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

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

Forecasting Financial Crises and Contagion in Asia using Dynamic Factor Analysis

In this paper we use a Dynamic Factor model to retrieve vulnerability indicators able to predict financial turmoil. A stochastic simulation experiment is then 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 Dynamic factor model improves upon a number of competing model, in terms of out of sample forecasting performanceFinancial Contagion, Dynamic Factor Model, Stochastic Simulation

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

Forecasting Large Datasets with Reduced Rank Multivariate Models

The paper addresses the issue of forecasting a large set of variables using multivariate models. In particular, we propose three alternative reduced rank forecasting models and compare their predictive performance with the most promising existing alternatives, namely, factor models, large scale bayesian VARs, and multivariate boosting. Specifically, we focus on classical reduced rank regression, a two-step procedure that applies, in turn, shrinkage and reduced rank restrictions, and the reduced rank bayesian VAR of Geweke (1996). As a result, we found that using shrinkage and rank reduction in combination rather than separately improves substantially the accuracy of forecasts, both when the whole set of variables is to be forecast, and for key variables such as industrial production growth, inflation, and the federal funds rate.Bayesian VARs, Factor models, Forecasting, Reduced rank

Capturing Aggregate Flexibility in Demand Response

Flexibility in electric power consumption can be leveraged by Demand Response (DR) programs. The goal of this paper is to systematically capture the inherent aggregate flexibility of a population of appliances. We do so by clustering individual loads based on their characteristics and service constraints. We highlight the challenges associated with learning the customer response to economic incentives while applying demand side management to heterogeneous appliances. We also develop a framework to quantify customer privacy in direct load scheduling programs.Comment: Submitted to IEEE CDC 201