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.     

Financial dependence analysis: applications of vine copulas

This paper features the application of a novel and recently developed method of statistical and mathematical analysis to the assessment of financial risk, namely regular vine copulas. Dependence modelling using copulas is a popular tool in financial applications but is usually applied to pairs of securities. Vine copulas offer greater flexibility and permit the modelling of complex dependence patterns using the rich variety of bivariate copulas that can be arranged and analysed in a tree structure to facilitate the analysis of multiple dependencies. We apply regular vine copula analysis to a sample of stocks comprising the Dow Jones index to assess their interdependencies and to assess how their correlations change in different economic circumstances using three different sample periods around Global Financial Crisis (GFC).: pre‐GFC (January 2005 to July 2007), GFC (July 2007 to September 2009) and post‐GFC periods (September 2009 to December 2011). The empirical results suggest that the dependencies change in a complex manner, and there is evidence of greater reliance on the Student‐t copula in the copula choice within the tree structures for the GFC period, which is consistent with the existence of larger tails in the distributions of returns for this period. One of the attractions of this approach to risk modelling is the flexibility in the choice of distributions used to model co‐dependencies. The practical application of regular vine metrics is demonstrated via an example of the calculation of the Value at Risk of a portfolio of stocks.     

Jump dynamics,spillover effect and option valuation

In this paper, we propose an affine discrete-time model that incorporates the jump process and spillover effect for valuing the 50 ETF options in China. Based on the proposed model, a closed-form solution is also derived for the new dynamics of underlying asset, which facilitates option pricing. The empirical results show that the proposed model offers greater economic benefit with reduced pricing errors than the traditional benchmark models, including the popular HNGARCH model of Heston and Nandi (2000), GARV model of Christoffersen et al. (2014), and BPJVM model of Christoffersen et al. (2015). Our finding is important for financial risk management and investment in Chinese derivatives market.     

Project financing,entrepreneurial activity,and investment in the presence of asymmetric information

We analyze a two-period signaling model in which a representative entrepreneur in a regional economy has a project that generates a random cash flow and that requires investment that the entrepreneur raises from a competitive market. The project's type is known to the entrepreneur but not to the investors. Further, the entrepreneur is restricted to issuing debt only or equity only. We first show that there is no separating perfect Bayesian equilibrium (PBE) contract involving the issuance of equity only, that there exists a pooling PBE contract involving the issuance of equity only, and that a debt contract is preferred to an equity contract by our entrepreneur. Next, we suppose that the entrepreneur incurs a non-pecuniary cost of financial distress F > 0 whenever he is unable to make a repayment at time t = 1. We provide conditions on F under which a pooling PBE contract with debt exists and a separating PBE contract with debt and equity exists. Finally, we examine whether a high type entrepreneur will prefer a setting with a cost of financial distress (F > 0) or a setting in which there is no such cost (F = 0).     

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 non-relevance of the elusive holy grail of asset pricing tests: The “true” market portfolio does not alter CAPM validity conclusions

Empirical evaluations of CAPM usually attach a caveat that rejection is conditional on the choice of market proxy. We explore the criticality of the proxy choice disclaimer. Using different proxies and comprehensive simulations of the unobserved “true” market in Fama–MacBeth tests of CAPM, we find that the significance (t-statistics) corresponding to betas remain consistently unaltered, even if the proxy is a small fraction of or has a low correlation with the true market. The constancy of t-statistics persists in a simulated true-CAPM world as well: if CAPM is indeed valid, the choice of proxy is unlikely to reject it erroneously. Identity of the elusive true market portfolio and the choice of representative proxy cannot overturn conclusions on validity of CAPM based on Fama–MacBeth tests. Roll’s Critique, incontrovertible in theory, may be quite forgiving in practice – CAPM cannot be resurrected by a “closer” approximation of the elusive true market portfolio when it has commonly been rejected.     

How long do housing cycles last? A duration analysis for 19 OECD countries

This paper analyzes the duration of house price upturns and downturns in the last 40 years for 19 OECD countries. Both upturns and downturns display duration dependence: they are more likely to end as their duration increases. Downturns display also lagged duration dependence: they are less likely to end if the previous upturn was particularly long. These patterns are consistent with a boom-bust view of housing price dynamics, where booms represent departures from fundamentals that are increasingly difficult to sustain and busts serve as readjustment periods. Findings are robust to the inclusion of macroeconomic variables, which allow for the estimation of additional determinants of house price expansions and contractions.     

Dynamic spatial autoregressive models with autoregressive and heteroskedastic disturbances

We propose a new class of models specifically tailored for spatiotemporal data analysis. To this end, we generalize the spatial autoregressive model with autoregressive and heteroskedastic disturbances, that is, SARAR(1, 1), by exploiting the recent advancements in score‐driven (SD) models typically used in time series econometrics. In particular, we allow for time‐varying spatial autoregressive coefficients as well as time‐varying regressor coefficients and cross‐sectional standard deviations. We report an extensive Monte Carlo simulation study in order to investigate the finite‐sample properties of the maximum likelihood estimator for the new class of models as well as its flexibility in explaining a misspecified dynamic spatial dependence process. The new proposed class of models is found to be economically preferred by rational investors through an application to portfolio optimization.     

An analysis of dependence between Central and Eastern European stock markets

We examine the dependence structure between four Central and Eastern European (CEE) stock markets (Czech Republic, Hungary, Poland and Romania) using static and dynamic copula functions with different forms of tail dependence. We find evidence of positive dependence between all CEE stock markets, although this dependence is stronger between the Hungarian, Czech and Polish markets than between these markets and the Romanian market. We also find evidence of symmetric tail dependence, although not for the Hungarian and Czech markets. The dependence is time-varying and intensified after the onset of the recent global financial crisis. These results confirm that CEE stock markets are gradually coupling, a fact that has risk management implications for policymakers and investors.     

Modeling non-normal corporate bond yield spreads by copula

This research focuses on modeling for how corporate bond yield spreads are affected by explanatory variables such as equity volatility, interest rate volatility, r, slope, rating, liquidity, coupon rate, and maturity. The existing literature assumes normality and linearity in the analysis, which is not the case in our sample. Thus, through a powerful and flexible copula approach, we study the dependence at the mean of the joint distribution by using the Gaussian copula marginal regression method and the dependence structure at the tails by using various copula functions. To our knowledge, this is the first application of the copula marginal regression model to bond market data. In addition, we employ several copula functions to test for the tail dependence between yield spreads and other explanatory variables. We find stronger tail dependence in the joint upper tail for the relation between equity volatility and yield spreads, among others. This result indicates the positive effect of equity volatility on yield spreads in the upper tail is greater than that in the low tail. This finding should be useful to practitioners, such as investors. By relying on better-fitting, more meaningful statistical models, this paper contributes to the extant literature on how corporate bond yield spreads are determined.     

EVT and tail-risk modelling: Evidence from market indices and volatility series

Value-at-Risk (VaR) has become the universally accepted risk metric adopted internationally under the Basel Accords for banking industry internal control, capital adequacy and regulatory reporting. The recent extreme financial market events such as the Global Financial Crisis (GFC) commencing in 2007 and the following developments in European markets mean that there is a great deal of attention paid to risk measurement and risk hedging. In particular, to risk indices and attached derivatives as hedges for equity market risk. The techniques used to model tail risk such as VaR have attracted criticism for their inability to model extreme market conditions. In this paper we discuss tail specific distribution based Extreme Value Theory (EVT) and evaluate different methods that may be used to calculate VaR ranging from well known econometrics models of GARCH and its variants to EVT based models which focus specifically on the tails of the distribution. We apply Univariate Extreme Value Theory to model extreme market risk for the FTSE100 UK Index and S&P-500 US markets indices plus their volatility indices. We show with empirical evidence that EVT can be successfully applied to financial market return series for predicting static VaR, CVaR or Expected Shortfall (ES) and also daily VaR and ES using a GARCH(1,1) and EVT based dynamic approach to these various indices. The behaviour of these indices in their tails have implications for hedging strategies in extreme market conditions.     

Do cryptocurrencies provide better hedging? Evidence from major equity markets during COVID-19 pandemic

Using the five-minute interval price data of two cryptocurrencies and eight stock market indices, we examine the risk spillover and hedging effectiveness between these two assets. Our approach provides a comparative assessment encompassing the pre-COVID-19 and COVID-19 sample periods. We employ copula models to assess the dependence and risk spillover from Bitcoin and Ethereum to stock market returns during both the pre-COVID-19 and COVID-19 periods. Notably, the COVID-19 pandemic has increased the risk spillover from Bitcoin and Ethereum to stock market returns. The findings vis-à-vis portfolio weights and hedge effectiveness highlight hedging gains; however, optimal investments in Bitcoin and Ethereum have reduced during the COVID-19 pandemic, while the cost of hedging has increased during this period. The findings also confirm that cryptocurrencies cannot provide incremental gains by hedging stock market risk during the COVID-19 pandemic.     

A bigger piece of the pie: The relative importance of affiliative and change-oriented citizenship and task performance in predicting overall job performance

We examine the extent to which both in-role (task performance) and extra-role dimensions of performance (organizational citizenship behaviors) account for variance in ratings of overall job performance by utilizing currently available meta-analytic estimates. Relative weight analysis results show that overall performance is determined more by three OCB forms in combination (RW = 0.34; %RW = 72.9%) than by task performance (RW = 0.12; %RW = 27.1%). Among the OCB forms, the relative weight of OCB-O (RW = 0.17; %RW = 36.9%) is greater than those of OCB-I (RW = 0.11; %RW = 0.22.9%) and OCB-CH (RW = 0.06; %RW = 13.1%). Consistent with the results from a relative weight analysis, results from a series of multiple regression analyses also show that the incremental contribution (ΔR²) of each performance dimension above and beyond the other performance dimensions is the highest for OCB-O (0.056), followed by those of task performance (0.041), OCB-CH (0.007), and OCB-I (0.003). We discuss the theoretical and practical implications of these findings along with study limitations and future research directions.     

Default models based on scale mixtures of Marshall-Olkin copulas: properties and applications

We present a unification of the Archimedean and the Lévy-frailty copula model for portfolio default models. The new default model exhibits a copula known as scale mixture of Marshall-Olkin copulas and an investigation of the dependence structure reveals that desirable properties of both original models are combined. This allows for a wider range of dependence patterns, while the analytical tractability is retained. Furthermore, simultaneous defaults and default clustering are incorporated. In addition, a hierarchical extension is presented which allows for a heterogeneous dependence structure. Finally, the model is applied to the pricing of CDO contracts. For this purpose, an efficient Laplace transform inversion approach is developed. Supporting a separation of marginal default probabilities and dependence structure, the model can be calibrated to CDS contracts in a first step. In a second step, the calibration of several parametric families to CDO contracts demonstrates a good fitting quality, which further emphasizes the suitability of the approach.     

Rational plunging and the option value of sequential investment: The case of petroleum exploration

The potential to invest sequentially in related assets creates a tradeoff between diversification and concentration. Loading a portfolio with correlated assets has the potential to inflate variance, but also creates information spillovers and real options that may augment total return and mitigate variance. We examine this tradeoff in the context of petroleum exploration. Using a simple model of geological dependence, we show that the value of learning options creates incentives for investors to plunge into dependence; i.e., to assemble portfolios of highly correlated exploration prospects. Risk-neutral and risk-averse investors are distinguished not by the plunging phenomenon, but by the threshold level of dependence that triggers such behavior. Aversion to risk does not imply aversion to dependence. Indeed the potential to plunge should be larger for risk-averse investors than for risk-neutral investors. We test the empirical validity of our theory by examining bidding activity in petroleum lease sales. We find significant plunging behavior across a broad sample of oil companies. We also find that privately-held firms pursue even more concentrated (less diversified) prospect portfolios than publicly-held firms—which we attribute to risk aversion rather than size.     

Risk spillover analysis of China’s financial sectors based on a new GARCH copula quantile regression model

This study employs a new GARCH copula quantile regression model to estimate the conditional value at risk for systemic risk spillover analysis. To be specific, thirteen copula quantile regression models are derived to capture the asymmetry and nonlinearity of the tail dependence between financial returns. Using Chinese stock market data over the period from January 2007 to October 2020, this paper investigates the risk spillovers from the banking, securities, and insurance sectors to the entire financial system. The empirical results indicate that (i) three financial sectors contribute significantly to the financial system, and the insurance sector displays the largest risk spillover effects on the financial system, followed by the banking sector and subsequently the securities sector; (ii) the time-varying risk spillovers are much larger during the global financial crisis than during the periods of the banking liquidity crisis, the stock market crash and the COVID-19 pandemic. Our results provide important implications for supervisory authorities and portfolio managers who want to maintain the stability of China’s financial system and optimize investment portfolios.     

Risk spillover analysis across worldwide ESG stock markets: New evidence from the frequency-domain

With the increasing global awareness of green environmental protection, the international environmental, social, and governance (ESG) stock markets are developing rapidly together with rising risk linkages across worldwide markets. Therefore, this study explores the risk spillover characteristics of international ESG stock markets in the time and frequency domains and constructs a risk linkage network to further explore the risk contagion mechanism. The results show that in most cases, the developed North American market is the core of outward risk spillover in international ESG stock markets. The entire system presents a small-world structure, and the internal regions display different risk spillover characteristics. Moreover, international ESG markets generally have strong time–frequency spillover and medium-frequency (a month to a year) spillover. In contrast, the high- (a day to a month) and low-frequency (more than one year) spillovers are located at relatively low levels, but they will rise significantly under sudden financial events. The empirical results expand the ESG stock market's theoretical framework and provide a reference for investors and market regulators to reduce the investment risk of ESG.     

The impact of China's stock market reforms on its international stock market linkages

This paper investigates how China's stock market reforms have affected the stock market linkages between China and Korea, Japan and the US respectively. We firstly use a 4 × 4 asymmetric GARCH-BEKK model and a series of likelihood ratio tests to uncover China's regional and global linkages between 1992 and 2010 and during three sub-periods representing the stages of the Chinese reforms. The results show that Chinese stock market is linked to these overseas markets and the reforms permit spillovers to these markets from China. The subsequent regression analyses of the time-varying conditional correlations, in the presence of growing economic integration, exchange rate risk and financial turbulence, further indicate that the interdependences between China and the regional markets increase due to the implementation of liberalisation policies. However, the correlation between China and the global market remains weak even though this correlation responds positively to the institutional reforms on China's stock market additionally.     

Strategic behavior across gender: A comparison of female and male expert chess players

This paper aims to measure differences in risk behavior among expert chess players. The study employs a panel data set on international chess with 1.4 million games recorded over a period of 11 years. The structure of the data set allows us to use individual fixed-effect estimations to control for aspects such as innate ability as well as other characteristics of the players. Most notably, the data contains an objective measure of individual playing strength, the so-called Elo rating. In line with previous research, we find that women are more risk-averse than men. A novel finding is that men choose more aggressive strategies when playing against female opponents even though such strategies reduce their winning probability.     

Dynamic stochastic copula models: estimation,inference and applications

We propose a new dynamic copula model in which the parameter characterizing dependence follows an autoregressive process. As this model class includes the Gaussian copula with stochastic correlation process, it can be viewed as a generalization of multivariate stochastic volatility models. Despite the complexity of the model, the decoupling of marginals and dependence parameters facilitates estimation. We propose estimation in two steps, where first the parameters of the marginal distributions are estimated, and then those of the copula. Parameters of the latent processes (volatilities and dependence) are estimated using efficient importance sampling. We discuss goodness‐of‐fit tests and ways to forecast the dependence parameter. For two bivariate stock index series, we show that the proposed model outperforms standard competing models. Copyright © 2010 John Wiley & Sons, Ltd.