Testing For Financial Contagion Between Developed And Emerging Markets During The 1997 East Asian Crisis

In this paper we examine whether during the 1997 East Asian crisis there was any contagion from the four largest economies in the region (Thailand, Indonesia, Korea and Malaysia) to a number of developed countries (Japan, UK, Germany and France). Following Forbes and Rigobon (2002), we test for contagion as a significant positive shift in the correlation between asset returns, taking into account heteroscedasticity and endogeneity bias. Furthermore, we improve on earlier empirical studies by carrying out a full sample test of the stability of the system that relies on more plausible (over)identifying restrictions. The estimation results provide some evidence of contagion, in particular from Japan (the major international lender in the region), which drastically cut its credit lines to the other Asian countries in 1997

The impact of bank concentration on financial distress: them case of the European banking system

This paper examines the impact of bank concentration on bank financial distress using a balanced panel of commercial banks belonging to EU 25 over the sample period running from 2003 to 2007. Financial distress is proxied by the observations falling below a given threshold of the empirical distribution of a risk adjusted indicator of bank performance: the Shareholder Value ratio. We employ a panel probit regression estimated by GMM in order to obtain consistent and efficient estimates following the suggestion of Bertschek and Lechner (1998). Our findings suggest, after controlling for a number of enviroment variables, a positive effect of bank concentration on financial distress

Depth dependence of westward-propagating North Atlantic features diagnosed from altimetry and a numerical 1/6° model

International audienceA 1/6° numerical simulation is used to investigate the vertical structure of westward propagation between 1993 and 2000 in the North Atlantic ocean. The realism of the simulated westward propagating signals, interpreted principally as the signature of first-mode baroclinic Rossby waves (RW), is first assessed by comparing the simulated amplitude and zonal phase speeds of Sea Level Anomalies (SLA) against TOPEX/Poseidon-ERS satellite altimeter data. Then, the (unobserved) subsurface signature of RW phase speeds is investigated from model outputs by means of the Radon Transform which was specifically adapted to focus on first-mode baroclinic RW. The analysis is performed on observed and simulated SLA and along 9 simulated isopycnal displacements spanning the 0-3250 m depth range. Simulated RW phase speeds agree well with their observed counterparts at the surface, although with a slight slow bias. Below the surface, the simulated phase speeds exhibit a systematic deceleration with increasing depth, by a factor that appears to vary geographically. Thus, while the reduction factor is about 15-18% on average at 3250 m over the region considered, it appears to be much weaker (about 5-8%) in the eddy-active Azores Current, where westward propagating structures might be more coherent in the vertical. In the context of linear theories, these results question the often-made normal mode assumption of many WKB-based theories that the phase speed is independent of depth. Alternatively, these results could also suggest that the vertical structure of westward propagating signals may significantly depend on their degree of nonlinearity, with the degree of vertical coherence possibly increasing with the degree of nonlinearity

Climate risk and investment in equities in Europe: a Panel SVAR approach

In this study, we use data on European stocks to construct a green-minus-brown portfolio hedging climate risk and to evaluate its performance in terms of cumulative expected and unexpected returns. More specifically, we estimate a Structural Panel VAR fitted to one month return and realized volatility computed for 40 constituents of a green portfolio (i.e., the low carbon emission portfolio monitored by Refinitiv) and for 41 constituents of a brown portfolio (underlying the Oil&Gas and Utilities industry sectors of the STOXX Europe 600). The common shocks underlying the cross-sectional averages, interpreted as portfolio shocks, are retrieved in a first stage of the analysis and they are used to control for cross-sectional dependence. We compute the historical decomposition (for cumulative returns) in a second stage of the analysis and we find, in line with P´astor, L., Stambaugh, R. F., & Taylor, L. A. (2022). Dissecting green returns. Journal of Financial Economics, 146 (2), 403–424, an out-performance of the expected component of the brown portfolio relative to the one for the green portfolio, and an out-performance of the green portfolio when we turn our focus on the unexpected component. We also extend the analysis of P´astor et al. (2022), assessing, for the top 5 constituents of the green portfolio (e.g., those which are found to have the worst performance in terms of expected return), the role played by idiosyncratic shocks in shaping their out-performance in terms of unexpected component. Finally, after exploiting the non-gaussian time series properties of the financial time series considered for the purpose of statistical identification, we are able to interpret ex post the idiosyncratic shocks in terms of financial leverage and risk aversion

Housing Market Shocks in Italy: a GVAR Approach

In this paper, we use a Global Vector Autoregression (GVAR) model to assess the spatio-temporal mechanism of house price spillovers, also known as “ripple effect”, among 93 Italian provincial housing markets, over the period 2004 − 2016. In order to better capture the local housing market dynamics, we use data not only on house prices but also on transaction volumes. In particular, we focus on estimating, to what extent, exogenous shocks, interpreted as negative housing demand shocks, arising from 10 Italian regional capitals, impact on their house prices and sales and how these shocks spill over to neighbours housing markets. The negative housing market demand shock hitting the GVAR model is identified by using theory-driven sign restrictions. The spatio-temporal analysis carried through impulse response functions shows that there is evidence of a “ripple effect” mainly occurring through transaction volumes

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 affecting 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 performanc

Leading indicator properties of US high-yield credit spread

In this paper we examine the out-of-sample forecast performance of high-yield credit 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 sector-specific high-yield credit spreads. This can be justified by observing that there is a gain from using a principal components model fitted to high-yield credit spreads compared to the prediction produced by benchmarks, such as an AR, and ARDL models that use either the term spread or the aggregate high-yield spread as exogenous regressor

Macro-uncertainty and financial stress spillovers in the Eurozone

This paper studies macro-uncertainty and financial distress spillovers within the Eurozone. We propose a novel methodology to derive the indices of spillovers, by using a Global Vector autoregressive model fitted to data sampled at mixed-frequencies. We find that macro-uncertainty and financial stress are relatively disconnected in the Eurozone. We also show that connectedness between core and periphery Eurozone countries mainly operates through financial stress and it decreases since the outbreak of the Eurozone sovereign debt crisis (with an increasing role played by peripheral countries). As a result, investors and policymakers should monitor separately macro-uncertainty and financial stress. Finally, we find that the mixed-frequency data should be taken into account in this context, otherwise, the spillovers can be underestimated

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

Temperature and Growth: a Panel Mixed Frequency VAR Analysis using NUTS2 data

In this study, we contribute to the existing literature on the impact of temperature on growth by examining the orthogonalized seasonal effect jointly with the feedback from economic activity (hence treating the increase in global temperature as anthropogenic) on a sample of 225 EU NUTS2 regions. For this purpose, we use a Panel Mixed- Frequency VAR. The empirical findings show, first, a worsening impact of temperature on growth over the last sub-sample (2000-2019) relative to the full sample analysis (covering the 1981-2019 time span). Moreover, our findings show that seasonal temperature effects are not restricted only to the agriculture sector, and we also find evidence of a heterogeneous impact of seasonal temperature on growth when we turn our focus on hot and cold regions (using the average EU median annual temperature as a threshold), rich and poor regions (using the average EU median income per capita as a threshold) and between competitiveness (using the median Regional Competitiveness index as a threshold)