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For vector Ito semimartingale dynamics, we derive the asymptotic distributions of likelihood-ratio-type test statistics for the purpose of identifying the eigenvalue structure of both integrated and spot covariance matrices estimated using high-frequency data. Unlike the existing approaches where the cross-section dimension grows to infinity, our tests do not necessarily require large cross-section and thus allow for a wide range of applications. The tests, however, are based on non-standard asymptotic distributions with many nuisance parameters. Another contribution of this paper consists in proposing a bootstrap method to approximate these asymptotic distributions. While standard bootstrap methods focus on sampling point-wise returns, the proposed method replicates features of the asymptotic approximation of the statistics of interest that guarantee its validity. A Monte Carlo simulation study shows that the bootstrap-based test controls size and has power for even moderate size samples.

Hard lockdowns have left policymakers to face the ethical dilemma of choosing between saving lives and saving the economy. However, massive testing could have helped to respond more effectively to Covid‐19 crisis. In this paper, we study the trade‐off between infection control, lockdown and testing. The aim is to understand how these policies can be effectively combined to contain Covid‐19 without damaging the economy. An extended SIR epidemic model is developed to identify the set of testing and lockdown levels that lead to a reproduction number below one, thus to infection control and saving lives. Depending on whether the testing policy is static or dynamic, the model suggests that testing 4% to 7% of the population is the way to safely reopen the economy and the society.

This article aims to use a bargaining power model to reduce moral hazard—in the form of entrepreneurial effort shirking—and derive an optimum sharing ratio of a Profit and Loss Sharing (PLS) contract that involves a Venture Capitalist and an Entrepreneur. The model reveals the following interesting findings. First, under complete information—where the Venture Capitalist has a bargaining power ‐ Venture Capitalist offers the entrepreneur a profit sharing ratio that is less than her capital contribution ratio. Second, in an incomplete information setting, the entrepreneur demands a profit sharing ratio higher than her capital contribution ratio when the sum of the marginal cost (from exercising a higher effort) and private benefits (from exercising a low effort) is greater than the marginal return (from exercising a high effort). In addition, the model is used to derive a span of negotiation about the profit sharing ratio. Finally, an agent based simulation (Netlogo) platform is considered to implement the model, which allows a faster numerical calculations of the profit share and helps decide on the validity of the funding contract.

In perfect capital markets, the futures price of an asset should be an unbiased forecast of its realized spot price when the contract matures. In reality, futures prices are often higher for some assets and lower for others. However, there is no stability in the relationship between futures prices and the realized spot prices. This instability has been a puzzle in the existing financial literature. The key to this puzzle may lie in the nature of the model and the lack of market imperfections. In this study, we take a theoretical approach in a dynamic multi-period environment. We incorporate competition between disparate economic agents and impose financial frictions (i.e., imperfections) that are in the form of hedging and borrowing limits on them. Our model gives rise to multiple equilibria, each with unique market clearing prices, with the market switching between these equilibria. Our analysis incorporates a comprehensive consideration of the risks faced by the futures markets participants (i.e., speculators and hedgers) and leads to a better understanding of the puzzle. JEL Classification

We consider measures of Granger causality in quantiles, which detect and quantify both linear and nonlinear causal effects between random variables. The measures are based on nonparametric quantile regressions and defined as logarithmic functions of restricted and unrestricted expectations of quantile check loss functions. They can consistently be estimated by replacing the unknown expectations of check loss functions by their nonparametric kernel estimates. We derive a Bahadur-type representation for the nonparametric estimator of the measures. We establish the asymptotic distribution of this estimator, which can be used to build tests for the statistical significance of the measures. Thereafter, we show the validity of a smoothed local bootstrap that can be used in finite-sample settings to perform statistical tests. A Monte Carlo simulation study reveals that the bootstrap-based test has a good finite-sample size and power properties for a variety of data-generating processes and different sample sizes. Finally, we provide an empirical application to illustrate the usefulness of measuring Granger causality in quantiles. We quantify the degree of predictability of the quantiles of equity risk premium using the variance risk premium, unemployment rate, inflation, and the effective federal funds rate. The empirical results show that the variance risk premium and effective federal funds rate have a strong predictive power for predicting the risk premium when compared to that of the predictive power of the other two macro variables. In particular, the variance risk premium is able to predict the center, lower, and upper quantiles of the distribution of the risk premium; however, the effective federal funds rate predicts only the lower and upper quantiles. Nevertheless, unemployment and inflation rates have no effect on the risk premium.

COVID-19 has created an unprecedented global health crisis that caused millions of infections and deaths worldwide. Many, however, argue that pre-existing social inequalities have led to inequalities in infection and death rates across social classes, with the most-deprived classes are worst hit. In this paper, we derive semi/non-parametric estimators of Health Concentration Curve (HC) that can quantify inequalities in COVID-19 infections and deaths and help identify the social classes that are most at risk of infection and dying from the virus. We express HC in terms of copula function that we use to build our estimators of HC. For the semi-parametric estimator, a parametric copula is used to model the dependence between health and socio-economic variables. The copula function is estimated using maximum pseudo-likelihood estimator after replacing the cumulative distribution of health variable by its empirical analogue. For the non-parametric estimator, we replace the copula function by a Bernstein copula estimator. Furthermore, we use the above estimators of HC to derive copula-based estimators of health Gini coeffcient. We establish the consistency and the asymptotic normality of HC’s estimators. Using different data-generating processes and sample sizes, a Monte-Carlo simulation exercise shows that the semiparametric estimator outperforms the smoothed nonparametric estimator, and that the latter does better than the empirical estimator in terms of Integrated Mean Squared Error. Finally, we run an extensive empirical study to illustrate the importance of HC’s estimators for investigating inequality in COVID-19 infections and deaths in the U.S. The empirical results show that the inequalities in state’s socio-economic variables like poverty, race/ethnicity, and economic prosperity are behind the observed inequalities in the U.S.’s COVID-19 infections and deaths.

Weather Effect on both US and UK Stock Markets: Does weather affect US versus UK Islamic equities returns? What are the Channels of Impact?&rsquo

We estimate the term structures of risk-neutral forward variance and skewness, and examine their predictive power for equity market excess returns and variance. We use Partial Least Squares to extract a single predictive factor from each term structure that is motivated by the theoretical implications of affine no-arbitrage models. The empirical analysis shows that an increased forward variance factor, FVF (forward skewness factor, FSF) corresponds to a more negatively sloped forward variance (more U-shaped forward skewness) term structure, and significantly forecasts higher future market excess returns and variance. More importantly, FSF exhibits predictive power for market returns that is stronger than, and incremental to, that provided by FVF. However, it does not outperform FVF in terms of excess variance predictability.

This paper shows that lagged information transmission between industry port-folio and market prices entails cointegration. We analyze monthly industry portfolios in the US market for the period 1963–2015. We find cointegration between six industry portfolio and market prices. We show that the equilibrium error, the long-term common factor between industry portfolio and market cumulative returns, has strong predictive power for excess industry portfolio returns. In line with gradual information diffusion across connected industries, the equilibrium error proxies for changes in the investment oppor-tunity set that lead to industry return predictability by informed investors. Forecasting models including the equilibrium error have superior forecasting performance relative to models without it, illustrating the importance of cointegration between the industry portfolio and market prices. Overall, our findings have important implications for investment and risk-management decisions, since the out-of-sample explanatory power of the equilibrium error is economically meaningful for making optimal portfolio allocations. Ministerio de Educación, Cultura y Deporte, Grant/Award Number:ECO2017-83255-C3-2-P

We propose model-free measures for Granger causality in mean between random variables. Unlike the existing measures, ours are able to detect and quantify nonlinear causal effects. The new measures are based on nonparametric regressions and defined as logarithmic functions of restricted and unrestricted mean square forecast errors. They are easily and consistently estimated by replacing the unknown mean square forecast errors by their nonparametric kernel estimates. We derive the asymptotic normality of nonparametric estimator of causality measures, which we use to build tests for their statistical significance. We establish the validity of smoothed local bootstrap that one can use in finite sample settings to perform statistical tests. Monte Carlo simulations reveal that the proposed test has good finite sample size and power properties for a variety of data-generating processes and different sample sizes. Finally, the empirical importance of measuring nonlinear causality in mean is also illustrated. We quantify the degree of nonlinear predictability of equity risk premium using variance risk premium. Our empirical results show that the variance risk premium is a very good predictor of risk premium at horizons less than six months. We also find that there is a high degree of predictability at horizon one-month which can be attributed to a nonlinear causal effect.

In this paper we provide three nonparametric tests of independence between continuous random variables based on the Bernstein copula distribution function and the Bernstein copula density function. The first test is constructed based on a Cramér-von Mises divergence-type functional based on the empirical Bernstein copula process. The two other tests are based on the Bernstein copula density and use Cramér-von Mises and Kullback–Leibler divergence-type functionals, respectively. Furthermore, we study the asymptotic null distribution of each of these test statistics. Finally, we consider a Monte Carlo experiment to investigate the performance of our tests. In particular we examine their size and power which we compare with those of the classical nonparametric tests that are based on the empirical distribution function.

We propose an extension of the existing information criterion-based structural break identification approaches. The extended approach helps identify both pure structural change (break) and partial structural change (break). A pure structural change refers to the case when breaks occur simultaneously in all parameters of regression equation, whereas a partial structural change happens when breaks occur in some parameters only. Our approach consistently outperforms other well-known approaches. We also extend the simulation studies of Bai and Perron (2006 and Hall, Osborn and Sakkas (2013) by including more general cases. This provides more comprehensive results and reveals the cases where the existing identification approaches lose power, which should be kept in mind when applying them.

This paper proposes a novel methodology to construct optimal portfolios that explicitly incorporates the occurrence of systemic events. Investors maximize a modified Sharpe ratio that is conditional on a systemic event, with the latter interpreted as a low market return environment. We solve the portfolio allocation problem analytically under the absence of short-selling restrictions and numerically when short-selling restrictions are imposed. This approach for obtaining an optimal portfolio allocation is made operational by embedding it in a multivariate dynamic setting using dynamic conditional correlation and copula models. We evaluate the out-of-sample performance of our portfolio empirically on the US stock market over the period 2007 to 2020 using ex-post wealth paths and systemic risk metrics against mean-variance, equally-weighted, and global minimum variance portfolios. Our portfolio maximizing a modified Sharpe ratio outperforms all competitors under market distress and remains competitive in non-crisis periods.

We introduce a nonparametric measure to quantify the degree of heteroskedasticity at a fixed quantile of the conditional distribution of a random variable. Our measure of heteroskedasticity is based on nonparametric quantile regressions and is expressed in terms of unrestricted and restricted expectations of quantile loss functions. It can be consistently estimated by replacing the unknown expectations by their nonparametric estimates. We derive a Bahadur-type representation for the nonparametric estimator of the measure. We provide the asymptotic distribution of this estimator, which one can use to build tests for the statistical significance of the measure. Thereafter, we establish the validity of a fixed regressor bootstrap that one can use in finite-sample settings to perform tests. A Monte Carlo simulation study reveals that the bootstrap-based test has a good finite sample size and power for a variety of data generating processes and different sample sizes. Finally, two empirical applications are provided to illustrate the importance of the proposed measure.

This paper aims to provide a better understanding of the causal structure in a multivariate time series by introducing several statistical procedures for testing indirect and spurious causal effects. In practice, detecting these effects is a complicated task, since the auxiliary variables that transmit/induce indirect/spurious causality are very often unknown. The availability of hundreds of economic variables makes this task even more difficult since it is generally infeasible to find the appropriate auxiliary variables among all the available ones. In addition, including hundreds of variables and their lags in a regression equation is technically difficult. The paper proposes several statistical procedures to test for the presence of indirect/spurious causality based on big data analysis. Furthermore, it suggests an identification procedure to find the variables that transmit/induce the indirect/spurious causality. Finally, it provides an empirical application where 135 economic variables were used to study a possible indirect causality from money/credit to income.

We review several exact sign-based tests that have been recently proposed for testing orthogonality between random variables in the context of linear and nonlinear regression models. The sign tests are very useful when the data at the hands contain few observations, are robust against heteroskedasticity of unknown form, and can be used in the presence of non-Gaussian errors. These tests are also flexible since they do not require the existence of moments for the dependent variable and there is no need to specify the nature of the feedback between the dependent variable and the current and future values of the independent variable. Finally, we discuss several applications where the sign-based tests can be used to test for multi-horizon predictability of stock returns and for the market efficiency.

We estimate the term structures of risk-neutral forward variance and skewness, and examine their predictive power for equity market excess returns and variance. We use Partial Least Squares to extract a single predictive factor from each term structure that is motivated by the theoretical implications of affine no-arbitrage models. The empirical analysis shows that an increased forward variance factor, FVF (forward skewness factor, FSF) corresponds to a more negatively sloped forward variance (more U-shaped forward skewness) term structure, and significantly forecasts higher future market excess returns and variance. More importantly, FSF exhibits predictive power for market returns that is stronger than, and incremental to, that provided by FVF. However, it does not outperform FVF in terms of excess variance predictability.

We empirically investigate the short-run impact of anticipated and unanticipated unemployment rates on stock prices. We particularly examine the nonlinearity in the stock market’s reaction to the unemployment rate and study the effect at each individual point (quantile) of the stock return distribution. Using nonparametric Granger causality and quantile regression-based tests, we find that only anticipated unemployment rate has a strong impact on stock prices. Quantile regression analysis shows that the causal effects of anticipated unemployment rate on stock returns are usually heterogeneous across quantiles. For the quantile range 0.35, 0.80, an increase in the anticipated unemployment rate leads to an increase in stock market prices. For other quantiles, the impact is generally statistically insignificant. Thus, an increase in the anticipated unemployment rate is, in general, good news for stock prices. Finally, we offer a reasonable explanation for the reason, and manner in which, the unemployment rate affects stock market prices. Using the Fisher and Phillips curve equations, we show that a high unemployment rate is followed by monetary policy action of the Federal Reserve (Fed). When the unemployment rate is high, the Fed decreases the interest rate, which in turn increases the stock market prices.

We propose a nonparametric estimation and inference for conditional density based Granger causality measures that quantify linear and nonlinear Granger causalities. We first show how to write the causality measures in terms of copula densities. Thereafter, we suggest consistent estimators for these measures based on a consistent nonparametric estimator of copula densities. Furthermore, we establish the asymptotic normality of these nonparametric estimators and discuss the validity of a local smoothed bootstrap that we use in finite sample settings to compute a bootstrap bias-corrected estimator and to perform statistical tests. A Monte Carlo simulation study reveals that the bootstrap bias-corrected estimator behaves well and the corresponding test has quite good finite sample size and power properties for a variety of typical data generating processes and different sample sizes. Finally, two empirical applications are considered to illustrate the practical relevance of nonparametric causality measures.

We model portfolio weights as a function of latent factors that summarize the information in a large number of economic variables. This approach (hereafter diffusion index approach) offers the opportunity to exploit a much richer information base to improve portfolio selection. We use factor analysis to estimate the space spanned by the factors. This provides consistent estimates for the optimal weights as the number of economic variables and sample size go to infinity. We consider an empirical application to illustrate the practical usefulness of our approach. The results indicate that the diffusion index approach helps to improve the portfolio performance.

Copulas are widely used for modeling the dependence structure of multivariate data. Many methods for estimating the copula density functions are investigated. In this paper, we study the asymptotic properties of the Bernstein estimator for unbounded copula density functions. We show that the estimator converges to infinity at the corner and we establish its relative convergence when the copula density is unbounded. Also, we provide the uniform strong consistency of the estimator on every compact in the interior region. We investigate the finite sample performance of the estimator via an extensive simulation study and we compare the Bernstein copula density estimator with other nonparametric methods. Finally, we consider an empirical application where the asymmetric dependence between international equity markets (US, Canada, UK, and France) is examined.

Recent research identifies several industry-related patterns that standard asset pricing models cannot explain effectively. This paper investigates what explains the cross-section of returns of firms in the oil industry and, in particular, how well an oil factor performs in comparison with the common systematic factors identified in the literature. We conduct a time series analysis and demonstrate that the oil factor has substantial explanatory power over traditional factors. A cross-sectional regression shows that the size, momentum and oil factors are associated with a positive risk premium and are able to explain the cross-sectional variation in stock returns in the oil industry. Our results suggest that investors demand compensation for the exposure to oil price changes, which has implications for the computation of the cost of equity. info:eu-repo/semantics/publishedVersion

We show, in a broad class of affine general equilibrium models with long-run risk, that the covariances between asset returns are linear functions of risk factors. We use a dynamic conditional correlation model to measure the covariances of stock and sovereign bond markets in the Euro Area. We use a new approach to measure risk factors based on Google search data. The factors explain 50 to 60% of the variation of the covariances between European stocks and 25 to 35% of the covariances between European bonds. The information improves the portfolio performance compared to an equally weighted portfolio. (C) 2016, Elsevier Inc. All rights reserved. Financial support from the Spanish Ministry of Education through grants #ECO2010-19357, ECO2014-56676-C2-1-P, MDM 2014-0431 and the Comunidad de Madrid, MadEco-CM (S2015/HUM-3444) are acknowledged.

We study risk assessment using an optimal portfolio in which the weights are functions of latent factors and firm-specific characteristics (hereafter, diffusion index portfolio). The factors are used to summarize the information contained in a large set of economic data and thus reflect the state of the economy. First, we evaluate the performance of the diffusion index portfolio and compare it to both that of a portfolio in which the weights depend only on firm-specific characteristics and an equally weighted portfolio. We then use value-at-risk, expected shortfall, and downside probability to investigate whether the weights-modeling approach, which is based on factor analysis, helps reduce market risk. Our empirical results clearly indicate that using economic factors together with firm-specific characteristics helps protect investors against market risk.

This article proposes a new nonparametric test for conditional independence that can directly be applied to test for Granger causality. Based on the comparison of copula densities, the test is easy to implement because it does not involve a weighting function in the test statistic, and it can be applied in general settings since there is no restriction on the dimension of the time series data. In fact, to apply the test, only a bandwidth is needed for the nonparametric copula. We prove that the test statistic is asymptotically pivotal under the null hypothesis, establishes local power properties, and motivates the validity of the bootstrap technique that we use in finite sample settings. A simulation study illustrates the size and power properties of the test. We illustrate the practical relevance of our test by considering two empirical applications where we examine the Granger noncausality between financial variables. In a first application and contrary to the general findings in the literature, we prov...

We use a Fourier transform to derive multivariate conditional and unconditional moments of multi-horizon returns under a regime-switching model. These moments are applied to examine the relevance of risk horizon and regimes for buy-and-hold investors. We analyze the impact of time-varying expected returns and risk (variance and covariance) on portfolio allocations' “term structure”—portfolio allocations as a function of the investment horizon. Using monthly observations on S&P composite index and 10-year Government Bond, we find that the term structure of the optimal allocations depends on market conditions measured by the probability of being in bull state. At short horizons and when this probability is low, buy-and-hold investors decrease their holdings of risky assets. We also find that the conditional optimal portfolio performs quite well at short and intermediate horizons and less at long horizons.

The concept of causality introduced by Wiener (1956) and Granger (1969) is defined in terms of predictability one period ahead. This concept can be generalized by considering causality at a given horizon h, and causality up to any given horizon h [Dufour and Renault (1998)]. This generalization is motivated by the fact that, in the presence of an auxiliary variable vector Z, it is possible that a variable Y does not cause variable X at horizon 1, but causes it at horizon h > 1. In this case, there is an indirect causality transmitted by Z. Another related problem consists in measuring the importance of causality between two variables. Existing causality measures have been defined only for the horizon 1 and fail to capture indirect causal effects. This paper proposes a generalization of such measures for any horizon h. We propose nonparametric and parametric measures of unidirectional and instantaneous causality at any horizon h. Parametric measures are defined in the context of autoregressive processes of unknown order and expressed in terms of impulse response coefficients. On noting that causality measures typically involve complex functions of model parameters in VAR and VARMA models, we propose a simple method to evaluate these measures which is based on the simulation of a large sample from the process of interest. We also describe asymptotically valid nonparametric confidence intervals, using a bootstrap technique. Finally, the proposed measures are applied to study causality relations at different horizons between macroeconomic, monetary and financial variables in the U.S. These results show that there is a strong effect of nonborrowed reserves on federal funds rate one month ahead, the effect of real gross domestic product on federal funds rate is economically important for the first three months, the effect of federal funds rate on gross domestic product deflator is economically weak one month ahead, and finally federal fundsrate causes the real gross domestic product until 16 months.

It is well known that the use of Gaussian models to assess financial risk leads to an underestimation of risk. The reason is because these models are unable to capture some important facts such as heavy tails and volatility clustering which indicate the presence of large fluctuations in returns. An alternative way is to use regime-switching models, the latter are able to capture the previous facts. Using regime-switching model, we propose an analytical approximation for multi-horizon conditional Value-at-Risk and a closed-form solution for conditional Expected Shortfall. By comparing the Value-at-Risks and Expected Shortfalls calculated analytically and using simulations, we find that the both approaches lead to almost the same result. Further, the analytical approach is less time and computer intensive compared to simulations, which are typically used in risk management.

In this paper, we provide evidence on two alternative mechanisms of interaction between returns and volatilities: the leverage effect and the volatility feedback effect. We stress the importance of distinguishing between realized volatility and implied volatility, and find that implied volatilities are essential for assessing the volatility feedback effect. The leverage hypothesis asserts that return shocks lead to changes in conditional volatility, while the volatility feedback effect theory assumes that return shocks can be caused by changes in conditional volatility through a time-varying risk premium. On observing that a central difference between these alternative explanations lies in the direction of causality, we consider vector autoregressive models of returns and realized volatility and we measure these effects along with the time lags involved through short-run and long-run causality measures proposed in Dufour and Taamouti (2010), as opposed to simple correlations. We analyze 5-minute observations on S&P 500 Index futures contracts, the associated realized volatilities (before and after filtering jumps through the bispectrum) and implied volatilities. Using only returns and realized volatility, we find a strong dynamic leverage effect over the first three days. The volatility feedback effect appears to be negligible at all horizons. By contrast, when implied volatility is considered, a volatility feedback becomes apparent, whereas the leverage effect is almost the same. These results can be explained by the fact that volatility feedback effect works through implied volatility which contains important information on future volatility, through its nonlinear relation with option prices which are themselves forward-looking. In addition, we study the dynamic impact of news on returns and volatility. First, to detect possible dynamic asymmetry, we separate good from bad return news and find a much stronger impact of bad return news (as opposed to good return news) on volatility. Second, we introduce a concept of news based on the difference between implied and realized volatilities (the variance risk premium) and we find that a positive variance risk premium (an anticipated increase in variance) has more impact on returns than a negative variance risk premium.

We empirically investigate the link between monetary policy measures and stock market prices. We document the following stylized facts about stock market’s reaction to money supply and examine the effect across the entire distribution of stock returns. Using a nonparametric Granger causality in mean test, we find that money supply has no impact on stock prices, which confirms many of the existing results that were based on linear mean regression. By contrast, when a nonparametric causality in distribution (hereafter general Granger causality) test and quantile regression based test were used, the effect of money becomes apparent and statistically very significant. Interestingly, money supply affects the left and right tails of stock return distribution but not its center. This might indicate that the monetary policy measure money supply is effective only during recessions and expansions. We have also investigated the extent to which the impact of money supply on stock returns detected by the nonparametric and quantile regression based tests can be attributed to a time-varying conditional variance of stock returns. After controlling for volatility persistence in stock returns, we continue to find evidence for the reaction of conditional distribution of stock market returns to money supply growth rate.

The concept of causality is naturally defined in terms of conditional distribution, however almost all the empirical works focus on causality in mean. This paper aim to propose a nonparametric statistic to test the conditional independence and Granger non-causality between two variables conditionally on another one. The test statistic is based on the comparison of conditional distribution functions using an L2 metric. We use Nadaraya-Watson method to estimate the conditional distribution functions. We establish the asymptotic size and power properties of the test statistic and we motivate the validity of the local bootstrap. Further, we ran a simulation experiment to investigate the finite sample properties of the test and we illustrate its practical relevance by examining the Granger non-causality between S&P 500 Index returns and VIX volatility index. Contrary to the conventional t-test, which is based on a linear mean-regression model, we find that VIX index predicts excess returns both at short and long horizons.

We present empirical evidence on whether the introduction of the euro has changed the effect of economic fundamentals on the growth rates of euro countries' GDPpc and GDPpc volatility. We find that there is a statistically sig- nificant structural break in the impact of increments in government debt on both economic growth and uncertainty. In particular, after adoption of the euro incre- ments in government debt decreased growth and increased uncertainty. These results are robust to a battery of checks, including exclusion of the recent finan- cial crisis period, comparison with non-euro European countries, and controlling for different debt/GDP ratios.

The reaction of EU bond and equity market volatilities to sovereign rating announcements (Standard & Poor’s, Moody’s, and Fitch) is investigated using a panel of daily stock market and sovereign bond returns. The parametric volatilities are defined using EGARCH specifications. The estimation results show that upgrades do not have significant effects on volatility, but downgrades increase stock and bond market volatility. Contagion is present, and sovereign rating announcements create interdependence among European financial markets with upgrades (downgrades) in one country leading to a decrease (increase) in volatility in other countries. The empirical results show also a financial gain and risk (valueat- risk) reduction for portfolio returns when taking into account sovereign credit ratings’ information for volatility modelling, with financial gains decreasing with higher risk aversion. info:eu-repo/semantics/publishedVersion

Theoretical risk factors underlying time-variations of risk premium across asset classes are typically unobservable or hard to measure by construction. Important examples include risk factors in Long Run Risk [LRR] structural models (Bansal and Yaron 2004) as well as stochastic volatility or jump intensities in reduced-form affine representations of stock returns (Duffie, Pan, and Singleton 2000). Still, we show that both classes of models predict that the term structure of risk-neutral variance should reveal these risk factors. Empirically, we use model-free measures and construct the ex-ante variance term structure from option prices. This reveals (spans) two risk factors that predict the bond premium and the equity premium, jointly. Moreover, we find that the same risk factors also predict the variance premium. This important contribution is consistent with theory and confirms that a small number of factors underlies common time-variations in the bond premium, the equity premium and the variance premium. Theory predicts that the term structure of higher-order risks can reveal the same factors. This is confirmed in the data. Strikingly, combining the information from the variance, skewness and kurtosis term structure can be summarized by two risk factors and yields similar level of predictability (i.e., R2s). This bodes well for our ability to bridge the gap between the macro-finance literature, which uses very few state variables, and valuations in option markets.

The reaction of EU bond and equity market volatilities to sovereign rating announcements (Standard & Poor’s, Moody’s, and Fitch) is investigated using a panel of daily stock market and sovereign bond returns. The parametric volatilities are filtered using EGARCH specifications. The estimation results show that upgrades do not have significant effects on volatility, but downgrades increase stock and bond market volatility. Contagion is present, with sovereign rating announcements creating interdependence among European financial markets with upgrades (downgrades) in one country leading to a decrease (increase) in volatility in other countries. The empirical results show also a financial gain and risk (value-at-risk) reduction for portfolio returns when taking into account sovereign credit ratings’ information for volatility modelling, with financial gains decreasing with higher risk aversion.

Recent research has identified several industry-related patterns that standard asset pricing models cannot explain effectively. This paper investigates whether industry commodity dependence affects the cross section of stock returns, using the case of oil industry. The results show that in the period 1988-2012, a value (equally) weighted portfolio of high oil loading stocks outperforms a portfolio of low oil loading stocks by 9.45% (9.18%) in average annually. Using the Fama and French asset pricing model extended with an oil factor, we find that oil price risk is priced supporting that the investors price the risk of commodity dependence. Other factors such as size and momentum are also priced. Results suggest that investors price industry specific risks and therefore a different asset pricing model should be used.

We consider impulse response functions to study the impact of both return and volatility on correlation between international equity markets. Using data on US (as the reference country), Canada, UK and France equity indices, empirical evidence shows that without taking into account the effect of return, there is an (asymmetric) effect of volatility on correlation. The volatility seems to have an impact on correlation especially during downturn periods. However, once we introduce the effect of return, the impact of volatility on correlation disappears. These observations suggest that, the relation between volatility and correlation is an association rather than a causality. The strong increase in the correlation is driven by the past of the return and the market direction rather than the volatility.

How the correlation between equity returns behaves during market turmoils has been an issue of discussion in the international finance literature. Some research suggest an increase of correlation during volatile periods [Ang and Bekaert, 2002], while others argue its stability [Forbes and Rigobon, 2002]. In this paper, we study the impact of returns and volatility on correlation between international equity markets. Our objective is to determine if there is any asymmetry in correlation and identify the main explanation for this asymmetry. Within a framework of autoregressive models we quantify the relationship between return, volatility, and correlation using the generalized impulse response function and we test for the asymmetries in the return-correlation and volatility-correlation relationships. We also examine the implications of these asymmetric effects for the optimal international portfolio. Empirical evidence using weekly data on US, Canada, UK, and France equity indices, show that without taking into account the effect of return, there is an asymmetric impact of volatility on correlation. The volatility seems to have more impact on correlation during market upturn periods than during downturn periods. However, once we introduce the effect of return, the asymmetric impact of volatility on correlation disappears. These observations suggest that, the relation between volatility and correlation is an association rather than a causality. The strong increase in the correlation is driven by the market direction and the level of return rather than the level of the volatility. These results are confirmed using some tests of the asymmetry in volatility-correlation and return-correlation relationships in separate models and then in a joint model. Finally, we find that taking into account the asymmetric effect of return on correlation leads to an average financial gain ranged between 3.35 and 37.25 basis points for optimal international diversification.

Copulas are extensively used for dependence modeling. In many cases the data does not reveal how the dependence can be modeled using a particular parametric copula. Nonparametric copulas do not share this problem since they are entirely data based. This paper proposes nonparametric estimation of the density copula for α-mixing data using Bernstein polynomials. We focus only on the dependence structure between stochastic processes, captured by the copula density defined on the unit cube, and not the complete distribution. We study the asymptotic properties of the Bernstein density copula, i.e., we provide the exact asymptotic bias and variance, we establish the uniform strong consistency and the asymptotic normality. An empirical application is considered to illustrate the dependence structure among international stock markets (US and Canada) using the Bernstein density copula estimator.