Explore the vast range of titles available.

This article addresses an optimisation problem of distributing rapid diagnostic kits among patients when the demands far surpass the supplies. This problem has not been given much attention in the field, and therefore, this article aims to provide a preliminary result in this problem domain. First, we describe the problem and define the goal of the optimisation by introducing an evaluation metric that measures the efficiency of the distribution strategies. Then, we propose two simple strategies, and a strategy that incorporates a prediction of patients’ visits utilising a standard epidemic model. The strategies were evaluated using the metric, with past statistics in Kitami City, Hokkaido, Japan, and the prediction-based strategy outperformed the other distribution strategies. We discuss the properties of the strategies and the limitations of the proposed approach. Although the problem must be generalised before the actual deployment of the suggested strategy, the preliminary result is promising in its ability to address the shortage of diagnostic capacity currently observed worldwide because of the ongoing coronavirus disease pandemic.

In this article, we model stock returns using fundamental data and minimizing average value at risk (AVaR) and multiperiod portfolio selection with weight and turnover constraints. Equity returns are decomposed into returns explained by fundamental and nonfundamental factors. While the former are found to be independent, the latter are found to be highly dependent among various stocks. Then, we construct models to forecast returns using several ARMA-GARCH models with different innovation distributions and simulate scenarios of future returns. Based on these scenarios, we examine various approaches of portfolio optimization. By comparing actual portfolios based on real data, we find that 1) the ARMA-GARCH model with classical tempered stable distribution provides a superior prediction of equity prices than the normal and Student's t-distribution and 2) AVaR provides a better risk measure than variance. We also see how portfolio performance changes under weight and turnover constraints and suggest that it is effective to reduce the stock universe and trade large-capitalization securities. [PUBLICATION ABSTRACT]

Risk analysis is one of the central parts of modern finance theory. It covers various topics: modeling, measuring, managing, and forecasting risk and returns. The purpose of this dissertation is to describe the recent methodology of risk analysis and to show its practical applications. Chapter 1 is dedicated to review the quantitative methodology of recent risk analysis, in which three components are presented in order to discuss its nature. The first component is the modeling of marginal distribution: The flaws of the Gaussian distribution and the alternative distributions based on the theory of the Levy process are discussed. The second component is the modeling of joint distribution: A copula model, a factor model, and independent component analysis are discussed. The third component is the definition of risk and its measurements: The idea of value at risk (VaR) is introduced. In Chapter 2, we present a practical example of how to construct a portfolio based on the return model and risk measure. The result is tested by the method of backtesting. Consequently, it is found that (1) the ARMA-GARCH model with classical tempered stable (CTS) distribution provides better prediction than that with the normal and Student-t distribution, and (2) average VaR (AVaR) provides a better risk measure than variance. It is also suggested that the number of universe has effect on the portfolio return, and that it is effective to reduce stock universe to large capitalization stocks. In Chapter 3, we analyze the distribution of returns on seven major Eurozone sovereign bonds (France, Germany, Greece, Ireland, Italy, Portugal, and Spain) and their co-movement. We investigate the ARMA-GARCH models based on different assumptions about the innovations: Gaussian, Student-t, CTS, normal tempered stable (NTS), and α-stable. For each of the five models, we apply four copula functions, and assess the forecasting performance of combinations of these models. In addition, to find a forward-looking measure to detect the financial crisis of Greece, we analyze the evolution of the tail parameter over time. In Chapter 4, we discusses the goodness of fitting of independent component analysis (ICA) using the sovereign CDS premiums of 11 Eurozone countries (Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Portugal, and Spain). Based on the log-likelihood, the Akaike information criterion and the Bayesian information criterion, we first show the fitness of ICA is as good as more complicated models such as the ARMA-GARCH and Student-t copula model. Also, using the structural default model based on CDS premium, we found that the characteristics of joint defaults based on ICA seem different from those based on copula.

In this article, we analyze the distribution of returns on seven major Eurozone sovereign bonds and their co-movement for the period 2001 to 2011. We investigate five ARMAGARCH models based on different innovation distributions: Gaussian, Student-i, classical tempered stable, normal tempered stable, and (X-stable. For each model, we apply four copula dependence structures: Gaussian, Student-i, skewed Student-i, and multivariate normal tempered stable. Finally, we assess the forecasting performance of these models, and provide a forward-looking measure of the financial crisis of Greece.