The dynamics of Central European equity market comovements

This paper examines short-term and long-term comovements between developed European Union (EU) stock markets and those of three Central European (CE) countries which recently joined the EU. Dynamic cointegration and principal components methods are applied, in addition to static tests. While we find no evidence of cointegration for the period July 1995–February 2005 as a whole, dynamic tests reveal alternating period of cointegration disrupted by episodes dominated by short-term domestic factors. Principal components analysis reveals that a stable factor explains a large proportion of return variances. Ultimately, despite the decade-long process of alignment by CE countries with the EU, evidence of steadily increasing convergence of equity markets is lacking.     

Interrelations in market fears of U.S. and European equity markets

We examine interdependence between the implied volatilities of U.S. and five European markets in an integrated multivariate system that allows interactions in the first and second moments of volatility processes. Our results find significant interactions in the variance-covariance matrix of VIX and European volatilities which persist and facilitate risk transmission. Changes in U.S. and Eurozone volatilities are important drivers of risk shocks in European markets. VIX and European volatilities have predictive ability for each other. Further, VIX shocks contribute significantly to the prediction error of European risk shocks, but not vice versa. Risk transmission from U.K. markets to U.S. and European markets intensified around the Brexit vote. Also, VIX shocks added significantly more to European risks during the global financial crisis. Our results highlight the potential weakness of risk transmission models that ignore the second-moment risk transmission channel and have implications for volatility trades, portfolio diversification strategies, and hedging the cross-market risks.     

Linkages between emerging and developed equity markets: Empirical evidence in the PMG framework

This paper empirically investigates the international equity market causal links between Central and South-Eastern Europe, on the one hand, and developed countries (Western Europe and the United States), on the other hand, over the monthly sample period spanning from October 2000 to September 2012. Unlike previous studies, we use the pooled mean group (PMG) approach of Pesaran et al. (1999), which is suitable to estimate dynamic heterogeneous panels to draw reliable conclusions. After cointegration is found between the stock markets of interest, the PMG estimates show evidence of each market's sensitivity to the fluctuations of the other markets over both the short- and long-run, supporting the feedback hypothesis. The impact of developed markets on emerging markets is more important than that of emerging markets on developed markets and the bidirectional impact is higher between emerging and Western European markets than between these emerging markets and the United States. These conclusions are robust to an alternative specification, which supports the view that the stock markets are closely interlinked. The findings are of great interest and have important implications for policy makers, investors, and practitioners.     

The structure of dynamic correlations in multivariate stochastic volatility models

This paper proposes two types of stochastic correlation structures for Multivariate Stochastic Volatility (MSV) models, namely the constant correlation (CC) MSV and dynamic correlation (DC) MSV models, from which the stochastic covariance structures can easily be obtained. Both structures can be used for purposes of determining optimal portfolio and risk management strategies through the use of correlation matrices, and for calculating Value-at-Risk (VaR) forecasts and optimal capital charges under the Basel Accord through the use of covariance matrices. A technique is developed to estimate the DC MSV model using the Markov Chain Monte Carlo (MCMC) procedure, and simulated data show that the estimation method works well. Various multivariate conditional volatility and MSV models are compared via simulation, including an evaluation of alternative VaR estimators. The DC MSV model is also estimated using three sets of empirical data, namely Nikkei 225 Index, Hang Seng Index and Straits Times Index returns, and significant dynamic correlations are found. The Dynamic Conditional Correlation (DCC) model is also estimated, and is found to be far less sensitive to the covariation in the shocks to the indexes. The correlation process for the DCC model also appears to have a unit root, and hence constant conditional correlations in the long run. In contrast, the estimates arising from the DC MSV model indicate that the dynamic correlation process is stationary.     

Implied volatility and the risk-free rate of return in options markets

We numerically solve systems of Black–Scholes formulas for implied volatility and implied risk-free rate of return. After using a seemingly unrelated regressions (SUR) model to obtain point estimates for implied volatility and implied risk-free rate, the options are re-priced using these parameters. After repricing, the difference between the market price and model price is increasing in time to expiration, while the effect of moneyness and the bid-ask spread are ambiguous. Our varying risk-free rate model yields Black–Scholes prices closer to market prices than the fixed risk-free rate model. In addition, our model is better for predicting future evolutions in model-free implied volatility as measured by the VIX.     

Technology ladders and R&D in dynamic Cournot markets

Motivated by dramatic and unpredictable technological advances in energy production (for instance drilling and extraction of shale oil), we extend Cournot models of competition to incorporate research and development (R&D) that can lead to (stochastic) drops in production costs. Our model combines features of patent racing with dynamic market structure, capturing the interplay between the immediate competition in terms of production rates and the long-term competition in R&D. The resulting Markov Nash equilibrium is found from a sequence of one-step static games arising between R&D successes, and several numerical examples and extensive analysis of the emerging comparative statics are presented. Analyzing the relationship between current market dominance and the level of R&D investments, we find that market leaders tend to invest more, which in some sense makes oligopoly dynamically unstable. We show that anticipated market transitions have long-term impact; for example the potential of future monopoly can spur R&D investment now, even if the firm is presently uncompetitive and not actively producing. We also show that, surprisingly, random innovations have an ambiguous effect on R&D. This feature, which is driven by the Cournot framework, contrasts with the common situation whereby uncertainty lowers innovation and delays R&D investments. Finally, we demonstrate that increased competition may actually increase efforts to innovate through higher desire to achieve dominance. This would match the anecdotal evidence that the threat of market entrants forces incumbents to maintain high R&D.     

Corporate credit risk prediction under stochastic volatility and jumps

This paper examines the impact of allowing for stochastic volatility and jumps (SVJ) in a structural model on corporate credit risk prediction. The results from a simulation study verify the better performance of the SVJ model compared with the commonly used Merton model, and three sources are provided to explain the superiority. The empirical analysis on two real samples further ascertains the importance of recognizing the stochastic volatility and jumps by showing that the SVJ model decreases bias in spread prediction from the Merton model, and better explains the time variation in actual CDS spreads. The improvements are found particularly apparent in small firms or when the market is turbulent such as the recent financial crisis.     

The dynamic connectedness and hedging opportunities of implied and realized volatility: Evidence from clean energy ETFs

This paper aims to examine dynamic connectedness and hedging opportunities between the realized volatilities of clean energy ETFs and energy implied volatilities through Time-Varying Parameter Vector Autoregression Model (TVP-VAR) and Asymmetric Dynamic Conditional Correlation (ADCC) GARCH models. TVP-VAR analysis results show that dynamic connectedness increases during turbulence periods. We also determine that clean energy ETFs such as PBW, QCLN, SMOG, and TAN are net volatility transmitters. Surprisingly, OVX is a net volatility receiver, especially with the developments after the Paris Agreement in 2016.As a result of the ADCC GARCH analysis, we determine that the conditional correlation between clean energy ETFs and implied volatility ETFs is asymmetric, and negative information shocks increase the conditional correlation. Although OVX is a cheap alternative for hedging long position risks in clean energy ETFs, VXXLE is more effective than OVX in terms of hedging effectiveness. These findings provide insight for individual and institutional investors, and portfolio managers on how negative and positive shocks change the conditional correlation between assets at different levels.     

Time-varying price shock transmission and volatility spillover in foreign exchange,bond, equity,and commodity markets: Evidence from the United States

We study the cross-market financial shocks transmission mechanism on the foreign exchange, equity, bond, and commodity markets in the United States using a time-varying structural vector autoregression model with stochastic volatility (TV-SVAR-SV). The price shocks are absorbed immediately in two or three days, suggesting that all markets are quite efficient. A slight mean reversion and an overshooting behavior are observed. Considering the volatility spillover effect, we highlight two properties of volatility shocks. First, the effects of the volatility shocks are released gradually. Reaching peak volatility spillover levels would require five to ten days. Second, the dynamics of volatility spillovers vary tremendously over time. Different types of markets respond to certain, but not all, extreme events. Our findings suggest the need to conduct investor monitoring of current events instead of using technical analysis based on historical data. Investors should also diversify their portfolios using assets that can respond to different and extreme shocks.     

Cross-sectional implications of dynamic asset pricing with stochastic volatility and ambiguity aversion

Building on recent research that highlights the importance of macroeconomic volatility and ambiguity aversion in explaining the dynamics of stock returns, in this paper we propose a dynamic asset pricing model that simultaneously accounts for stochastic macroeconomic volatility and ambiguity, assuming that investors deal with uncertainty about the mechanics of macroeconomic fluctuations using first-release consumption and revisions to aggregate consumption on vintage data. Our results show that the proposed model captures a large fraction of the cross-sectional variation of excess returns for a wide range of market anomaly portfolios. Furthermore, while the price of risk for ambiguity is positive and significant for the vast majority of assets under study, macroeconomic volatility yields ambiguous outcomes, although it significantly increases the explanatory power of the model for specific assets. Our results suggest that macroeconomic volatility and ambiguity complement each other in explaining the cross-sectional behavior of stock returns.     

Oil price uncertainty and movements in the US government bond risk premia

In this paper, we analyze the predictability of the movements of bond premia of US Treasury due to oil price uncertainty over the monthly period 1953:06 to 2016:12. For our purpose, we use a higher order nonparametric causality-in-quantiles framework, which in turn, allows us to test for predictability over the entire conditional distribution of not only bond returns, but also its volatility, by controlling for misspecification due to uncaptured nonlinearity and structural breaks, which we show to exist in our data. We find that oil uncertainty not only predicts (increases) US bond returns, but also its volatility, with the effect on the latter being stronger. In addition, oil uncertainty tends to have a stronger impact on the shortest and longest maturities (2- and 5-year), and relatively weaker impact on bonds with medium-term (3- and 4-year) maturities. Our results are robust to alternative measures of oil market uncertainty and bond market volatility.     

Time-varying risk aversion and renminbi exchange rate volatility: Evidence from CARR-MIDAS model

In this paper, we investigate the relation between time-varying risk aversion and renminbi exchange rate volatility using the conditional autoregressive range-mixed-data sampling (CARR-MIDAS) model. The CARR-MIDAS model is a range-based volatility model, which exploits intraday information regarding the intraday trajectory of the price. Moreover, the model features a MIDAS structure allowing for time-varying risk aversion to drive the long-run volatility dynamics. Our empirical results show that time-varying risk aversion has a significantly negative effect on the long-run volatility of renminbi exchange rate. Moreover, we observe that both intraday ranges and time-varying risk aversion contain important information for forecasting renminbi exchange rate volatility. The range-based CARR-MIDAS model incorporating time-varying risk aversion provides more accurate out-of-sample forecasts of renminbi exchange rate volatility compared to a variety of competing models, including the return-based GARCH, GARCH-MIDAS and GARCH-MIDAS incorporating time-varying risk aversion as well as range-based CARR, CARR-MIDAS and heterogeneous autoregressive (HAR), for forecast horizons of 1 day up to 3 months. This result is robust to alternative risk aversion measure, alternative MIDAS lags as well as alternative out-of-sample periods. Overall, our findings highlight the value of incorporating intraday information and time-varying risk aversion for forecasting the renminbi exchange rate volatility.     

Corporate bond market interdependence: Credit spread correlation between and within U.S. and Canadian corporate bond markets

This study investigates the correlation and interdependence between and within the U.S. and Canadian corporate bond markets. The empirical framework adopted allows credit spreads to depend on common systematic risk factors derived from structural models and incorporates dynamic conditional correlations (DCC) between spreads. Results show that there is a surprisingly weak correlation between the two markets in normal times. However, during crises, there is a sudden and strong increase in the correlation between U.S. and Canadian credit spreads. The analysis of credit spread correlation within each market also shows an unusual increase in credit spread correlations between sectors and between risk classes in the U.S. during the 2007–2009 global financial crisis. This increase persists over the post-crisis period. By contrast, in Canada, credit spread correlations between sectors remain remarkably stable over time, suggesting an interdependence of credit spreads within the Canadian market.     

Modeling dynamic conditional correlations with leverage effects and volatility spillover effects: Evidence from the Chinese and US stock markets affected by the recent trade friction

In this article, we investigate the dynamic conditional correlations (DCCs) with leverage effects and volatility spillover effects that consider time difference and long memory of returns, between the Chinese and US stock markets, in the Sino-US trade friction and previous stable periods. The widespread belief that the developed markets dominate the emerging markets in stock market interactions is challenged by our findings that both the mean and volatility spillovers are bidirectional. We do find that most of the shocks to these DCCs between the two stock markets are symmetric, and all the symmetric shocks to these DCCs are highly persistent between Shanghai’s trading return and S&P 500′s trading or overnight return, however all the shocks to these DCCs are short-lived between S&P 500′s trading return and Shanghai’s trading or overnight return. We also find clear evidence that the DCC between Shanghai’s trading return and S&P 500′s overnight return has a downward trend with a structural break, perhaps due to the “America First” policy, after which it rebounds and fluctuates sharply in the middle and later periods of trade friction. These findings have important implications for investors to pursue profits.     

Common shocks,common transmission mechanisms and time-varying connectedness among Dow Jones Islamic stock market indices and global risk factors

The purpose of this paper is twofold. First, we examine the importance of permanent versus transitory shocks as well as their domestic and foreign components in explaining the business cycle fluctuations of seven Dow Jones Islamic stock markets (DJIM), namely U.S., U.K., Canada, Europe, Asia-Pacific, Japan and GCC, over the period from April 2003 to November 2018, using the permanent-transitory (P-T) decompositions approach of Centoni et al. (2007). Second, we investigate the spillover mechanisms of these shocks across Islamic stock markets and a set of global risk factors, using the Diebold and Yilmaz (DY) (2012) approach. The P-T decomposition results show that the DJIM U.S., U.K., Europe and GCC indices are sensitive to both domestic and foreign shocks, while the DJIM Canada, Japan and Asia-Pacific are most sensitive to domestic shocks. The empirical results of the DY approach indicate that: (i) the return and volatility spillover intensity increase during financial turmoil, supporting evidence of the contagion phenomenon, (ii) the DJIM U.S. is the main transmitter of return and volatility spillovers, while the DJIM GCC is identified as the main receiver of both return and volatility spillovers, (iii) the seven Dow Jones Islamic stock indices are weakly linked to movements of global risk factors, and (iv) there is evidence of possible portfolio diversification between the selected Islamic stock markets and the oil commodity market.     

Asymmetry in the jump-size distribution of the S&P 500: Evidence from equity and option markets

This paper studies alternative distributions for the size of price jumps in the S&P 500 index. We introduce a range of new jump-diffusion models and extend popular double-jump specifications that have become ubiquitous in the finance literature. The dynamic properties of these models are tested on both a long time series of S&P 500 returns and a large sample of European vanilla option prices. We discuss the in- and out-of-sample option pricing performance and provide detailed evidence of jump risk premia. Models with double-gamma jump size distributions are found to outperform benchmark models with normally distributed jump sizes.     

The costs of suboptimal dynamic asset allocation: General results and applications to interest rate risk, stock volatility risk, and growth/value tilts

The recent theoretical asset allocation literature has derived optimal dynamic investment strategies in various advanced models of asset returns. But how sensitive is investor welfare to deviations from the theoretically optimal strategy? Will unsophisticated investors do almost as well as sophisticated investors? This paper develops a general theoretical framework for answering such questions and applies it to three specific models of interest rate risk, stochastic stock volatility, and mean reversion and growth/value tilts of stock portfolios. Among other things, we find that growth/value tilts are highly valuable, but the hedging of time-varying stock risk premia is less important.     

Credit risk of subsidiaries of foreign banks in CEE countries: Impacts of the parent bank and home country economic environment

In this study, using dynamic panel data, we investigated the influences of the home country economic environment and parent bank condition on the credit risk of foreign banks in Central and Eastern European (CEE) countries. We concentrated on the international transmission of credit risk through the internal capital market of multinational banks. Our theoretical assumptions follow studies that document how the parent bank condition and home country macroeconomic environment affect lending in subsidiaries in CEE countries. However, our results go one step further. We provide evidence that these relationships are reflected in subsidiaries’ credit risk in CEE countries. Our results suggest that the size and profitability of the parent bank have negative influences, while the liquidity and credit risk of the parent bank have positive influences on the subsidiaries’ credit risk. Moreover, the GDP growth in the parent bank’s country has a negative effect on the credit risk of the subsidiary, while the lending rate and liquidity in the parent bank country cause growth in the credit risk. These results indicate a new channel of international risk transfer from parent bank countries to host countries through foreign-owned banks.     

Granger causality in risk and detection of extreme risk spillover between financial markets

Controlling and monitoring extreme downside market risk are important for financial risk management and portfolio/investment diversification. In this paper, we introduce a new concept of Granger causality in risk and propose a class of kernel-based tests to detect extreme downside risk spillover between financial markets, where risk is measured by the left tail of the distribution or equivalently by the Value at Risk (VaR). The proposed tests have a convenient asymptotic standard normal distribution under the null hypothesis of no Granger causality in risk. They check a large number of lags and thus can detect risk spillover that occurs with a time lag or that has weak spillover at each lag but carries over a very long distributional lag. Usually, tests using a large number of lags may have low power against alternatives of practical importance, due to the loss of a large number of degrees of freedom. Such power loss is fortunately alleviated for our tests because our kernel approach naturally discounts higher order lags, which is consistent with the stylized fact that today’s financial markets are often more influenced by the recent events than the remote past events. A simulation study shows that the proposed tests have reasonable size and power against a variety of empirically plausible alternatives in finite samples, including the spillover from the dynamics in mean, variance, skewness and kurtosis respectively. In particular, nonuniform weighting delivers better power than uniform weighting and a Granger-type regression procedure. The proposed tests are useful in investigating large comovements between financial markets such as financial contagions. An application to the Eurodollar and Japanese Yen highlights the merits of our approach.