Bayesian inference for CoVaR

Recent financial disasters emphasised the need to investigate the consequence associated with the tail co-movements among institutions; episodes of contagion are frequently observed and increase the probability of large losses affecting market participants' risk capital. Commonly used risk management tools fail to account for potential spillover effects among institutions because they provide individual risk assessment. We contribute to analyse the interdependence effects of extreme events providing an estimation tool for evaluating the Conditional Value-at-Risk (CoVaR) defined as the Value-at-Risk of an institution conditioned on another institution being under distress. In particular, our approach relies on Bayesian quantile regression framework. We propose a Markov chain Monte Carlo algorithm exploiting the Asymmetric Laplace distribution and its representation as a location-scale mixture of Normals. Moreover, since risk measures are usually evaluated on time series data and returns typically change over time, we extend the CoVaR model to account for the dynamics of the tail behaviour. Application on U.S. companies belonging to different sectors of the Standard and Poor's Composite Index (S&P500) is considered to evaluate the marginal contribution to the overall systemic risk of each individual institutio

Measuring sovereign contagion in Europe

This paper analyzes sovereign risk shift-contagion, i.e. positive and significant changes in the propagation mechanisms, using bond yield spreads for the major eurozone countries. By emphasizing the use oftwo econometric approaches based on quantile regressions (standard quantile regression and Bayesianquantile regression with heteroskedasticity) we find that the propagation of shocks in euro\u2019s bond yieldspreads shows almost no presence of shift-contagion in the sample periods considered (2003\u20132006,Nov. 2008\u2013Nov. 2011, Dec. 2011\u2013Apr. 2013). Shock transmission is no different on days with big spreadchanges and small changes. This is the case even though a significant number of the countries in our sample have been extremely affected by their sovereign debt and fiscal situations. The risk spillover amongthese countries is not affected by the size or sign of the shock, implying that so far contagion has remainedsubdued. However, the US crisis does generate a change in the intensity of the propagation of shocks inthe eurozone between the 2003\u20132006 pre-crisis period and the Nov. 2008\u2013Nov. 2011 post-Lehman one,but the coefficients actually go down, not up! All the increases in correlation we have witnessed overthe last years come from larger shocks and the heteroskedasticity in the data, not from similar shockspropagated with higher intensity across Europe. These surprising, but robust, results emerge becausethis is the first paper, to our knowledge, in which a Bayesian quantile regression approach allowing forheteroskedasticity is used to measure contagion. This methodology is particularly well-suited to dealwith nonlinear and unstable transmission mechanisms especially when asymmetric responses to signand size are suspected

Financial Network Systemic Risk Contributions

We propose the systemic risk beta as a measure for financial companies’ contribution to systemic risk given network interdependence between firms’ tail risk exposures. Conditional on statistically pre-identified network spillover effects and market and balance sheet information, we define the systemic risk beta as the time-varying marginal effect of a firm’s Value-at-risk (VaR) on the system’s VaR. Suitable statistical inference reveals a multitude of relevant risk spillover channels and determines companies’ systemic importance in the U.S. financial system. Our approach can be used to monitor companies’ systemic importance allowing for a transparent macroprudential regulation.Systemic risk contribution, systemic risk network, Value at Risk, network topology, two-step quantile regression, time-varying parameters

Measuring and testing for the systemically important financial institutions

This paper analyzes the measure of systemic importance ΔCoVaR proposed by Adrian and Brunnermeier (2009, 2010) within the context of a similar class of risk measures used in the risk management literature. Inaddition, we develop a series of testing procedures, based on ΔCoVaR, toidentify and rank the systemically important institutions. We stress the importance of statistical testing in interpreting the measure of systemicx importance. An empirical application illustrates the testing procedures, using equity data for three European banks.

Sources of time varying return comovements during different economic regimes: evidence from the emerging Indian equity market

We study the economic and non-economic sources of stock return comovements of the emerging Indian equity market and the developed equity markets of the US, UK, Germany, France, Canada and Japan. Our findings show that the probability of extreme comovements in the economic contraction regime is relatively higher than in the economic expansion regime. We show that international interest rates, inflation uncertainty and dividend yields are the main drivers of the asymmetric return comovements. Findings reported in the paper imply that the impact of interest rates and inflation on return comovements could be used for anticipating financial contagion and/or spillover effects. This is particularly critical since during extreme market conditions, the tail return comovements can potentially reveal critical information for active portfolio management

Are benefits from oil - stocks diversification gone? New evidence from a dynamic copula and high frequency data

Oil is perceived as a good diversification tool for stock markets. To fully understand this potential, we propose a new empirical methodology that combines generalized autoregressive score copula functions with high frequency data and allows us to capture and forecast the conditional time-varying joint distribution of the oil -- stocks pair accurately. Our realized GARCH with time-varying copula yields statistically better forecasts of the dependence and quantiles of the distribution relative to competing models. Employing a recently proposed conditional diversification benefits measure that considers higher-order moments and nonlinear dependence from tail events, we document decreasing benefits from diversification over the past ten years. The diversification benefits implied by our empirical model are, moreover, strongly varied over time. These findings have important implications for asset allocation, as the benefits of including oil in stock portfolios may not be as large as perceived

Essays on Machine Learning for Risk Analysis in Finance, Insurance and Energy

[eng] This thesis provides research catalogued in the area of risk assessment. Specifically, it contributes to the fields of international finance and asset pricing in finance, and risk assessment in energy economics and transportation research. We present in this thesis a generalization of the spillover indexes to analyze interconnectedness at firm level, and define the aggregate influence from a sector and a country on a firm. We also discuss which factors are relevant for predicting conditional quantiles across the distribution of returns and present a method for selecting factors based on the investor interests. We study the performance of quantile regression against quantile time-series models. Finally, we present a regression framework which estimates VaR and CTE ensuring noncrossing conditions for various quantile levels, and discuss results on energy and telematics data. Within the financial contagion literature, we aim to provide a better understanding of international spillovers and a method for visualize which country and sector are its main drivers. We show that not all companies are driven by their own country or sector, which should be considered by investors and risk managers when assessing company risk and managing investments. In this paper we show that a large percentage of firms’ stocks are driven by their country. But contrary to the belief where country is the main driver of a company’s return movements, a part depends mainly on its sector. We note that 1) the financial services and energy companies are positioned at the center of the network, and 2) northern and western Europe are highly interconnected, while eastern and southern Europe present lower spillovers. 3) For the British energy firms British Petroleum (BP) and Royal Dutch Shell, we evidence greater spillovers from France than from Great Britain itself. 4) We identify which non-Russian firms are most influenced by Russia, simulating a risk management analysis in the event of of turmoil distresses such as the recent Ukrainian conflict. 5) We show the improvement on spillover information when using individual firm connectedness and aggregating spillovers afterwards against calculating spillovers directly from indexes. 6) We finally show that eastern Europe has increased interconnectedness with the rest of the continent after the Covid-19 pandemic. Regarding the asset pricing literature, we aim to understand the key elements that predict extreme quantile levels of a stock return. We study which factors for a 7-factor asset pricing specification are more relevant for each part of the distributions’ tail. The 7-factor specification is constituted by the factors size, book-to-market, operating profitability, investment, momentum, market beta and liquidity. We present a method to add more factors depending on the investors’ interests. We use quantile regression models for predicting quantile levels 0.05, 0.25, 0.5, 0.75 and 0.95 of the stock returns using cross-sectional characteristics as covariates from the Open Source Cross-Sectional Asset Pricing Dataset (Chen and Zimmermann, 2021). We observe that the factor size changes from positive to negative sign when predicting lower quantiles to higher quantiles. We show that extreme quantile level estimations perform better than the median in terms of pseudo-R2. Regarding factor significance, the variable investment has lower predictive power than other factors in terms of t-statistics for all tested quantile levels. Liquidity gains significance if quantile levels increase. For book-to-market, profitability, momentum and market beta, median predictions of returns are more significant than extreme quantile level estimations. The opposite happens for size, which presents higher relevance for predicting extreme quantile levels of the returns’ distribution. We observe that during crisis periods, some factors lose significance. This is the case of profitability and momentum for quantile levels 0.05 and 0.5, and size, book-to-market and market beta for quantile level 0.5. We add additional factors individually and compare the weighted average pseudo-R2 obtained across all 5 quantile levels. The weighting depends on the strategy that the investor follows. For all strategies tested, the most relevant factors to add to the 7-factor specification are momentum seasonality and net operating assets. Following, for strategies more interested in predicting losers’ tails (left part of the distribution), adding asset growth is recommended, but if the investor is interested in the winners’ tail (right part of the distribution), the recommended factor to add is enterprise multiple. Within the asset pricing literature, we encourage the use of cross-sectional information against time-series factors to predict extreme quantile levels of the right-hand side of the response distribution during periods of high volatility. By using this methodology, we do not restrict the information on panel-like datasets, which allows us to study more companies, and provide estimates for newly added firms. We use quantile regression specification with cross-sectional characteristics obtained from the Open Source Cross-Sectional Asset Pricing Dataset (Chen and Zimmermann, 2021) and compare results against a CAViaR (Engle and Manganelli, 2004) specification. Fama and French (2020) evidence that the average returns are better explained by using cross-sectional factors than by using time-series factors. We show that this only applies on extreme quantile levels during high volatility periods. We show that individual firm Hits (exceedances above VaR) calculated using time-series models tend to accumulate, while using cross-sectional data we avoid concentrations. We show that cross-sectional information improves the prediction of Value-at-Risk (VaR) and Conditional Tail Expectations (CTE). We finally discuss changes on capital requirements for a firm. In general, by using cross-sectional information, capital requirements should be increased from when time-series information is used. During turmoil periods the opposite happens: capital requirements should decrease compared to when using the CAViaR specification. Inside the area of non-crossing quantiles, we define the non-crossing property for VaR and CTE for several quantile levels. We define a regression framework based on neural networks that creates an environment for predicting VaR and CTE for several quantile levels while asserting non-crossing conditions. The proposed neural network predicts VaR and CTE as positive excesses of the previous VaR and CTE. We prove that this definition satisfies the non-crossing property and show its improvement against the Monotone Composite Quantile Regression Neural Network (Cannon, 2018) and a quantile regression and CTE linear approach on an energy consumption and telematic datasets. We show the estimation improvements on extreme quantile levels of the right part of the distribution against the other tested models by using Murphy diagrams (Ehm et al., 2016). We present examples with crossing predictions to demonstrate the infeasibility of such results in a business context, which we overcome using the proposed model

Risk Spillovers and Interconnectedness between Systemically Important Institutions

In this paper, we gauge the degree of interconnectedness and quantify the linkages between global and other systemically important institutions, and the global financial system. We document that the two groups and the financial system become more interconnected during the global financial crisis when linkages across groups grow. In contrast, during tranquil times linkages within groups prevail. Global systemically important banks (G-SIBs) contribute most to system-wide distress but are also most exposed. There are more links coming from G-SIBs to other systemically important institutions (O-SIIs) than the other way around, confirming the role of G-SIBs as major risk transmitters in the financial system. The two groups and the global financial system tend to co-vary for periods up to 60 days Prior to their official designation as G-SIBs or O-SIIs, the prevalent news sentiment about these institutions (we measure with a textual analysis) was negative. Importantly, the systemic importance and exposure of G-SIBs and O-SIIs is perceived differently by the Financial Stability Board (FSB) and the European Banking Authority (EBA)

Essays on Systemic Risk in European Banking

This thesis makes a contribution to systemic risk literature in the European banking system. The intimate interdependence between the European banking industries and the fragile GIIPS debt market has jeopardized the banking sector in Europe. The threats of unfavourable financial conditions in European bank- ing sufficiently highlight the importance of the dissertation’s distinct focus on systemic risk measurement and on the risk drivers. The outcomes of the three included papers give support to the European authorities to enact comprehen- sive macroprudential regulation schemes.The first paper estimates the systemic risk contributions of GIIPS-block bank- ing on 14 major banking systems in Europe. The CoVaR measure further eval- uates the magnitude of risk using two methods; quantile regression and DCC. Our results indicate a substantial spillover effect of GIIPS banking on the exam- ined banking systems. In other words, the countries’ banking sectors are in part driven by systemic risk in the GIIPS banking system. We also find supporting ev- idence of amplified spillover effects from the GIIPS-block banking sector during the financial crises.The second paper firstly quantifies the sovereign debt spillovers based on daily returns of GIIPS and individual banks’ CDSs over the period of 2007-2015. Then, it examines banks’ financial features and financial markets’ circumstances that determine variations in the banks’ sovereign risk exposures. We find those banks that hold higher assets in times of crisis or work in markets with unfa- vorable profiles, i.e. low returns and high idiosyncratic risks tend to be further susceptible to sovereign risk. However, we do not observe that variations in the risk exposures have been driven by dissimilarities in individual fundamentals such as leverage, debt-to-cash, and market-to-book value of equity ratios.The third paper analyzes the main determinants of systemic contagion from an individual country’s banking sector to the whole banking industry of Europe in 1999-2013. The results show that differences in systemic risk contribution are driven by a combination of balance-sheet characteristics and macroeconomic conditions such as the country-level VaR, crisis episodes, size or total asset, bi- lateral loan, market-to-book ratio, stock market returns, and industry produc- tion index (IPI).Keywords: Systemic Risk, CoVaR, GIIPS, Quantile Regression, DCC, CDS JEL Classification: G01, G21, E43, N24, H63, F30