Systemic risk determinants in the European banking industry during financial crises, 2006-2012

The recent financial turmoil has stimulated a rich debate in banking and financial literature on the identification of systemic risk determinants and devices to forecast and prevent crises. This paper explores the contribution of corporate variables to systemic risk using the CoVaR approach (Adrian and Brunnermeier, 2016). Using balanced panel data on 141 European banks from 24 countries, which were listed from 2006Q1 to 2012Q4, we investigated the impact of corporate variables during the three regimes that characterised the European banking sector-the subprime crisis (2007Q3-2008Q3), the European Great Financial Depression (2008Q4-2010Q2), and the sovereign debt crisis (2010Q3-2012Q4). Our results show that size did not play a significant role in spreading systemic risk, while maturity mismatch did. However, the nature and intensity of these two determinants varied across the three regimes

Micro versus macro cointegration in heterogeneous panels

We consider the issue of cross-sectional aggregation in nonstationary and heterogeneous panels where each unit cointegrates. We derive asymptotic properties of the aggregate estimate, and necessary and sufficient conditions for cointegration to hold in the aggregate relationship. We then analyze the case when cointegration does not carry through the aggregation process, and we investigate whether the violation of the formal conditions for perfect aggregation can still lead to an aggregate equation that is observationally equivalent to a cointegrated relationship. We derive a measure of the degree of noncointegration of the aggregate relationship and we explore its asymptotic properties. We propose a valid bootstrap approximation of the test. A Monte Carlo exercise evaluates size and power properties of the bootstrap test

Identification robust inference in cointegrating regressions

In cointegrating regressions, estimators and test statistics are nuisance parameter dependent. This paper addresses this problem from an identification-robust perspective. Confidence sets for the long-run coefficient (denoted β) are proposed that invert LR-tests against an unrestricted or a cointegration-restricted alternative. For empirically relevant special cases, we provide analytical solutions to the inversion problem. A simulation study, imposing and relaxing strong exogeneity, analyzes our methods relative to standard Maximum Likelihood, Fully Modified and Dynamic OLS, and a stationarity-test based counterpart. In contrast with all the above, proposed methods have good size regardless of the identification status, and good power when β is identified

True Versus Spurious Long Memory: Some Theoretical Results and a Monte Carlo Comparison

A common feature of financial time series is their strong persistence. Yet, long memory may just be the spurious effect of either structural breaks or slow switching regimes. We explore the effects of spurious long memory on the elasticity of the stock market price with respect to volatility and show how cross-sectional aggregation may generate spurious persistence in the data. We undertake an extensive Monte Carlo study to compare the performance of five tests, constructed under the null of true long memory versus the alternative of spurious long memory due to level shifts or breaks

Trading Price Jump Clusters in Foreign Exchange Markets

We investigate trading opportunities of price jump clusters in the FX markets. We identify clusters for eight FX rates against the U.S. dollar from March 1, 2013 to June 6, 2013 sampled at a 5-minute frequency. We propose a high-frequency jump cluster-based trading strategy and show that jumps carry a tradable signal for all currencies; however, when incorporating the bid-ask spread, the only profitable currencies are the euro, yen and rand. From the portfolio perspective, a combination of the euro and yen represents a strategy robust to the holding period, minimizes the transaction costs, and diversifies out the U.S.-related risk

High- and Low-Frequency Correlations in European Government Bond Spreads and Their Macroeconomic Drivers

We propose to adopt high-frequency DCC-MIDAS models to estimate high- and low-frequency correlations in the 10-year government bond spreads for Belgium, France, Italy, the Netherlands, and Spain relative to Germany, from June 1, 2007 to May 31, 2012. The high-frequency component, reflecting financial market conditions, is evaluated at 15-minute frequency, while the low-frequency component, fixed through a month, depends on country-specific macroeconomic conditions. We find strong links between spreads volatility and worsening macroeconomic fundamentals; in presence of similar macroeconomic fundamentals relative spreads move together; the increasing correlation in spreads during the burst of the sovereign debt crisis cannot be entirely ascribed to macroeconomic factors but rather to changes in market liquidity

MAXIMUM NON-EXTENSIVE ENTROPY BLOCK BOOTSTRAP FOR NON-STATIONARY PROCESSES

In this paper, we propose a novel entropy-based resampling scheme valid for non-stationary data. In particular, we identify the reason for the failure of the original entropy-based algorithm of Vinod and López-de Lacalle (2009) to be the perfect rank correlation between the actual and bootstrapped time series. We propose the Maximum Entropy Block Bootstrap which preserves the rank correlation locally. Further, we also introduce the Maximum non-extensive Entropy Block Bootstrap to allow for fat tail behaviour in time series. Finally, we show the optimal finite sample properties of the proposed methods via a Monte Carlo analysis where we bootstrap the distribution of the Dickey-Fuller test

Trading strategies with implied forward credit default swap spreads

Credit default risk for an obligor can be hedged with either a credit de fault swap (CDS) or a constant maturity credit default swap (CMCDS). We find strong evidence of persistent differences in the hedging cost associated with the two comparable contracts. Between 2001 and 2006, it would have been more profitable to sell CDS and buy CMCDS while after the crisis between 2008 and 2013 the opposite strategy was profitable. Panel data tests indicate that for our sample period the implied forward CDS rates are unbiased estimates of future spot CDS rates. The changes in the company implied volatility is the main determinant of trading inefficiencies, followed by the changes in GDP and in the interest rates before the crisis, and the changes in sentiment index and in the VIX after the crisis

Evaluating the Accuracy of Value-at-Risk Forecasts: New Multilevel Tests

We propose independence and conditional coverage tests which are aimed at evaluating the accuracy of Value-at-Risk (VaR) forecasts from the same model at different confidence levels. The proposed procedures are multilevel tests, i.e., joint tests of several quantiles corresponding to different confidence levels. In a comprehensive Monte Carlo exercise, we document the superiority of the proposed tests with respect to existing multilevel tests. In an empirical application, we illustrate the implementation of the tests using several VaR models and daily data for 15 MSCI world indices

Common stochastic trends and aggregation in heterogeneous panels

In nonstationary heterogeneous panels where the number of units is finite and where each unit cointegrates, a large number of conditions needs to be satisfied for cointegration to be preserved in the aggregate relationship. In reality, the conditions most likely will not hold. This paper takes a closer look at what happens when the conditions are violated. In this case, the question of whether an aggregate relationship is observationally equivalent to a cointegrating equation is of particular interest. We derive a measure of the degree of noncointegration of the aggregate estimates, and we explore its asymptotic properties