1,721,064 research outputs found
Testing for contagion: a time-scale decomposition
Get PDFThe aim of the paper is to test for financial contagion by estimating a simultaneous equation model
subject to structural breaks. For this purpose, we use the Maximum Overlapping Discrete Wavelet
Transform, MODWT, to decompose four asset returns into different scale components (each
associated with a given frequency range). The decomposition will enable us to obtain the moment
conditions necessary to (over)identify a structural form model with a single dummy and the one
with multiple dummies capturing shifts in the co-movement of asset returns occurring during
periods of financial turmoil. A Montecarlo simulation exercise shows that test based on a single
dummy structural form model has good size and power properties in detecting financial contagion.
The empirical results for four East Asian stock markets show that, once we account for
interdependence through an (unobservable) common factor, there is no evidence of contagion but
only (limited) empirical support of hypersensitivity and extra-vulnerability during the 1997-1998
financial turbulence
Wavelet analysis of financial contagion
No full textIn this paper we estimate a simultaneous equation model fitted to financial returns in order to
disentangle the role played by common shock and idiosyncratic shocks in shaping the co-movement
between asset returns during periods of calm and financial turbulence. For this purpose we use
wavelet analysis, and, in particular, the Maximum Overlapping Discrete Wavelet Transform, to
decompose the covariance matrix of the asset returns on a scale by scale basis, where each scale is
associated to a given frequency range. This decomposition will give enough moment conditions to
identify the role played by common and idiosyncratic shocks. A Montecarlo simulation experiment
shows that our testing methodology has good size and power properties to test for the null of no
contagion. Finally, using Full Information Maximum Likelihood, we fit our model to test for the
presence of contagion within the East Asian region stock markets during the 1997-1998 period of
financial turbulence
The impact of bank concentration on financial distress: them case of the European banking system
Get PDFThis 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
Measuring bank capital requirements through dynamic factor analysis
Get PDFIn this paper, using industry sector stock returns as proxies of firm asset values, we obtain bank capital requirements (through the cycle). This is achieved by Montecarlo simulation of a bank loan portfolio loss density. We depart from the Basel 2 analytical formula developed by Gordy (2003) for the computation of the economic capital by, first, allowing dynamic heterogeneity in the factor loadings, and, also, by accounting for stochastic dependent recoveries. Dynamic heterogeneity in the factor loadings is introduced by using dynamic forecast of a Dynamic Factor model fitted to a large dataset of macroeconomic credit drivers. The empirical findings show that there is a decrease in the degree of Portfolio Credit Risk, once we move from the Basel 2 analytic formula to the Dynamic Factor model specification
Climate risk and investment in equities in Europe: a Panel SVAR approach
Get PDFIn 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
Temperature and Growth: a Panel Mixed Frequency VAR Analysis using NUTS2 data
Get PDFIn 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)
Leading indicator properties of US high-yield credit spread
Get PDFIn 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
Financial distress and real economic activity in Lithuania: a Granger causality test based on mixed-frequency VAR
No full textIn this paper, we extend the monthly financial stress index for Lithuania, computed by the European Central Bank, to a daily frequency and we also include banking sector stress among its constituents, beyond bond, equity and foreign exchange markets. We investigate the causal relationship between the daily financial stress index and monthly industrial production growth, using a Granger causality test applied to a mixed-frequency VAR. Our results suggest evidence of Granger causality from financial stress to industrial production growth once the index is enriched by daily observations from the financial markets. Our findings, based on impulse response analysis, confirm the negative effect of financial stress on real economy found in the empirical literature through common frequency analysis. Finally, the comparison between common and mixed frequency analysis suggests that ignoring the information content of daily data would lead to a mild temporal aggregation bias that could affect the evaluation of financial stress shocks on industrial production
Macro-uncertainty and financial stress spillovers in the Eurozone
Get PDFThis 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
Housing market shocks in italy: A GVAR approach
No full textIn 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
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