment of attitudes: (1) clustering effects; (2) member cross-influence effects; and (3) leader cross-influence effects

Monitoring of Spatio-Temporal Properties with Nonlinear SAT solvers

Funding Information: Open access funding provided by FCT|FCCN (b-on). This work is supported by the EU/Next Generation EU, through Programa de Recuperação e Resiliência (PRR) [Project Route 25 No. C645463824-00000063]. This work was also partially supported by: i) the European Regional Development Fund (ERDF) through the Competitiveness and Internationalization Operational Program (COMPETE 2020) of Portugal 2020 [Project STEROID with No. 069989 (POCI-01-0247-FEDER-069989)]. Publisher Copyright: © The Author(s) 2024.The automotive industry is increasingly dependent on computing systems with different critical requirements. The verification and validation methods for these systems are now leveraging complex AI methods, for which the decision algorithms introduce non-determinism, especially in autonomous driving. This paper presents a runtime verification technique agnostic to the target system, which focuses on monitoring spatio-temporal properties that abstract the evolution of objects’ behavior in their spatial and temporal flow. First, a formalization of three known traffic rules (from the Vienna convention on road traffic) is presented, where a spatio-temporal logic fragment is used. Then, these logical expressions are translated to a monitoring model written in first-order logic, where they are processed by a non-linear satisfiability solver. Finally, the translation allows the solver to check the validity of the encoded properties according to an instance of a specific traffic scenario (a trace). The results obtained from our tool, which automatically generates a monitor from a formula, show that our approach is feasible for online monitoring in a real-world environment.publishersversionpublishe