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This paper studies the impact of the banks’ portfolio holdings of financial derivatives on the banks’ individual contribution to systemic risk over and above the effect of variables related to size, interconnectedness, substitutability, and other balance sheet information. Using a sample of 95 U.S. bank holding companies from 2002 to 2011, we compare five measures of the banks’ contribution to systemic risk and find that the new measure proposed in this study, Net Shapley Value, outperforms the others. Using this measure we find that banks’ aggregate holdings of five classes of derivatives do not exhibit a significant effect on the bank’s contribution to systemic risk. On the contrary, the banks’ holdings of certain specific types of derivatives such as foreign exchange and credit derivatives increase the banks contributions to systemic risk whereas holdings of interest rate derivatives decrease it. Nevertheless, the proportion of non-performing loans over total loans and the leverage ratio have much stronger impact on systemic risk than derivatives holdings. Therefore, the derivatives’ impact plays a second fiddle in comparison with traditional banking activities related to the former two items.

This paper compares the in-sample and out-of-sample performance of several models for computing the tail risk of one-month and one-year electricity futures contracts traded in the NordPool, French, German, and Spanish markets in 2008–2017. As measures of tail risk, we use the one-day-ahead Value-at-Risk (VaR) and the Expected Shortfall (ES). With VaR, the AR (1)-GARCH (1,1) model with Student-t distribution is the best-performing specification with 88% cases in which the Fisher test accepts the model, with a success rate of 94% in the left tail and of 81% in the right tail. The model passes the test of model adequacy in the 100% of the cases in the NordPool and German markets, but only in the 88% and 63% of the cases in the Spanish and French markets. With ES, this model passes the test of model adequacy in 100% of cases in all markets. Historical Simulation and Quantile Regression-based approaches misestimate tail risks. The right-hand tail of the returns is more difficult to model than the left-hand tail and therefore financial regulators and the administrators of futures markets should take these results into account when setting additional regulatory capital requirements and margin account regulations to short positions. We acknowledge financial support from FUNCAS, through grant PRELEC2020–2017/00085/00, from DGICYT, through grant ECO2016–77807-P, and from CAM, through grant EARLYFIN-CM, #S2015/HUM-3353

We examine whether the source of debt financing is important for assessments of firms' default risk. This study reveals that during the 2007–2010 financial crisis, firms that depend mainly on financing from banks suffer higher increases in default risk than do firms with no such dependence. Conversely, firms that rely solely on financing from public debt markets do not experience significant increases in default risk. These findings suggest that the bank supply shock theory explains the transmission of financial shocks to the real economy. Finally, firms that depend on bank financing cannot offset the adverse impacts of bank lending shocks by substituting bank loans with publicly traded debt.

We study the effect of rollover risk on the risk of default using a comprehensive database of U.S. industrial firms during 1986–2013. Dependence on bank financing is the key driver of the impact of rollover risk on default risk. Default risk and rollover risk present a significant positive relation in firms dependent on bank financing. In contrast, rollover risk is uncorrelated with default probability in the case of firms that do not rely on bank financing. Our measure of rollover risk is the amount of long-term debt maturing in one year, weighted by total assets. In the case of a firm that depends on bank financing, an increase of one standard deviation in this measure leads to a significant increase of 3.2% in its default probability within one year. Other drivers affecting the interaction between rollover risk and default risk are whether a firm suffers from declining profitability and has poor credit. Additionally, rollover risk's impact on default probability is stronger during periods when credit market conditions are tighter.

This paper investigates tail risk spillovers from the financial sector to real economy firms in the United States and the United Kingdom from 2003 to 2011. We measure these spillovers by evaluating the number of joint occurrences of extreme negative returns in a nonfinancial firm conditional on an extreme negative return in the financial sector. Such spillovers increased dramatically in the 2007–2009 crisis. We also examine whether cash holdings act as a buffer in mitigating these spillovers. Our results indicate that greater cash holdings are associated with lower levels of tail risk spillovers for financially constrained firms and more strongly so among constrained firms with high hedging needs and no ratings (i.e., firms with restricted external financing sources). Overall, our findings support the view that the greater cash holdings of constrained firms act as a shock absorber against costly external financing.

We present a new model for pricing electricity swaps. Two general factors affect contracts but unique risk elements affect each contract. General factors are average swap prices and deterministic trend-seasonal components, and unique elements are forward premiums. Innovations follow MNIG distributions. We estimate the model with data from the European Energy Exchange. The model outperforms four competitors, both in in-sample valuation and in out-of-sample forecasting, and in fitting the term structure of volatilities by market segments. Competitor models are (i) diffusion spot prices, (ii) jump-diffusion spot prices with time dependent volatility, (iii) HJM-based and (iv) Levy multifactor model with NIG distributions. Value-at-Risk measures based on normality strongly underestimate tail risk but our model gives estimates that are more exact. Juan Ignacio Peña and Rosa Rodriguez acknowledge financial support from the Ministry of Economics and Competitiveness, respectively, through grants ECO2012-35023, ECO2016-77807-P, and ECO2012-36559

We build a Real Options model to assess the importance of private provision and the impact of expropriation risk on investment timing, business values, governmental costs and social welfare. We consider two types of businesses (essential and non-essential) and two stages (operating businesses and investment opportunities) and answer questions regarding three main topics: the firm's reaction to expropriation risk, the government drivers to expropriate, and the welfare costs of expropriation. Our results show that responding to expropriation risk the private investor is driven to suboptimal investment decisions. When we endogenize the reputational costs of expropriation, our results show that the decision of the government to expropriate largely depends on the type of business being targeted. In terms of welfare, our results show that expropriation is always associated with a loss.

This article proposes a new measure of tail risk spillover: the conditional coexceedance (CCX), defined as the number of joint occurrences of extreme negative returns in an industry, conditional on an extreme negative return in the financial sector. The empirical application provides evidence of significant volatility and tail risk spillovers from the financial sector to many real sectors in the U.S. economy from 2001 to 2011. These spillovers increase in crisis periods. The CCX in a given sector is positively related to its amount of debt financing and negatively related to its valuation and investment. Therefore, real economy sectors—which require relatively high debt financing and whose value and investment activity are relatively lower—are prime candidates for stock price volatility and depreciation in the wake of a financial sector crisis. Evidence also suggests that the higher the industry’s degree of competition, the stronger the tail risk spillover from the financial sector.

This study presents robust empirical evidence suggesting the existence of significant liquidity commonalities in the corporate Credit Default Swap (CDS) market. Using daily data for 438 firms from 25 countries in the period 2005-2012 we find that these commonalities vary over time, being stronger in periods in which the global, counterparty, and funding liquidity risks increase. However, commonalities do not depend on firm's characteristics. The level of the liquidity commonalities differs across economic areas being on average stronger in the European Monetary Union. The effect of market liquidity is stronger than the effect of industry specific liquidity in most industries excluding the banking sector. We document the existence of asymmetries in commonalities around financial distress episodes such that the effect of market liquidity is stronger when the CDS market price increases. The results are not driven by the CDS data imputation method or by the liquidity of firms with high credit risk and are robust to alternative liquidity measures.

This paper studies statistical arbitrage opportunities in credit derivatives markets using strategies combining Credit Default Swaps (CDSs) and Asset Swap Packages (ASPs) by means of an improved statistical arbitrage test. Using four different databases (GFI, Reuters, CMA, and J.P. Morgan) from 2005 to 2009, we find persistent mispricings between the CDS and ASP spreads of individual firms, which should be priced similarly, before and during the 2007–2009 financial crisis. These mispricings are more frequent in low credit quality bonds and appear to offer arbitrage opportunities. We also aggregate the firms' CDS and ASP in a portfolio and still find persistent deviations, mainly in the lower rated bonds. In aggregate terms the deviations from the parity relation can be explained from systematic factors such as financing costs, counterparty risk, and global risk. However, after considering realistic estimations of funding and trading costs, all these mispricings are unlikely to provide profitable arbitrage opportunities. Sergio Mayordomo acknowledges financial support from PIUNA (University of Navarra) and the Spanish Ministry of Economy and Competitiveness (grant ECO2012-32554). Juan Ignacio Peña acknowledges financial support from MCI grant ECO2012-35023.

We analyse the extent to which prices in the sovereign credit default swap (CDS) and bond markets reflect the same information on credit risk in the context of the European Monetary Union. The empirical analysis is based on the theoretical equivalence relation that should hold between the CDS and bond spreads in a frictionless environment. We first test and find evidence in favour of the existence of persistent deviations between both spreads during the crisis but not before. Such deviations are found to be related to some market frictions, like counterparty risk, market illiquidity, and funding costs. We also find evidence suggesting that the price-discovery process is state-dependent. Specifically, the levels of counterparty and global risk, funding costs, market liquidity, volume of debt purchases by the European Central Bank in the secondary market, and the banks’ willingness to accept losses on their holdings of Greek bonds are found to be significant factors in determining which market leads price discovery.

This paper estimates and compares two groups of high-frequency market-based systemic risk measures from 2004 to 2009 using European and US data of interbank rates, stock prices and credit derivatives both at aggregate market level as well as the individual bank level. The former group of measures gauges the overall tension in the financial sector whereas second group relies on individual institution information to extract joint distress at portfolio level. We rank the measures using three criteria: i) Granger Causality tests, ii) Gonzalo and Granger metric, and iii) the correlation with an index of systemic events and policy actions. We find that the best systemic risk indicator based on aggregate market measures is the First Principal Component of a portfolio of Credit Default Swap (CDS) spreads whereas the best indicator based on individual institution’s measures is the Multivariate Densities computed from CDS spreads. These results suggest that the measures based on CDSs outperform measures based on interbank rates or stock market prices. Some implications for regulators and policymakers are discussed.

We hypothesize and find empirical evidence that two structural constraints of the industry are informative in the corporate failure prediction, industry concentration and dependence on customers and suppliers. Using an extensive database on corporate failures and bankruptcies in the U.S. market from 1998 to 2009 we find that the probabilities of failure and bankruptcy are significantly higher for firms in highly concentrated industries. The probability of bankruptcy is higher for firms in industries with stronger customer dependency, but this factor does not affect failure probabilities. Also in the case of failures the model's fit is noticeably higher than in the case of bankruptcies.

Many firms choose to refinance their debt. We investigate the long run effects of this extended practice on credit ratings and credit spreads. We find that debt refinancing generates systematic rating downgrades unless a minimum firm value growth is observed. Deviations from this growth path imply asymmetric results. A lower firm value growth generates downgrades and a higher firm value growth generates upgrades, as expected. However, downgrades tend to be higher in absolute terms. We also find that the inverse relation between credit spreads and risk free rate that structural models usually predict still holds in this setting, but only in the short run. This negative relation will turn to be null in the medium run and positive in the long run.

The objective of this article is twofold. The first is to incorporate mental accounting, loss-aversion, asymmetric risk-taking behaviour and probability weighting in a multi-period portfolio optimization for individual investors. While these behavioural biases have previously been identified in the literature, their overall impact during the determination of optimal asset allocation in a multi-period analysis is still missing. The second objective is to account for the estimation risk in the analysis. Considering 26 daily index stock data over the period from 1995 to 2007, we empirically evaluate our model (Behaviour Resample Adjusted Technique–BRATE) against the traditional Markowitz model.

This paper explores the dynamic relationship between stock market implied credit spreads, CDS spreads, and bond spreads. A general VECM representation is proposed for changes in the three credit spread measures which accounts for zero, one, or two independent cointegration equations, depending on the evidence provided by any particular company. Empirical analysis on price discovery, based on a proprietary sample of North American and European firms, and tailored to the specific VECM at hand, indicates that stocks lead CDS and bonds more frequently than the other way round. It likewise confirms the leading role of CDS with respect to bonds. Publicado

This article analyzes the tail behavior of energy price risk using a multivariate approach, in which the exposure to energy markets is given by a portfolio of oil, gas, coal, and electricity. To accommodate various dependence and tail decay patterns, this study models energy returns using different generalized hyperbolic conditional distributions and time-varying conditional mean and covariance. Employing daily energy futures data from August 2005 to March 2012, the authors recursively estimate the models and evaluate tail risk measures for the portfolio's profit-and-loss distribution for long and short positions at various horizons and confidence levels. Both in-sample and out-of-sample analyses applied to different energy portfolios show the importance of heavy tails and positive asymmetry in the distribution of energy risk factors. Thus, tail risk measures for energy portfolios based on standard methods (e.g. normality, constant covariance matrix) and on models with exponential tail decay underestimate actual tail risk, especially for short positions and short time horizons.

The effects of Spanish Stock Exchange Reform on the seasonal patterns of daily stock excess returns are addressed. Before the Reform, positive abnormal average Monday excess returns are found. Possible causes are discussed and related with clearing and trading mechanisms. After the Reform daily seasonal effects disappear, suggesting an increase in the market's operational efficiency

The aim of this article is 2-fold: first to test the adequacy of Pareto distributions to describe the tail of financial returns in emerging and developed markets, and second to study the possible correlation between stock market indices observed returns and return's extreme distributional characteristics measured by Value at Risk and Expected Shortfall. We test the empirical model using daily data from 41 countries, in the period from 1995 to 2005. The findings support the adequacy of Pareto distributions and the use of a log linear regression estimation of their parameters, as an alternative for the usually employed Hill's estimator. We also report a significant relationship between extreme distributional characteristics and observed returns, especially for developed countries. Publicado

In this paper we have studied the ability of relatively standard equilibrium asset pricing models to explain two important empirical regularities of asset returns extensively documented in the literature: i) returns can be predicted by a set of macro variables; and ii) returns are very volatile. Those empirical regularities are relevant because they have often been used to reject market efficiency. In the analysis we have made use of the approximation technology in the solution of intertemporal asset pricing models recently developed by Campbell (1993) in the form suggested by Restoy and Weil (1997). We have obtained evidence from eight OECD economies using both quarterly and annual observations. Equilibrium models seem generally to find fewer difficulties in explaining the volatility of returns than their predictability for general output processes. In the case of the United States, for annual frequencies the observed predictability and volatility of asset returns are broadly compatible with the predictions of equilibrium models for a reasonable specification of preferences.

We report simple regressions and Granger causality tests in order to understand the pattern of implied volatilities across exercise prices. We employ all calls and puts transacted between 16:00 and 16:45 on the Spanish IBEX-35 index from January 1994 to April 1996. Transaction costs, proxied by the bid–ask spread, seem to be a key determinant of the curvature of the volatility smile. Moreover, time to expiration, the uncertainty associated with the market and the relative market momentum are also important variables in explaining the smile.