Financial Globalization and Banking Crises in Emerging Markets

Bank crises in emerging economies have been a feature of the recent global crisis, and their incidence has increased in the post-Bretton Woods era. This paper investigates the impact of financial globalization on the incidence of systemic bank crises in 20 emerging markets over the years 1976–2002 using measures of de facto and de jure financial openness. An increase in foreign debt liabilities contributes to an increase in the incidence of crises, but foreign direct investment and portfolio equity liabilities have the opposite effect. A more liberal de jure capital regime lowers the incidence of banking crises, while a regime of fixed exchange rates increases their frequency. The results of the econometric analysis is consistent with the experience of East European and central Asian emerging markets, which attracted a relatively large proportion of capital flows in the form of debt in recent years and have been particularly hard hit by the global financial crisis.     

Inflation and Welfare in Latin America

This paper follows Bailey (J Polit Econ 64:93–110, 1956) and Lucas (Econometrica 68:247–274, 2000) and estimates the welfare cost of inflation for 17 Latin American economies. We use annual data, from 1955 to 2000, and recent advances in the field of applied econometrics to estimate the inflation rate elasticity of money demand and report significantly high and differential welfare cost estimates for these economies.     

“Speculative Influence Network” during financial bubbles: application to Chinese stock markets

We introduce the Speculative Influence Network (SIN) to decipher the causal relationships between sectors (and/or firms) during financial bubbles. The SIN is constructed in two steps. First, we develop a Hidden Markov Model (HMM) of regime-switching between a normal market phase represented by a geometric Brownian motion and a bubble regime represented by the stochastic super-exponential Sornette and Andersen (Int J Mod Phys C 13(2):171–188, 2002) bubble model. The calibration of the HMM provides the probability at each time for a given security to be in the bubble regime. Conditional on two assets being qualified in the bubble regime, we then use the transfer entropy to quantify the influence of the returns of one asset i onto another asset j, from which we introduce the adjacency matrix of the SIN among securities. We apply our technology to the Chinese stock market during the period 2005–2008, during which a normal phase was followed by a spectacular bubble ending in a massive correction. We introduce the Net Speculative Influence Intensity variable as the difference between the transfer entropies from i to j and from j to i, which is used in a series of rank ordered regressions to predict the maximum loss (%MaxLoss) endured during the crash. The sectors that influenced other sectors the most are found to have the largest losses. There is some predictability obtained by using the transfer entropy involving industrial sectors to explain the %MaxLoss of financial institutions but not vice versa. We also show that the bubble state variable calibrated on the Chinese market data corresponds well to the regimes when the market exhibits a strong price acceleration followed by clear change of price regimes. Our results suggest that SIN may contribute significant skill to the development of general linkage-based systemic risks measures and early warning metrics.     

Defect Detection Using Unanimous Vote Among Mahalanobis Classifiers for Each Color Component

In manufacturing industries, product inspection is automated and the use of image data is increasingly being employed for defect detection. A manufacturing company in Japan produces an item and inspects the produced products using image data. Reducing the error rate is important in product inspection because poor inspection of products might lead to the delivery of defective products to consumers (consumer’s risk) and strict inspection increases production cost (producer’s risk). To reduce the error rate, we highlighted fault points using a two-dimensional moving range filter and discriminated defect production through a unanimous vote among Mahalanobis classifiers for each color component. For results, we achieved a lower error rate than the current system. This research is an empirical study of how to use image data in defect detection.     

Broadband Demand Analysis for Network Building

An increase of broadband demand is forecasted by transitional methods that consider the effect of this increase through many factors, such as customer requirement diversification, and new service introduction and deployment under competition. Broadband demand forecasting has become important for closing the digital divide, promoting regional developments, and constructing networks economically; therefore, a demand forecast model that considers the mechanisms of market structure is necessary. In this paper, a demand analysis method for broadband access combining macro- and micro-data mining is proposed, and the service choice behaviour of customers is introduced as a customer model not only to express the macro trend of market structure, but also to consider area marketing. The proposed method can estimate the potential demand, determine the point at which broadband demand growth peaks in a specified area, and support a decision for ultra high-speed broadband access facility installation.