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

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

Leading indicator properties of the US corporate spreads

The focus of this paper is on the leading indicator properties of high-yield corporate spreads regarding the level of real economic activity. This is motivated by both the financial accelerator mechanism underlying business cycle fluctuations as suggested by Bernanke and Gertler (1989). We examine the out-of-sample forecast performance of the high-yield spreads regarding employment and industrial production in the US, using both a point forecast and a probability forecast exercise. Our main findings suggest the use of few factors obtained by pooling information from a number of sub sectors high-yield credit spreads. This can be justified by observing that there is a substantial gain from using a Dynamic Factor fitted to credit spreads compared to the prediction produced by benchmarks, such as an AR and ARDL models, where the exogenous regressor is either the term spread, or the aggregate high-yield spread.credit spreads, dynamic factor, forecasting

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

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

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

Business cycle effects on Portfolio Credit Risk: scenario generation through Dynamic Factor analysis

In this paper, we focus on measuring the risk associated to a bank loan portfolio. In particular, we depart from the standard one factor model representation of portfolio credit risk. In particular, we consider an hetrogeneous portfolio, and we account for stochastic dependent recoveries. We also examine the influence of either one systemic shock (interpreted as the state of the business cycle) or two systemic shocks (interpreted as demand and supply innovations) on portfolio credit risk. The identification and estimation of the common shocks is obtained by fitting a Dynamic Factor model to a large number of macro credit drivers. The scenarios are obtained by employing Montecarlo stochastic simulation.Risk management default correlation Dynamic Factor