Application of Stationary Wavelet Support Vector Machines for the Prediction of Economic Recessions

This paper examines the efficiency of various approaches on the classification and prediction of economic expansion and recession periods in United Kingdom. Four approaches are applied. The first is discrete choice models using Logit and Probit regressions, while the second approach is a Markov Switching Regime (MSR) Model with Time-Varying Transition Probabilities. The third approach refers on Support Vector Machines (SVM), while the fourth approach proposed in this study is a Stationary Wavelet SVM modelling. The findings show that SW-SVM and MSR present the best forecasting performance, in the out-of sample period. In addition, the forecasts for period 2012-2015 are provided using all approaches

Forecasting Exchange Rate Volatility: The Superior Performance of Conditional Combinations of Time Series and Option Implied Forecasts

This paper provides empirical evidence that combinations of option implied and time series volatility forecasts that are conditional on current information are statistically superior to individual models, unconditional combinations, and hybrid forecasts. Superior forecasting performance is achieved by both, taking into account the conditional expected performance of each model given current information, and combining individual forecasts. The method used in this paper to produce conditional combinations extends the application of conditional predictive ability tests to select forecast combinations. The application is for volatility forecasts of the Mexican Peso-US Dollar exchange rate, where realized volatility calculated using intra-day data is used as a proxy for the (latent) daily volatility.Composite Forecasts, Forecast Evaluation, GARCH, Implied volatility, Mexican Peso-U.S. Dollar Exchange Rate, Regime-Switching

Stability periods between financial crises : The role of macroeconomic fundamentals and crises management policies

The aim of this paper is to identify which factors explain why some countries are more prone to enjoy long durations of stability, while others experience crises in shorter intervals. To this end, we analyze the duration of stability periods between currency, debt, and banking crises from 1980 to 2008. We find that durations of tranquility between currency and debt crises are bimodally distributed, making conventional econometric models unsuitable. Therefore, we introduce an innovative econometric strategy, the Finite Mixture Model. Real and financial variables are found to have high predictive power for the spell of stability between currency crises, while for debt crises, the real interest rate is observed to be the best predictor. The time between the occurrence of systemic financial crises is prolonged through large-scale government interventions and IMF aid programs, while recapitalization turns out to have a negative impact.Financial crises, Finite mixture model, duration, bimodality.

Towards a new early warning system of financial crises

This paper develops a new Early Warning System (EWS) model for predicting financial crises, based on a multinomial logit model. It is shown that EWS approaches based on binomial discrete-dependent-variable models can be subject to what we call a post-crisis bias. This bias arises when no distinction is made between tranquil periods, when economic fundamentals are largely sound and sustainable, and crisis/post-crisis periods, when economic variables go through an adjustment process before reaching a more sustainable level or growth path. We show that applying a multinomial logit model, which allows distinguishing between more than two states, is a valid way of solving this problem and constitutes a substantial improvement in the ability to forecast financial crises. The empirical results reveal that, for a set of 32 open emerging markets from 1993 till the present, the model would have correctly predicted a large majority of crises in emerging markets. Moreover, we derive general results about the optimal design of EWS models, which allows policy-makers to make an optimal choice based on their degree of risk-aversion against unanticipated financial crises. JEL Classification: F31, F47, F30crisis prediction, currency crises, Early Warning System

Towards A New Early Warning System of Financial Crises

This paper develops a new Early Warning System (EWS) model for predicting financial crises, based on a multinomial logit model. It is shown that EWS approaches based on binomial discrete-dependent-variable models can be subject to what we call a post-crisis bias. This bias arises when no distinction is made between tranquil periods, when economic fundamentals are largely sound and sustainable, and crisis/post-crisis periods, when economic variables go through an adjustment process before reaching a more sustainable level or growth path. We show that applying a multinomial logit model, which allows distinguishing between more than two states, is a valid way of solving this problem and constitutes a substantial improvement in the ability to forecast financial crises. The empirical results reveal that, for a set of 32 open emerging markets from 1993 till the present, the model would have correctly predicted a large majority of crises in emerging markets. Moreover, we derive general results about the optimal design of EWS models, which allows policy-makers to make an optimal choice based on their degree of risk-aversion against unanticipated financial crises.currency crises, Early Warning System, crisis prediction

On currency crises and contagion

This paper analyzes the role of contagion in the currency crises in emerging markets during the 1990s. It employs a non-linear Markov-switching model to conduct a systematic comparison and evaluation of three distinct causes of currency crises: contagion, weak economic fundamentals, and sunspots, i.e. unobservable shifts in agents' beliefs. Testing this model empirically through Markov-switching and panel data models reveals that contagion, i.e. a high degree of real integration and financial interdependence among countries, is a core explanation for recent emerging market crises. The model has a remarkably good predictive power for the 1997-98 Asian crisis. The findings suggest that in particular the degree of financial interdependence and also real integration among emerging markets are crucial not only in explaining past crises but also in predicting the transmission of future financial crises. JEL Classification: F30, E60, E65, E44

Distribution and Dynamics of Central-European Exchange Rates: Evidence from Intraday Data

This paper investigates the behavior of the EUR/CZK, EUR/HUF and EUR/PLN spot exchange rates in the period 2002–2008, using 5-minute intraday data. The authors find that daily returns on the corresponding exchange rates scaled by model-free estimates of daily realized volatility are approximately normally distributed and independent over time. On the other hand, daily realized variances exhibit substantial positive skewness and very persistent, long-memory type of dynamics. The authors estimate a simple three-equation model for daily returns, realized variance and the time-varying volatility of realized variance. The model captures all salient features of the data very well and can be successfully employed for constructing point, as well as density forecasts for future volatility. The authors also discuss some issues associated with measuring volatility from the noisy high-frequency data and employ a simple correction that accounts for the distortions present in our dataset.intraday data, realized variance, return and volatility distributions, heterogeneous autoregressive model

The Dynamic Relationship between Stock Prices and Exchange Rates: evidence for Brazil

This paper studies the dynamic relationship between stock prices and exchange rates in the Brazilian economy. We use recently developed unit root and cointegration tests, which allow endogenous breaks, to test for a long run relationship between these variables. We performed linear, and nonlinear causality tests after considering both volatility and linear dependence. We found that there is no long-run relationship, but there is linear Granger causality from stock prices to exchange rates, in line with the portfolio approach: stock prices lead exchange rates with a negative correlation. Furthermore, we found evidence of nonlinear Granger causality from exchange rates to stock prices, in line with the traditional approach: exchange rates lead stock prices. We believe these findings have practical applications for international investors

Determinants of Currency Crises in Emerging Markets: An Empirical Investigation on Turkey

This article aims at identifying the determinants of currency crises in Turkey the period 1980:01-2006:06. Following a general-to-specific model selection methodology, a broad set of pre-selected variables were tested through bivariate logit regressions. Significant variables were then used in a multivariate logit model. Strong evidence emerged that current account balance/GDP, short-term debt/long-term debt, domestic credit/GDP, foreign liabilities/foreign assets of banks, and fiscal balance/GDP are significant with correct signs. The measures of goodness-of-fit and in-sample predictive power of the model turned out to be favorable. The resulting model correctly calls 87.18% and 73.08% of the months at 10% and 20% levels, respectively.Speculative attacks; currency crises; logit model, Turkey.