The conditional dependence structure of insurance sector credit default swap indices

This study assesses the dependence structure of insurance sector credit default swap indices, using a copula-GARCH approach. We use daily data of the US, EU, and UK insurance sectors, covering the period from January 2004 to June 2013. We find substantial increases in dependence during the financial crisis periods. Prior to the crises, various copulas are found to best fit each pair; specifically, asymmetric tail dependence is found for the UK–US pair, suggesting the possibility of large simultaneous losses. However, during the crisis periods, the Frank copula fits best, with no significant tail dependence detected, implying low systemic risks.     

Empirical evidence of extreme dependence and contagion risk between main cryptocurrencies

This study examines the dependence and contagion risk between Bitcoin (BTC), Litecoin (LTC) and Ripple (XRP) using non-parametric mixture copulas (developed by Zimmer, 2012) and recently proposed methods of full-range tail dependence copulas (advanced by Hua, 2017, Su and Hua, 2017), for the period from 04-08-2013 to 17-06-2018. The Chi-plots and Kendall plots results show heavy tail dependence between each pairs of the cryptocurrencies. Evidence from the mixture copula indicates that for the BTC-LTC pair the upper-tail dependence is both stronger and more prevalent, while for the other pairs of cryptocurrencies the lower-tail dependence is very strong and more prevalent. However, the results of the full-range tail dependence copulas reveal a strong and prevalent upper and lower-tail dependence of each pairs of cryptocurrencies. These results provide evidence of significant risk contagion among price returns of major cryptocurrencies, both in bull and bear markets.     

Estimation and model selection of semiparametric multivariate survival functions under general censorship

We study estimation and model selection of semiparametric models of multivariate survival functions for censored data, which are characterized by possibly misspecified parametric copulas and nonparametric marginal survivals. We obtain the consistency and root-n$n$ asymptotic normality of a two-step copula estimator to the pseudo-true copula parameter value according to KLIC, and provide a simple consistent estimator of its asymptotic variance, allowing for a first-step nonparametric estimation of the marginal survivals. We establish the asymptotic distribution of the penalized pseudo-likelihood ratio statistic for comparing multiple semiparametric multivariate survival functions subject to copula misspecification and general censorship. An empirical application is provided.     

Estimation of copula-based semiparametric time series models

This paper studies the estimation of a class of copula-based semiparametric stationary Markov models. These models are characterized by nonparametric marginal distributions and parametric copula functions, while the copulas capture all the scale-free temporal dependence of the processes. Simple estimators of the marginal distribution and the copula parameter are provided, and their asymptotic properties are established under easily verifiable conditions. These results are used to obtain root-n consistent and asymptotically normal estimators of important features of the transition distribution such as the (nonlinear) conditional moments and conditional quantiles. The semiparametric conditional quantile estimators are automatically monotonic across quantiles, which is attractive for portfolio conditional value-at-risk calculations.     

Nonparametric inference on multivariate versions of Blomqvist’s beta and related measures of tail dependence

We consider nonparametric estimation of multivariate versions of Blomqvist’s beta, also known as the medial correlation coefficient. For a two-dimensional population, the sample version of Blomqvist’s beta describes the proportion of data which fall into the first or third quadrant of a two-way contingency table with cutting points being the sample medians. Asymptotic normality and strong consistency of the estimators are established by means of the empirical copula process, imposing weak conditions on the copula. Though the asymptotic variance takes a complicated form, we are able to derive explicit formulas for large families of copulas. For the copulas of elliptically contoured distributions we obtain a variance stabilizing transformation which is similar to Fisher’s z-transformation. This allows for an explicit construction of asymptotic confidence bands used for hypothesis testing and eases the analysis of asymptotic efficiency. The computational complexity of estimating Blomqvist’s beta corresponds to the sample size n, which is lower than the complexity of most competing dependence measures.      

Are the interdependence characteristics of the US and Canadian energy equity sectors nonlinear and asymmetric?

Nonlinear, symmetric, and asymmetric dependence characteristics in energy equity sectors matter to portfolio investors and risk managers because of the risks and diversification opportunities they entail. Specifically, nonlinear dependence dynamics between assets are harder to predict, monitor, and manage, and can make investment positions go wrong unexpectedly. In this paper, we investigate whether the dependence dynamics of US and Canadian large-capitalized energy equity portfolios are nonlinear, symmetric, or asymmetric. We draw our results by implementing a robust copula approach based on time-varying parameter copulas and vine copula methods. Both time varying parameter and vine-copula methods indicate that the Canadian energy sector portfolio is driven by nonlinear negative tail asymmetric dependence during the global financial crisis and when the full sample period is employed. On the other hand, it displays nonlinear symmetric dependence during the oil price crisis, implying the need for close monitoring and rebalancing and a more continuous assessment of long investment positions. The US energy sector portfolio is driven by positive tail asymmetric dependence, and by symmetric dependence dynamics during crisis and non-crisis periods.     

The t Copula and Related Copulas

The t copula and its properties are described with a focus on issues related to the dependence of extreme values. The Gaussian mixture representation of a multivariate t distribution is used as a starting point to construct two new copulas, the skewed t copula and the grouped t copula, which allow more heterogeneity in the modelling of dependent observations. Extreme value considerations are used to derive two further new copulas: the t extreme value copula is the limiting copula of componentwise maxima of t distributed random vectors; the t lower tail copula is the limiting copula of bivariate observations from a t distribution that are conditioned to lie below some joint threshold that is progressively lowered. Both these copulas may be approximated for practical purposes by simpler, better-known copulas, these being the Gumbel and Clayton copulas respectively.     

Improving forecasting performance using covariate-dependent copula models

Copulas provide an attractive approach to the construction of multivariate distributions with flexible marginal distributions and different forms of dependences. Of particular importance in many areas is the possibility of forecasting the tail-dependences explicitly. Most of the available approaches are only able to estimate tail-dependences and correlations via nuisance parameters, and cannot be used for either interpretation or forecasting. We propose a general Bayesian approach for modeling and forecasting tail-dependences and correlations as explicit functions of covariates, with the aim of improving the copula forecasting performance. The proposed covariate-dependent copula model also allows for Bayesian variable selection from among the covariates of the marginal models, as well as the copula density. The copulas that we study include the Joe-Clayton copula, the Clayton copula, the Gumbel copula and the Student’s $t$-copula. Posterior inference is carried out using an efficient MCMC simulation method. Our approach is applied to both simulated data and the S&P 100 and S&P 600 stock indices. The forecasting performance of the proposed approach is compared with those of other modeling strategies based on log predictive scores. A value-at-risk evaluation is also performed for the model comparisons.     

Size and support ratings of US banks

We examine whether Fitch support ratings of US banks depend on bank size. Using quarterly data for the period 2004:Q4 to 2012:Q4 and controlling for several factors that make large and small banks different, we find that bank size is positively related to support ratings. However, the effect is non-linear in line with the ‘too-big-to-rescue’ hypothesis. After the failure of Lehman Brothers and the passing of Dodd-Frank the relation between size and potential support has become stronger.     

A method to obtain new copulas from a given one

Given a strictly increasing continuous function φ from [0, 1] to [0, 1] and its pseudo-inverse φ^[−1], conditions that φ must satisfy for C_φ(x₁, . . . ,x_n)=φ^[−1](C(φ(x₁), . . . ,φ(x_n))) to be a copula for any copula C are studied. Some basic properties of the copulas obtained in this way are analyzed and several examples of generator functions φ that can be used to construct copulas C_φ are presented. In this manner, a method to obtain from a given copula C a variety of new copulas is provided. This method generalizes that used to construct Archimedean copulas in which the original copula C is the product copula, and it is related with mixtures     

Relationship between oil,stock prices and exchange rates: A vine copula based GARCH method

In this paper, we apply a vine copula approach to investigate the dynamic relationship between energy, stock and currency markets. Dependence modeling using vine copulas offers a greater flexibility and permits the modeling of complex dependency patterns for high-dimensional distributions. Using a sample of more than 10 years of daily return observations of the WTI crude oil, the Dow Jones Industrial average stock index and the trade weighted US dollar index returns, we find evidence of a significant and symmetric relationship between these variables. Considering different sample periods show that the dynamic of the relationship between returns is not constant over time. Our results indicate also that the dependence structure is highly affected by the financial crisis and Great Recession, over 2007–2009. Finally, there is evidence to suggest that the application of the vine copula model improves the accuracy of VaR estimates, compared to traditional approaches.     

The impact of data revisions on the robustness of growth determinants—a note on ‘determinants of economic growth: Will data tell?’

Ciccone and Jarociński (American Economic Journal: Macroeconomics 2010; 2 : 222–246) show that inference in Bayesian model averaging (BMA) can be highly sensitive to small data perturbations. In particular, they demonstrate that the importance attributed to potential growth determinants varies tremendously over different revisions of international income data. They conclude that ‘agnostic’ priors appear too sensitive for this strand of growth empirics. In response, we show that the found instability owes much to a specific BMA set‐up: first, comparing the same countries over data revisions improves robustness; second, much of the remaining variation can be reduced by applying an evenly ‘agnostic’ but flexible prior. Copyright © 2012 John Wiley & Sons, Ltd.     

Replication of unconditional Quantile Regressions by Firpo,Fortin and Lemieux (2009)

This paper is a narrow replication of Firpo, Fortin and Lemieux (Unconditional quantile regressions. Econometrica 2009; 77(3): 953–973), who propose a new estimation method, called ‘unconditional quantile regressions’. Using their empirical example, we confirm their results for the effects of unionization on US wage inequality during the period 1983–1985 for both conditional and unconditional quantile regressions. In addition, this paper applies the proposed estimation method to another paper by Autor, Katz and Kearney (Trends in US wage inequality: revising the revisionists. Review of Economics and Statistics 2008; 90(2): 300–323). The latter paper looks at measures of wage inequality in the US labor market data over the period 1963–2005. Copyright © 2015 John Wiley & Sons, Ltd.     

Estimating the Production Efficiency of US Foreign Direct Investment

This paper empirically estimates the efficiency of outward US foreign direct investment (FDI) within the framework of economies of scale, which is hypothesized as a main goal of ‘efficiency‐seeking’ multinationals. By using data for the period 1982–2007 and a nonhomogeneous production, the estimates of efficiency indicate an improvement in efficiency from decreasing to increasing returns to scale. The increasing productivity of both capital and labor is a contributory factor. Evidence of increasing elasticity of factor substitution attests to management use of a flexible production technology that enables efficient resource allocation. The results lend support to the ‘efficiency‐seeking’ hypothesis of FDI. Copyright © 2012 John Wiley & Sons, Ltd.     

A measure of mutual complete dependence

Two random variables X and Y on a common probability space are mutually completely dependent (m.c.d.) if each one is a function of the other with probability one. For continuous X and Y, a natural approach to constructing a measure of dependence is via the distance between the copula of X and Y and the independence copula. We show that this approach depends crucially on the choice of the distance function. For example, the L ^p -distances, suggested by Schweizer and Wolff, cannot generate a measure of (mutual complete) dependence, since every copula is the uniform limit of copulas linking m.c.d. variables. Instead, we propose to use a modified Sobolev norm, with respect to which mutual complete dependence cannot approximate any other kind of dependence. This Sobolev norm yields the first nonparametric measure of dependence which, among other things, captures precisely the two extremes of dependence, i.e., it equals 0 if and only if X and Y are independent, and 1 if and only if X and Y are m.c.d. Examples are given to illustrate the difference to the Schweizer–Wolff measure.     

Canada's Housing Bubble Story: Mortgage Securitization,the State,and the Global Financial Crisis

Canada's experience during and after the financial crisis appears to distinguish it from its international peers. Canadian real estate sales and values experienced record increases since the global financial crisis emerged in 2008, rather than declines, and Canada did not witness any bank failures. The dominant trope concerning Canada's financial and housing markets is that they are sound, prudent, appropriately regulated and ‘boring but effective’. It is widely assumed that Canadian banks did not need, nor receive, a ‘bailout’, that mortgage lending standards remained high, and that the securitization of mortgages was not widespread. The truth, however, does not accord with this mainstream view. In fact, the Canadian financial and housing markets reveal marked similarities with their international peers. Canada's banks needed, and received, a substantial ‘bailout’, while federal policies before and after the financial crisis resulted in the massive growth of mortgage securitization and record household indebtedness. This article documents the growth of Canada's housing bubble, the history of mortgage securitization, and of government policies implemented before and after the crisis. Instead of making the Canadian financial and housing sectors more resilient and sustainable, the outcomes of state responses are best understood as regressively redistributive.     

Modelling dependence using skew t copulas: Bayesian inference and applications

We construct a copula from the skew t distribution of Sahu et al. ( 2003 ). This copula can capture asymmetric and extreme dependence between variables, and is one of the few copulas that can do so and still be used in high dimensions effectively. However, it is difficult to estimate the copula model by maximum likelihood when the multivariate dimension is high, or when some or all of the marginal distributions are discrete‐valued, or when the parameters in the marginal distributions and copula are estimated jointly. We therefore propose a Bayesian approach that overcomes all these problems. The computations are undertaken using a Markov chain Monte Carlo simulation method which exploits the conditionally Gaussian representation of the skew t distribution. We employ the approach in two contemporary econometric studies. The first is the modelling of regional spot prices in the Australian electricity market. Here, we observe complex non‐Gaussian margins and nonlinear inter‐regional dependence. Accurate characterization of this dependence is important for the study of market integration and risk management purposes. The second is the modelling of ordinal exposure measures for 15 major websites. Dependence between websites is important when measuring the impact of multi‐site advertising campaigns. In both cases the skew t copula substantially outperforms symmetric elliptical copula alternatives, demonstrating that the skew t copula is a powerful modelling tool when coupled with Bayesian inference. Copyright © 2010 John Wiley & Sons, Ltd.     

The Early Millennium Slowdown: Replicating the Peersman (2005) Results

This paper undertakes both a narrow and wide replication of the constant coefficients vector autoregression (VAR) identified with sign restrictions considered by Peersman (Journal of Applied Econometrics 2005; 20 (2): 185–207. His results for the US are robust to an increase in the sample period from 2002:Q2 to 2014:Q2, but the extension to time‐varying parameters highlights the importance of testing the robustness of results against time variation. In particular, there are differences across models regarding the role of individual shocks during the 2001 US slowdown. Copyright © 2015 John Wiley & Sons, Ltd.     

Semiparametric Estimator for Binary‐outcome Sample Selection: Prejudice Matters in Election

a semiparametric estimator for binary‐outcome sample‐selection models is proposed that imposes only single index assumptions on the selection and outcome equations without specifying the error term distribution. I adopt the idea in Lewbel (2000) using a ‘special regressor’ to transform the binary response Y so that the transformed Y becomes linear in the latent index, which then makes it possible to remove the selection correction term by differencing the transformed Y equation. There are various versions of the estimator, which perform differently trading off bias and variance. A simulation study is conducted, and then I apply the estimators to US presidential election data in 2008 and 2012 to assess the impact of racial prejudice on the elections, as a black candidate was involved for the first time ever in the US history.     

ANOTHER LOOK AT ‘BANK COMPETITION AND FINANCIAL STABILITY: MUCH ADO ABOUT NOTHING’?

This study replicates Zigraiova and Havranek's (2016) meta-analysis of banking competition and financial stability. It performs multiple types of replications: a ‘Reproduction’ replication where Z&H's data and code are verified to reproduce the results of their study; a ‘Repetition’ replication where the studies used by Z&H are independently recoded and then re-analysed; an ‘Extension’ replication where additional studies on banking competition and stability are analysed; and a ‘Robustness Analysis’ where we check Z&H's results using an alternative empirical procedure. Our analysis strongly confirms Z&H's main finding that competition in the banking sector has an economically negligible effect on financial stability. This result is consistently confirmed across a variety of replication analyses. Most impressively, we confirm their finding even when we analyse a completely independent set of 35 studies not included in Z&H's meta-analysis. Our results for Z&H's other findings are less supportive. As the first comprehensive replication of a meta-analysis, this study also provides insights into the robustness of meta-analysis. We find that meta-regression analysis, where estimated effects are related to data, estimation, and study characteristics, is sensitive to how data are coded and to the choice of estimation procedure; and that this sensitivity extends to ‘best practice’ estimates.