Multiple satellite observations of oceanic planetary waves: techniques and findings

Recent satellite-based observations of oceanic planetary waves have improved our knowledge of the wave properties and lead to advancements in the theory. Firstly, we review some of the techniques adopted to extract the information on planetary wave properties, illustrating them with examples based on satellite altimeter data, and we summarize the main findings. Then we discuss a cross-spectral approach for the comparison of the wave signals in the different datasets (altimetry, ocean colour and sea surface temperature) that is necessary to unveil the causes of the newly found wave signature in satellite-derived maps of phytoplankton chlorophyll, a discovery that is attracting considerable interest as it implies some effects of the waves on biology

Forecasting Financial Crises and Contagion in Asia using Dynamic Factor Analysis

In this paper we use principal components analysis to obtain vulnerability indicators able to predict financial turmoil. Probit modelling through principal components and also stochastic simulation of a Dynamic Factor model are used to produce the corresponding probability forecasts regarding the currency crisis events a®ecting a number of East Asian countries during the 1997-1998 period. The principal components model improves upon a number of competing models, in terms of out-of-sample forecasting performance.Financial Contagion, Dynamic Factor Model

Forecasting Financial Crises and Contagion in Asia Using Dynamic Factor Analysis

In this paper we compare the performance of a regional indicator of vulnerability in predicting, out of sample, the crisis events affecting the South East Asian region during the 1997-98 period. A Dynamic Factor method was used to retrieve the vulnerability indicator and stochastic simulation is used to produce probability forecasts. The empirical findings suggest evidence of financial contagion.Financial contagion, Dynamic factor model

Measuring bank capital requirements through Dynamic Factor analysis

In this paper, using industry sector stock returns as proxies of firm asset values, we obtain bank capital requirements (through the cycle). This is achieved by Montecarlo simulation of a bank loan portfolio loss density. We depart from the Basel 2 analytical formula developed by Gordy (2003) for the computation of the economic capital by, first, allowing dynamic heterogeneity in the factor loadings, and, also, by accounting for stochastic dependent recoveries. Dynamic heterogeneity in the factor loadings is introduced by using dynamic forecast of a Dynamic Factor model fitted to a large dataset of macroeconomic credit drivers. The empirical findings show that there is a decrease in the degree of Portfolio Credit Risk, once we move from the Basel 2 analytic formula to the Dynamic Factor model specification.Dynamic Factor Model, Forecasting, Stochastic Simulation, Risk Management, Banking

A Dynamic Factor Analysis of Financial Contagion in Asia

In this paper we compared the performance of country specific and regional indicators of reserve adequacy in predicting, out of sample, the balance of payment crisis affecting the South East Asian region during the 1997-98 period. A Dynamic Factor method was used to retrieve reserve adequacy indicators. The empirical findings suggest clear evidence of financial contagion.Financial contagion, Dynamic factor model

Dynamic Factor analysis of industry sector default rates and implication for Portfolio Credit Risk Modelling

In this paper we use a reduced form model for the analysis of Portfolio Credit Risk. For this purpose, we fit a Dynamic Factor model, DF, to a large dataset of default rates proxies and macrovariables for Italy. Multi step ahead density and probability forecasts are obtained by employing both the direct and indirect method of prediction together with stochastic simulation of the DF model. We, first, find that the direct method is the best performer regarding the out of sample projection of financial distressful events. In a second stage of the analysis, we find that reduced form Portfolio Credit Risk measures obtained through DF are lower than the one corresponding to the Internal Ratings Based analytic formula suggested by Basel 2. Moreover, the direct method of forecasting gives the smallest Portfolio Credit Risk measures. Finally, when using the indirect method of forecasting, the simulation results suggest that an increase in the number of dynamic factors (for a given number of principal components) increases Portfolio Credit Risk.Dynamic Factor Model, Forecasting, Stochastic Simulation, Risk Management, Banking

A Stochastic Variance Factor Model for Large Datasets and an Application to S&P Data

The aim of this paper is to consider multivariate stochastic volatility models for large dimensional datasets. We suggest use of the principal component methodology of Stock and Watson (2002) for the stochastic volatility factor model discussed by Harvey, Ruiz, and Shephard (1994). The method is simple and computationally tractable for very large datasets. We provide theoretical results on this method and apply it to S&P data.Stochastic volatility, Factor models, Principal components

SOFT feature-tracking software handbook

This handbook (SOFT_WP31_handbook.pdf) describes the suite of MATLAB programs developed within Work Package 3, task 3.1 of the SOFT Project, for the tracking of large-scale, westward propagating features (planetary waves or westward-travelling eddies) in altimeter data and the removal of the identified features from the datasets. The suite has been applied to TOPEX/POSEIDON data over the Azores region (one of the SOFT study regions) but its modularity makes it adaptable in a straightforward way to other datasets and other regions. The companion to this handbook is the progress report on task 3.1 released in January 2003 (SOFT_WP31_report.pdf), which presents the rationale to the study and gives ample details on the scheme adopted for the fitting of elementary waves (according to a Gaussian wave shape model) to altimeter data. A synopsis of the fitting scheme is briefly recalled in the following sections of this document, for the benefit of the reader. All the code listings are in the appendix. The forecasting of the westward-propagating fields (which is the object of task 3.2 in Work Package 3 id described in version 1 of another report, SOFT_WP32_rep1.pdf

SOFT Wave forecasting report - v.1.0

This report (SOFT_WP32_rep1.pdf) describes the first version of the wave forecasting code developed within Work Package 3, task 3.2 (implementation of a hybrid SOFT tracking system) of the SOFT Project. The forecasting of westward propagating signals (planetary waves or westward-travelling eddies), using the fields of tracked wave from Work Package 3, task 3.1, is one of the two components of the hybrid system which is the overall deliverable of task 3.2. The results presented here are provisional and are likely to be replaced as research proceeds. Related to this report are two other documents:- the progress report on task 3.1 released in January 2003(SOFT_WP31_report.pdf), which presents the rationale to the study and gives ample details on the scheme adopted for the fitting of elementary waves (according to a Gaussian wave shape model) to altimeter data (see also the paper by Cipollini, 2003);- the handbook SOFT_WP31_handbook.pdf describing the suite of MATLAB programs developed within Work Package 3, task 3.1 of the SOFTProject, for the tracking of large-scale, westward propagating features (planetary waves or westward-travelling eddies) in altimeter data and the removal of the identified features from the datasets. The suite has been applied to TOPEX/POSEIDON data over the Azores region (one of the SOFTstudy regions) and the output results have been used for the forecast

SOFT Development of feature tracking methods

The present report describes the work carried out within task 3.1 of Work Package 3 of the SOFT Project. The above task is ‘Development of feature tracking methods’ and consists of the development of a software to track large-scale, westward propagating features (planetary waves or westward-travelling eddies) in the altimetric datasets, and in the removal of the identified features from the datasets. The residual field (that is the original dataset minus the tracked features) is then made available to the other work packages in the Project