Comovement Structure Research Articles

Understanding co-movements based on heterogeneous information associations

Both systematic and idiosyncratic information dissemination contribute to assets co-movement. To proxy for co-movement based on heterogeneous information diffusion, we construct two-layer network structures in terms of the correlations of systematic and idiosyncratic returns for Fama–French 49 industry portfolios. We further delve into the co-movement structures by studying their dynamics and interactions in terms of topological properties at both the system and individual levels. Our findings reveal the following: (1) Co-movement structures exhibit temporal changes and are closely associated with major risk events. (2) At the system level, there is time-varying mutual Granger causality between co-movement structures, particularly during or after recession periods. (3) At the individual level, we identify statistically significant but economically insignificant interplay between the two co-movement structures. Moreover, the co-movement structure based on systematic information diffusion is mainly influenced by industry-level characteristics, including idiosyncratic momentum, idiosyncratic volatility, and market beta. These results deepen our understanding of asset co-movement structures and bear significance for asset allocation and risk management practices.

Co-movement between dirty and clean energy: A time-frequency perspective

In the backdrop of the recent covid-19 pandemic there is a renewed interest to understand the interlinkages between dirty and clean energies. In this regard, the study examines the co-movement structure between clean energy stocks and dirty energies before and during the covid-19 outbreak. The study analyses the interlinkages between the underlying markets by utilizing a vast sample of dirty energies namely crude oil, heating oil, gas oil, gasoline and natural gas, whereas clean energy sector is proxied by S&P Global clean energy index and Wilder Hill clean energy index. We make use of rolling window wavelet approach and wavelet coherence analysis to identify interdependencies between the clean energy stocks and dirty energies. The results exhibit weak linkages between clean energy equities and dirty energies in the short-run, while; we also record few occasions of high co-movements among dirty and clean energy markets in the long-run. Noticeably, a distinct decoupling effect persisted between dirty and clean energy markets. In addition, the findings also illustrate that clean energy market is relatively isolated from dirty energies during the recent pandemic crisis, amplifying portfolio diversification benefits across clean and dirty energy markets. The findings of the study hold meaningful insights for investors, policy makers and other market participants in energy financial markets.

On the higher-order moment interdependence of stock and commodity markets: A wavelet coherence analysis

This study aims to provide fresh time-frequency evidence on global equity and commodity market interdependencies, in terms of the first four moments of their respective return distributions (i.e., mean, variance, skewness, and kurtosis), using wavelet analysis techniques. The results indicate that the co-movement structure of the same-order moments of equity, gold, and energy returns tends to be both time-dependent and frequency-dependent. With respect to the equity-gold nexus, we detect substantial cross-volatility, cross-skewness, and cross-kurtosis effects at medium- and long-term time scales during the 2010–2014 turmoil, with the stock market leading the gold counterpart in most cases. From 2015 onwards, we observe a strong degree of coherence at high and medium frequencies for each of the four same-order moment pairs, but generally with zero-phase difference. The findings for the equity-energy nexus suggest significant mean and volatility co-movement over most of the sample period and across frequency bands. Additionally, cross-skewness and cross-kurtosis linkages are more pronounced at short- and medium-term horizons and, noticeably, in the wake of global downturns. Practical implications are deduced from the analysis.

A spatial panel quantile model with unobserved heterogeneity

This paper introduces a spatial panel quantile model with unobserved heterogeneity. The proposed model is capable of capturing high-dimensional cross-sectional dependence and allows heterogeneous regression coefficients. For estimating model parameters, a new estimation procedure is proposed. When both the time and cross-sectional dimensions of the panel go to infinity, the uniform consistency and the asymptotic normality of the estimated parameters are established. In order to determine the dimension of the interactive fixed effects, we propose a new information criterion. It is shown that the criterion asymptotically selects the true dimension. Monte Carlo simulations document the satisfactory performance of the proposed method. Finally, the method is applied to study the quantile co-movement structure of the U.S. stock market by taking into account the input–output linkages as firms are connected through the input–output production network.

A network perspective of comovement and structural change: Evidence from the Chinese stock market

How to appropriately characterize the comovement between any pair of individual stocks and describe the market comovement structure is a great challenge and plays a key role in understanding emerging markets. This paper applies the complex network approach to deal with this issue for the Chinese stock market. Firstly, in view of the topological properties, we investigate the time-varying comovement between individual stocks by constructing 14 directed weighted stock networks. Furthermore, the weighted LeaderRank algorithm is employed to describe the comovement structure of the entire market. Most importantly, from the perspective of fundamental factors and industry factors, we reveal the driving factors of the comovement and structural change of the entire market. The empirical results suggest that: (i) Stocks with higher weighted LeaderRank algorithm scores generally have more long-term investment value; and the so-called views, “too big to fail” and “too connected to fail”, are further confirmed. (ii) ROE, BMratio and Growth are significantly positively correlated with the comovement between individual stocks, and Mvalue is significantly negatively correlated during normal periods. However, during the crisis, the signs of regression coefficients of above four explanatory variables are reversed. (iii) In normal periods, we only find that the agriculture, forestry, animal husbandry & fishery and composite have significant influence on the comovement structure of the entire market. Besides, public utilities and medias also have a significant impact during the crisis. In addition, a very interesting fact in point is that network density, average clustering coefficient, and global efficiency can provide an “early warning” for possible upcoming crises.

Measuring Complexity in Financial Data

Stock market is a canonical example of complex systems, where a large number of interacting agents lead to joint evolution of stock returns and the collective market behavior exhibits emergent properties. However, quantifying complexity in the stock market data has been a challenging task. In this report, we explore four different measures to characterize the intrinsic complexity by evaluating structural relationship among stock returns. The first two measures are based on linear and non-linear comovement structure (accounting for contemporaneous and Granger-causal relationships), the third one is based on algorithmic complexity and the fourth one is based on spectral analysis of interacting dynamical systems. In our dataset comprising daily prices of a large number of stocks in the complete historical data of NASDAQ (1972- 2018), we see that the third and fourth measures are able to identify the largest global economic downturn in 2007-09 and associated spillovers, substantially more accurately than the first two measures. Finally, we conclude this brief research report with discussions on implications of such quantification for risk management in complex systems.

Copula-Based Assessment of Co-Movement and Tail Dependence Structure Among Major Trading Foreign Currencies in Ghana

This paper examines the joint movement and tail dependence structure between the pair of foreign exchange rates (EUR, USD and GBP) against the GHS, using daily exchange rates data expressed in GHS per unit of foreign currencies (EUR, USD and GBP) between the time range of 24 February 2009 and 19 December 2019. We use different sets of both static (time-invariant) and time-varying copulas with different levels of dependence and tail dependence measures, and the study results reveal positive dependence between all exchange rates pairs, though the dependencies for EUR-USD and GBP-USD pairs are not as strong as the EUR-GBP pair. The findings also reveal symmetric tail dependence, and dependence evolves over time. Notwithstanding this, the asymmetric tail dependence copulas provide evidence of upper tail dependence. We compare the copula results to DCC(1,1)-GARCH(1,1) model result and find the copula to be more sensitive to extreme co-movement between the currency pairs. The afore-mentioned findings, therefore, offer forex market players the opportunity to relax in hoarding a particular foreign currency in anticipation of domestic currency depreciation.

Dynamic Dependence between U.S. Inbound Visits and Exchange Rate

This article revisits the dynamic dependence between the U.S. international tourism demand and the exchange rate using a copula-based specification that features a time-varying and state-switching comovement structure. The empirical results find a state of high-volatile dependence during the oil price upsurge (2005M11-2006M09) and economic/financial crisis (2008M06-2011M06) and a state of low-volatile dependence during the remaining periods. During the former periods, a positive dependence between U.S. inbound visits and currency depreciation is indicated most often, and the magnitude of positive dependence varies dramatically. During the remaining periods, the positive and negative dependences coexist and interchange at a smooth pace. This finding implies that the exchange rate policy affects the tourism industry in the high-volatile dependence state but not in the low-volatile dependence state. The moderating role of crude oil price on the relationship between the international tourism demand and the exchange rate is also verified.

A Spatial Panel Quantile Model with Unobserved Heterogeneity

This paper introduces a spatial panel quantile model with unobserved heterogeneity. The proposed model is capable of capturing high-dimensional cross-sectional dependence and allows heterogeneous regression coefficients. For estimating model parameters, a new estimation procedure is proposed. When both the time and cross-sectional dimensions of the panel go to infinity, the uniform consistency and the asymptotic normality of the estimated parameters are established. In order to determine the dimension of the interactive fixed effects, we propose a new information criterion. It is shown that the criterion asymptotically selects the true dimension. Monte Carlo simulations document the satisfactory performance of the proposed method. Finally, the method is applied to study the quantile co-movement structure of the U.S. stock market by taking into account the input-output linkages as firms are connected through the input-output production network.

Time-varying comovement and changes of comovement structure in the Chinese stock market: A causal network method

The driving force for the comovement in stock returns is a long-standing debate between classical asset pricing theory and behavioral finance theory. It has become critically important recently for understanding systemic risk and risk contagion in the market. In this study, we propose complex networks enabled new methods to measure the causal comovement of individual stocks and the comovement structure of the market, which facilitate the examination of all kinds of hypotheses of comovement theories in a unified framework. Using a sample of the Chinese stock market from Jan. 1, 2006 to Dec. 31, 2016, we find that the degree of comovement generally intensifies over time, with a drastic increase from 2011 to 2015, while the comovement structure of the market changes with different market situations. Most importantly, our study reveals the driving force of causal comovement among individual stocks; that is, sentiment-based factors related to the market index indeed induce excess causal comovement in returns beyond that can be justified by fundamental factors including beta coefficient, book-to-market ratio, liquidity, profitability and volatility. Our study also reveals the determinants of comovement structure, which are attributable to the change of investors' behaviors in different periods. It turns out that investors in the Chinese stock market care about risk-return relationship in normal periods, while they seem to care only about risk in crisis periods.

The Core, Periphery, and Beyond: Stock Market Comovements among EU and Non‐EU Countries

AbstractUsing linear and nonlinear correlations, copulas, quantile dependence and lower tail dependence, we find that (1) equity markets of the advanced European Union (EU) countries comove more closely with each other than with the peripheral economies, (2) comovements with non‐EU countries are lower, (3) relative comovement structure before, during, and after the global financial crisis has been very stable, and (4) the level of comovements remained virtually the same between the crisis and post‐crisis periods. Our results are robust to controlling for Fama‐French, U.S. and global risk factors, as well as monetary policy, market interest rates, exchange rates, and uncertainty.

A network approach to unravel asset price comovement using minimal dependence structure

Abstract We develop a network representation-based methodology to aid an exploratory analysis of temporally evolving comovement in asset prices. This parsimonious order-n representation of the most significant comovement in asset prices, filtered by common factors, allows tackling a large number of assets and unraveling their complex comovement structure. Flexibility in choosing explanatory factors to suit the specific objectives of a study makes this methodology useful for portfolio analysis, risk parity approaches, and risk management decisions. We illustrate the features of the methodology for a set of major industry equity indices and to blue chip stocks, where we analyze the dynamic relevance of Fama–French factors. Investigating the network for more than 20 years, including the dot-com bust, global financial crisis, and European debt crisis, helps draw many insights. For instance, unexpected industries are seen to connect idiosyncratically through the dot-com bust. We demonstrate that a network factor model based portfolio allocation performs better than a regular factor model based allocation.

Copula-Based Market Comovements in the EU

Recent economic events across the PIIGS countries (Portugal, Ireland, Italy, Greece and Spain) may have resulted in correlated stock market returns across those countries and caused contagion to more developed European markets such as Germany or the UK, particularly during the Great Recession. Using several complementary techniques, including linear and nonlinear correlations, copulas, quantile dependence, and lower-tail dependence on data from 1999 to 2015, we find equity market comovements among the PIIGS stock markets to be weaker than between each of the PIIGS and the more advanced European economies of France, Germany, and the UK. We also find that stock markets of the advanced countries co-move more closely with each other than with any of the peripheral economies. Surprisingly, the nature of this comovement structure did not change during the global financial crisis, suggesting that this relative comovement structure is very stable even in the face of dramatic economic change. Further, we find that the level of comovements remained virtually the same as the Eurozone was transitioning from the financial crisis to the post-crisis recovery. Comovements with non-EU countries are not as strong, even with Switzerland, which suggests that the UK's impending withdrawal from the UK will result in weaker comovements of the British stock market with other European markets. Our results are largely immune to joint (marginal) distributional assumptions and do not require joint normality.

Tail dependence and information flow: Evidence from international equity markets

Bhatti and Nguyen (2012) used the copula approach to measure the tail dependence between a number of international markets. They observed that some country pairs exhibit only left-tail dependence whereas others show only right-tail. However, the flow of information from uni-dimensional (one-tail) to bi-dimensional (two-tails) between various markets was not accounted for. In this study, we address the flow of information of this nature by using the dynamic conditional correlation (DCC–GARCH) model. More specifically, we use various versions of the DCC models to explain the nexus between the information flow of international equity and to explain the stochastic forward vs. backward dynamics of financial markets based on data for a 15-year period comprising 3,782 observations. We observed that the information flow between the US and Hong Kong markets and between the US and Australian markets are bi-directional. We also observed that the DCC model captures a wider co-movement structure and inter-connectedness compared to the symmetric Joe–Clayton copula.

Statistical properties of short-selling and margin-trading activities and their impacts on returns in the Chinese stock markets

We investigate the collective behaviors of short-selling and margin-trading between Chinese stocks and their impacts on the co-movements of stock returns by cross-correlation and partial correlation analyses. We find that the collective behaviors of margin-trading are largely attributed to the index cohesive force, while those of short-selling are mainly due to some direct interactions between stocks. Interestingly, the dominant role the finance industry plays in the collective behaviors of short-selling could make it more important in affecting the co-movement structure of stock returns by strengthening its relationship with the market index. By detecting the volume–return and volume–volatility relationships, we find that the investors of the two leverage activities are positively triggered by individual stock volatility first, and next, at the return level, margin-buyers show trend-following properties, while short-sellers are probably informative traders who trade on the information impulse of specific firms. However, the return predictability of the two leverage trading activities and their impacts on stock volatility are not significant. Moreover, both tails of the cumulative distributions of the two leverage trading activities are found following the stretched exponential law better than the power-law.

Drug pricing behaviour across Central American countries

This paper studies the behaviour of drug prices over time and across Central American countries. The paper is based on the price co-movement literature, which studies whether prices of some commodities move in a synchronised way over time. Correlations for quarterly pricing information of drugs in 11 therapeutic groups in all six Central American countries are calculated. The main findings show that there are country and firm-specific effects in the co-movement of prices across Central American countries. Furthermore, it is possible to identify a regional drug price co-movement structure that integrates some countries' sub-segments and isolates others. Most price co-movement research has focused on the study of prices of different commodities within a given geographical market; this paper goes beyond the study of price co-movements within single markets – a specific country – to also study price co-movement across well-defined jurisdictional markets – the Central American countries.

(Re)correlation: A Markov Switching Multifractal Model with Time Varying Correlations

The paper develops a Markov switching multifractal model with dynamic conditional correlations. The objective is to give more flexibility to the initial bivariate Markov switching multifractal model [MSM] (Calvet et al. (2006)) by introducing some time dependency in the comovement structure. The new defined model is applied to stock index data (CAC, DAX, FTSE, NYSE) between 1996 and 2008 and compared to both the standard MSM and the DCC model of Engle and Sheppard (2002). The MSMDCC models present, in sample, better fit than the MSM and DCC models. Moreover, by combining these two setups, MSMDCC improves forecast performances for longer horizons, and provides a better understanding of market comovements during crisis episodes.

Inter-temporal Stability of the European Credit Spread Co-movement Structure

Corporate bonds expose the investor to credit risk, which will be reflected in the credit spread. Based on the EMU Broad Market indices, we study the inter-temporal stability of the covariance and correlation matrices of credit spread changes on weekly data. For a multivariate framework, the Box and Jennrich tests are the most commonly used test statistics in the literature. However, we show that for small samples these tests are not well specified when the normality assumption is relaxed. A bootstrap-based statistical inference provides evidence that correlations and covariances between various (investment grade) credit spread changes are unstable over the 1998-2003 period.

Mathematics of Derivative Securities

Foreword R. C. Merton Part I. Introduction: 1. Editors' introduction 2. Stochastic calculus and Markov methods L. C. G. Rogers 3. The risk premium in trading equilibria which support Black-Scholes option pricing S. D. Hodges and M. J. P. Selby 4. On the numeraire portfolio P. Artzner Part II. Option Pricing and Hedging: 5. Convergence of Snell envelopes and critical prices in the American Put N. J. Cutland, P. E. Kopp, W. Willinger and M. C. Wyman 6. Some combination of Asian, Parisian and Barrier options M. Yor, M. Chesnay, H. Geman and M. Jeanblanc-Pique 7. Co-movement term structure and the valuation of crack energy spread options A. Mbanefo 8. Pricing and hedging with Smiles B. Dupire 9. Filtering derivative security valuations from market prices R. J. Elliott, C. H. Lahaie and D. B. Madan 10. Option pricing in the presence of extreme fluctuations J.-P. Bouchard, D. Sornette and M. Potters 11. Hedging long maturity commodity commitments with short-dated futures contracts M. J. Brennan and N. I. Crew 12. Nonlinear financial markets: hedging and portfolio optimization J. Cvitanic 13. Semimartingales and asset pricing under constraints M. Frittelli 14. Option pricing in incomplete markets M. H. A. Davis 15. Option pricing and hedging in discrete time with transaction costs F. Mercurio and T. C. F. Vorst Part III. Term Structure and Interest Rate Derivatives: 16. Bond and bond option pricing based on the current term structure P. H. Dybvig 17. Dynamic models for yield curve evolution B. Flesaker and L. P. Hughston 18. General interest rate models and the universality of HJM M. W. Baxter 19. Swap derivatives in a Gaussian HJM framework A. Brace and M. Musiela 20. Modelling bonds and derivatives with default risk D. Lando 21. Term structure modelling under alternative official regimes S. H. Babbs and N. J. Webber 22. Interest rate distributions, yield curve modelling and monetary policy L. El-Jahel, H. Lindberg and W. Perraudin Part IV. Numerical Methods: 23. Numerical option pricing using conditioned diffusions S. K. Gandhi and P. J. Hunt 24. Numerical valuation of cross-currency swaps and swaptions M. A. H. Dempster and J. P. Hutton 25. Numerical methods for stochastic control problems in finance H. J. Kushner 26. Simulation methods for option pricing J. P. Lehoczky 27. New methodologies for valuing derivatives S. H. Paskov.

Stability and predictability of the comovement structure of returns in the american depository receipts market

Stability and predictability of the comovement structure of returns in the american depository receipts market