Early-warning analysis for currency crises in emerging markets: A revisit with fuzzy clustering

Currency crises, also often called balance-of-payment crises, occur when massive capital outflows force a country to devalue or float its currency. The world-wide integration of capital markets since the 1980s and 1990s has increased the degree of capital mobility, which also determined a substantial turbulence in foreign exchange markets and frequent currency crises. In this paper, we explore advanced supporting instruments for predicting currency crises, based on an empirical study of the currency crisis episodes in 23 emerging markets around the world during the second half of last century. More specifically, we investigate the usefulness of prediction models built based on the fuzzy c-means method. First we build clustering models that partition data into a certain number of overlapping natural groups. Thereafter, we classify the data clusters into early-warning clusters and tranquil clusters. We compare the performance of our models with a conventional c-means clustering model and a benchmark probit model. The results show that the proposed models achieve a similar level of out-of-sample performance as the probit model and c-means model. The fuzzy approach also introduces additional explanatory advantages into the early-warning analysis process. Copyright © 2010 John Wiley & Sons, Ltd.     

Urban and suburban retail development in Albania's capital after socialism

This article discusses trends in the distribution of retail development in Tirana (the capital of Albania) and its surrounding areas. The story of these trends in Tirana exhibits unique characteristics connected to the city's exceptional population growth and exceptional densification since the end of the communist era in 1990. Tirana provides an example of how a center city can continue to dominate retail commerce when there are very limited restraints on residential and commercial density in the inner city and very limited foreign investment in retail development.     

Multivariate Analysis of Premium Dynamics in P&L Insurance

This article studies the dynamic relationship between premiums and losses on the U.S. property–casualty insurance market, accounting for the external impacts of GDP and interest rate. Compared to the existing literature, the present work innovates in that the dynamic relationships between premiums, losses, GDP, and interest rate are studied in a cointegration framework, single‐equation and vector approach, involving the long‐ and short‐run dynamics. The results suggest a stable long‐run equilibrium between premiums, losses, and general economy. On short term, the premiums adjust quickly and significantly to the long‐term disequilibrium and have a strong autoregressive behavior. External factors contribute to explain the dynamics of premiums.     

Martingale difference hypothesis and financial crisis: Empirical evidence from European emerging foreign exchange markets

This study investigates the effects of the recent global crisis on the relative efficiency of six CEE currency markets, using the generalized spectral test of Escanciano and Velasco (2006) in a rolling window approach. The empirical results show that the global crisis adversely affected the efficiency of most CEE currency markets, with the Turkish lira being hit the hardest, followed by the Russian ruble, Czech koruna, Romanian leu, Polish zloty and Hungarian forint. In the first stage of the crisis, covering the second half of 2008 and the first months of 2009, all foreign exchange markets experienced periods of inefficiency. In the second stage of the crisis, the Hungarian, Polish and Romanian foreign markets recovered market efficiency quickly, while Russia, Turkey and the Czech Republic continue to register a low degree of efficiency.     

Visual predictions of currency crises using self‐organizing maps

Throughout the 1990s, four global waves of financial turmoil occurred. The beginning of the 21st century has also suffered from several crisis episodes, including the global financial crisis of 2008–2009. However, to date, the forecasting results are still disappointing. This paper examines whether new insights can be gained from the application of the self‐organizing map (SOM) – a non‐parametric neural‐network‐based visualization tool. We develop a SOM‐based model for prediction of currency crises. We evaluate the predictive power of the model and compare it with that of a classical probit model. The results indicate that the SOM‐based model is a feasible tool for predicting currency crises. Moreover, its visual capabilities facilitate the understanding of the factors and conditions that contribute to the emergence of currency crises. Copyright © 2011 John Wiley & Sons, Ltd.     

NEURO‐GENETIC PREDICTIONS OF CURRENCY CRISES

We create a neuro‐genetic (NG) model for predicting currency crises by using a genetic algorithm for specifying (1) the combination of inputs, (2) the network configuration and (3) the training parameters for a back‐propagation artificial neural network (ANN). The performance of the NG model is evaluated by comparing it with standalone probit and ANN models in terms of utility for a policy decision‐maker. We show that the NG model provides better in‐sample and out‐of‐sample performance, as well as provides an automatic and more objective calibration of a predictive ANN model. We show that using a genetic algorithm for finding an optimal model specification for an ANN is not only less laborious for the analyst, but also more accurate in terms of classifying in‐sample and predicting out‐of‐sample crises. For a sufficiently parsimonious, but still nonlinear, model for generalized processing of out‐of‐sample data, the creation and evaluation of models is performed objectively using only in‐sample information as well as an early stopping procedure. Copyright © 2011 John Wiley & Sons, Ltd.