Unconditional Correlation Research Articles

On patience and trust

ABSTRACT We show that variation in trust is crucial to explain variation in patience. The unconditional correlation between trust and patience is 0.190 across 76 countries based on the outcomes of a survey among 80,000 individuals. Controlling for variations in country-level early agricultural development by means of heteroskedastic linear maximum likelihood regression resolves the data’s heteroscedasticity and raises the correlation to 0.345. Two-stage least squares estimation, with Genetic Distance – a descriptor of within country homogeneity – as its most valuable exogenous instrumental variable, removes endogeneity bias from the empirical relationship, and results in a robust final correlation of 0.754. Our study underlines the importance of proper econometric analysis as a prerequisite to accurately measure the extent of the relationship between patience and trust, two cultural variables that are seen as important determinants of economic development.

Unveiling the relationship between oil and green bonds: Spillover dynamics and implications

This study investigates the spillover dynamics and directionality between oil prices (OP) and green bonds (GB) using a bivariate Generalized Autoregressive Conditional Heteroskedasticity (GARCH) framework. The research aims to contribute to the understanding of the complex relationship between OP and GB and provide valuable policy insights to stakeholders. The results confirm the presence of significant spillovers between the two markets, indicating a transmission of risk and contagion. While the unconditional correlation between the returns of OP and GB is close to zero, bivariate asymmetric GARCH-BEKK estimations reveal a clear connection between the two markets. Past return shocks in the GB market are found to mitigate subsequent volatility in the OP, suggesting a level of substitution between the two markets. Volatility spillovers between the two markets are also evident, although their signs vary. This study contributes to the literature by filling a research gap and offering insights into the spillover dynamics and directionality between OP and GB. The results highlight the interconnectedness and transmission of risk between the two markets, suggesting the need to consider their relationship for investment decisions. Policymakers, investors, and other stakeholders can benefit from the insights provided in this research for effective risk management and investment strategies.

Foreign investors, rebalancing trades, and increases in U.S.-Japan stock market correlations

ABSTRACT Foreign investors’ transaction volume in the Tokyo Stock Exchange has grown steadily in the past three decades, and Japanese stocks have become a familiar instrument for international diversification. Rebalancing trades in a local national market by foreign institutional investors holding multi-country asset portfolios, often heavily represented by U.S. stocks, amplify the national market’s stock index co-movements with the U.S. This paper examines the extent to which greater participation of foreign investors is associated with increases in the unconditional correlation of daily returns between the U.S. (S&P 500) and Japan (TOPIX). A Gaussian copula marginal regression model is applied to the monthly series of unconditional correlations. We find that the growth in foreign investors’ transaction share of the Tokyo market’s turnover approximately accounts for the rise in U.S.-Japan correlations for 1990–2019. Foreign investors’ trades improve the informational efficiency of local national markets. Increased correlations lead to smaller potential gains from international diversification, however.

Connexion Between Cryptocurrencies Liquid and Futures Markets: Discernment from Static, Dynamic and Wavelet Approach

The knowledge of the interconnectedness between liquid and futures markets of cryptocurrencies amidst dynamic contemporary environment can be enriched through the full characterization of the direction, persistence and intensity of information flows between these markets. So, the present study attempts to investigate the static and dynamic connexions between liquid and futures markets of Bitcoin, Ethereum, Litecoin, Ripple XRP and Bitcoin Cash from June 2018 to June 2022. The connexion between their liquid and futures markets is first investigated using unconditional correlation, Johansen’s cointegration, vector error correction and Wald’s block exogeneity. Their estimates discern connexions encompassing significant long-run relationships between their liquid and futures markets; momentous unidirectional long-run causality from their futures market to liquid market; and momentous bidirectional short-run causality from their liquid market to futures market and from their futures market to liquid market. The present treatise encompasses methodological advancement in the investigation of interconnectedness between these markets by employing a dynamic conditional correlation model and a wavelet transform framework. Their discerned estimates indicate that the markets of Bitcoin, Litecoin, Ethereum and Bitcoin Cash have only momentous long-run perseverance, lingering and spillover effects of shocks’ sway on conditional correlations. However, there is momentous short- and long-run perseverance, lingering and spillover effects in the case of Ripple XRP. The wavelet coherence analysis also confirms these results by indicating a bidirectional short-run causal relation and a long-run positive comovement between liquid returns and futures returns of these cryptocurrencies. These discernments may help investors, portfolio managers and policymakers to enhance hedging effectiveness through optimal portfolio allocation and monitor financial contagion to attain and sustain financial stability in economies.

Invited Commentary: Bayesian Inference with Multiple Tests.

Dr. Leonhard presents a comprehensive and insightful critique of the existing malingering research literature and its implications for neuropsychological practice. Their statistical critique primarily focuses on the crucial issue of diagnostic inference when multiple tests are involved. While Leonhard effectively addresses certain misunderstandings, there are some overlooked misconceptions within the literature and a few new confusions were introduced. In order to provide a balanced commentary, this evaluation considers both Leonhard's critiques and the malingering research literature. Furthermore, a concise introduction to Bayesian diagnostic inference, utilizing the results of multiple tests, is provided. Misunderstandings regarding Bayesian inference are clarified, and a valid approach to Bayesian inference is elucidated. The assumptions underlying the simple Bayes model are thoroughly discussed, and it is demonstrated that the chained likelihood ratios method is an inappropriate application of this model due to one reason identified by Leonhard and another reason that has not been previously recognized. Leonhard's conclusions regarding the primary dependence of incremental validity on unconditional correlations and the alleged mathematical incorrectness of the simple Bayes model are refuted. Finally, potential directions for future research and practice in this field are explored and discussed.

News-based economic policy uncertainty and financial contagion: An international evidence

In this paper, we examine the relative importance of economic policy uncertainty as a potential source intensifying market integration/contagion for domestic stock markets of 31 countries. Our data spans the period June 2001-October 2021 that encompasses several episodes of economic and financial turmoil. We use the multifactor asset pricing model of Bekaert et al. (2005) with a two-factor setting which are the U.S. market return and the regional equity portfolio return to reflect the local, regional and global risk sources. Our findings show that economic policy uncertainty has an important role in intensifying spillover effects among regional European markets. Results also show an increased interdependency between used Asia Pacific and Latin American markets and the U.S. market during periods of high U.S. and global uncertainties induced by the increased portion of return shock variance explained by the U.S. factor. Considering contagion, we fail to provide evidence of additional unconditional correlation of returns with the United States and the region during these same periods of time, which suggests that the influence is rather induced by fundamentals. The implications of this study will be of interest to both practitioners and market regulators to better adopt proactive awareness regarding stock market behavior.

Is a correlation-based investment strategy beneficial for long-term international portfolio investors?

Using negative to low-correlated assets to manage short-term portfolio risk is not uncommon among investors, although the long-term benefits of this strategy remain unclear. This study examines the long-term benefits of the correlation strategy for portfolios based on the stock market in Asia, Central and Eastern Europe, the Middle East and North Africa, and Latin America from 2000 to 2016. Our strategy is as follows. We develop five portfolios based on the average unconditional correlation between domestic and foreign assets from 2000 to 2016. This yields five regional portfolios based on low to high correlations. In the presence of selected economic and financial conditions, long-term diversification gains for each regional portfolio are evaluated using a panel cointegration-based testing method. Consistent across all portfolios and regions, our key cointegration results suggest that selecting a low-correlated portfolio to maximize diversification gains does not necessarily result in long-term diversification gains. Our empirical method, which also permits the estimation of cointegrating regressions, provides the opportunity to evaluate the impact of oil prices, U.S. stock market fluctuations, and investor sentiments on regional portfolios, as well as to hedge against these fluctuations. Finally, we extend our data to cover the years 2017–2022 and find that our main findings are robust.

Have cryptocurrencies become an inflation hedge after the reopening of the U.S. economy?

In this paper, we study how the comovement between cryptocurrencies and the U.S. inflation expectation rates has changed during the post-reopening of the U.S. economy after the Covid-19 crisis. To do so, we develop a new concept of “exceedance co-kurtosis” which allows us to quantify asymmetry in strong comovement between each cryptocurrency and the inflation expectation rate. The key findings are as follows. First, we show the change in the co-kurtosis asymmetry for major cryptocurrencies: the downside co-kurtosis was higher than the upside co-kurtosis but it decreased after the reopening of the economy. Although the unconditional correlations between cryptocurrencies and the inflation expectation rates remain very low, our results indicate that the major cryptocurrencies become a slightly better inflation hedge after the reopening. Second and more interestingly, the results do not depend on whether a cryptocurrency has a cap on maximum supply or not. Therefore, treating the major cryptocurrencies as digital commodities could be misleading from the viewpoint of portfolio optimization.

Co-occurrence probabilities between mosquito vectors of West Nile and Eastern equine encephalitis viruses using Markov Random Fields (MRFcov)

Mosquito vectors of eastern equine encephalitis virus (EEEV) and West Nile virus (WNV) in the USA reside within broad multi-species assemblages that vary in spatial and temporal composition, relative abundances and vector competence. These variations impact the risk of pathogen transmission and the operational management of these species by local public health vector control districts. However, most models of mosquito vector dynamics focus on single species and do not account for co-occurrence probabilities between mosquito species pairs across environmental gradients. In this investigation, we use for the first time conditional Markov Random Fields (CRF) to evaluate spatial co-occurrence patterns between host-seeking mosquito vectors of EEEV and WNV around sampling sites in Manatee County, Florida. Specifically, we aimed to: (i) quantify correlations between mosquito vector species and other mosquito species; (ii) quantify correlations between mosquito vectors and landscape and climate variables; and (iii) investigate whether the strength of correlations between species pairs are conditional on landscape or climate variables. We hypothesized that either mosquito species pairs co-occur in patterns driven by the landscape and/or climate variables, or these vector species pairs are unconditionally dependent on each other regardless of the environmental variables. Our results indicated that landscape and bioclimatic covariates did not substantially improve the overall model performance and that the log abundances of the majority of WNV and EEEV vector species were positively dependent on other vector and non-vector mosquito species, unconditionally. Only five individual mosquito vectors were weakly dependent on environmental variables with one exception, Culiseta melanura, the primary vector for EEEV, which showed a strong correlation with woody wetland, precipitation seasonality and average temperature of driest quarter. Our analyses showed that majority of the studied mosquito species’ abundance and distribution are insignificantly better predicted by the biotic correlations than by environmental variables. Additionally, these mosquito vector species may be habitat generalists, as indicated by the unconditional correlation matrices between species pairs, which could have confounded our analysis, but also indicated that the approach could be operationalized to leverage species co-occurrences as indicators of vector abundances in unsampled areas, or under scenarios where environmental variables are not informative.Graphical

Measuring conditional correlation between financial markets' inefficiency

<abstract><p>Assuming that stock prices follow a multi-fractional Brownian motion, we estimated a time-varying Hurst exponent ($ h_t $). The Hurst value can be considered a relative volatility measure and has been recently used to estimate market inefficiency. Therefore, the Hurst exponent offers a level of comparison between theoretical and empirical market efficiency. Starting from this point of view, we adopted a multivariate conditional heteroskedastic approach for modeling inefficiency dynamics in various financial markets during the 2007 financial crisis, the COVID-19 pandemic and the Russo-Ukranian war. To empirically validate the analysis, we compared different stock markets in terms of conditional and unconditional correlations of dynamic inefficiency and investigated the predicted power of inefficiency measures through the Granger causality test.</p></abstract>

Comovement and contagion in commodity markets

This article investigates comovement and contagions in the commodities markets. We examine the comovement by analyzing the unconditional correlation coefficients. We document that commodities tend to partially integrate. We perform contagion tests by identifying coexceedances and estimating multinomial logit to explain the joint occurrence of those coexceedances. We document that commodities price changes tend to affect the probability of both positive and negative coexceedances. Overall, we conclude that there are comovement and contagions among commodities. However, the degrees of comovement and contagion are different among commodities and between positive and negative extreme returns. The contagion among commodities is asymmetric.

Large dynamic covariance matrices: Enhancements based on intraday data

Multivariate GARCH models do not perform well in large dimensions due to the so-called curse of dimensionality. The recent DCC-NL model of Engle et al. (2019) is able to overcome this curse via nonlinear shrinkage estimation of the unconditional correlation matrix. In this paper, we show how performance can be increased further by using open/high/low/close (OHLC) price data instead of simply using daily returns. A key innovation, for the improved modeling of not only dynamic variances but also of dynamic correlations, is the concept of a regularized return, obtained from a volatility proxy in conjunction with a smoothed sign of the observed return.

A unified approach for jointly estimating the business and financial cycle, and the role of financial factors

We jointly estimate the U.S. business and financial cycle through a unified empirical approach which also simultaneously quantifies the role of financial factors. Our approach uses the Beveridge-Nelson decomposition within a medium-scale Bayesian Vector Autoregression. First, we show, both in reduced form and when we identify a structural financial shock, that variation in financial factors had a larger role post-2000 and a more modest role pre-2000. Our results suggest that the financial sector did play a role in overheating the business cycle pre-Great Recession. Second, while an identified financial shock can generate a negative correlation between the lagged credit cycle and the contemporaneous output gap, the unconditional correlation between the credit cycle and the output gap is still positive. The latter at least suggests that one should be careful in associating an increase in the financial cycle to bust in the business cycle.

International risk sharing with heterogeneous firms

This paper explores international consumption risk sharing in an open economy macro model with firm heterogeneity and shows that firm entry and the self-selection of more efficient firms into exporting account for better international risk sharing. I show analytically that the conventional unconditional correlation between relative consumption and the real exchange rate is not a good metric for measuring international consumption risk sharing. World trade data covering more than two decades indicate that the extent of international risk sharing is underestimated.

Equity home bias and consumption-real exchange rate puzzles: A joint solution

In a standard dynamic stochastic general equilibrium model with a complete asset market, home agents should hold a foreign equity biased portfolio to hedge the non-traded labor income risk, which contradicts home equity biased portfolios observed worldwide. As the labor income share increases, the degree of home bias should decrease because there is more incentive to hold foreign equity. In the data, there is not any evidence that the labor income share and the degree of home bias are negatively correlated. The standard model also predicts that the consumption differential-real exchange rate correlation is positive, while it is negative in the data. I show that a combination of market incompleteness, non-tradable goods, and labor supply can explain the three features above. My model can generate a large equity home bias, despite the strong positive correlation of non-traded human capital return with domestic equity return. The home bias is not sensitive to the labor income share. The consumption differential-real exchange rate unconditional correlation generated by my model simulation is zero.

A cross-country study of skills and unemployment flows

Using an international survey that directly assesses the cognitive skills of the adult population, I study the relation between skills and unemployment flows across 37 countries. Depending on the specifically assessed domain, I document that skills have an unconditional correlation with the log-risk-ratio of exiting to entering unemployment of 0.65–0.68 across the advanced and skill-abundant countries in the sample. The relation is remarkably robust and it is unlikely to be due to reverse causality. I do not find evidence that this positive relation extends to the seven relatively less advanced and less skill-abundant countries in the sample: Peru, Ecuador, Indonesia, Mexico, Chile, Turkey and Kazakhstan.

A Unified Approach for Jointly Estimating the Business and Financial Cycle, and the Role of Financial Factors

We jointly estimate the U.S. business and financial cycle through a unified empirical approach while simultaneously accounting for the role of financial factors. Our approach uses the Beveridge-Nelson decomposition within a medium-scale Bayesian Vector Autoregression. First, we show, both in reduced form and when we identify a structural financial shock, that variation in financial factors had a larger role post-2000 and a more modest role pre-2000. Our results suggest that the financial sector did play a role in overheating the business cycle pre-Great Recession. Second, while we document a positive unconditional correlation between the credit cycle and the output gap, the correlation of the lagged credit cycle and the contemporaneous output gap turns negative when we condition on a financial shock. The sign-switch suggests that the nature of the underlying shocks may be important for understanding the relationship between the business and financial cycles.

Trends and cycles in U.S. job mobility

AbstractRecent studies document a decline in U.S. labor‐market fluidity from as early as the 1970s on. Making use of the Annual Social and Economic supplement to the Current Population Survey, I uncover a pronounced increase in job‐to‐job mobility from the 1970s to the 1990s, i.e. the annual share of continuously employed job‐to‐job movers rises from 5.9% of the labor force in 1975–1979 to 8.8% in 1995–1999. Job‐to‐job mobility exhibits a downward trend only since the turn of the millennium. In order to provide a formal economic interpretation, I additionally estimate the parameters of the random on‐the‐job search model. Furthermore, I document that job‐to‐job mobility has an unconditional correlation of −0.86 with the unemployment rate at business‐cycle frequencies in 1975–2017, varying by around 3 percentage points over the business cycle.

Does Integration in the Asian Frontier, Emerging, and Developed Stock Markets Conceal a Hidden Opportunity for Portfolio Diversification?

In the current scenario where Asian markets are growing at an enormous rate, they present a lucrative opportunity for investors to reap better rewards. However, the steep rise in the interdependencies of the markets within the Asian belt has made the investment scenario complex for investors. This study examines the integrated behavior of frontier, emerging, and developed stock markets in Asia. The study used unconditional correlation analysis and a VAR framework for the period ranging from August 1, 2000, to March 31, 2019. In the course of the study, it emerged that the high-level integration between emerging and developed stock markets in this region creates an unfavorable landscape for portfolio diversification. However, there remains an exciting opportunity with safe asset allocation strategies to derive diversification benefits by investing in dynamic and surging Asian frontier stock markets. TOPICS:Exchanges/markets/clearinghouses, developed markets, VAR and use of alternative risk measures of trading risk, portfolio construction Key Findings ▪ The extent of integration between the Asian emerging and developed stock markets has increased and gained greater momentum with the episode of the Global Financial Crisis (GFC). Therefore, there is little/no scope left for portfolio diversification. ▪ In the journey of wealth creation, portfolio investors can concentrate on Asian frontier markets to diversify their portfolio, due to the low integration of these markets with Asian developed and emerging stock markets. ▪ The influencing role of Asian developed markets on emerging stock markets has grown predominant since the GFC. But the notion that developed markets dominate small markets does not hold true in the case of Asian frontier stock markets.

The power of diversification: Do African fixed-income investors have a chance in Malaysian Sukuk market?

PurposeThis study investigates the presence of portfolio diversification benefits for South African, Nigerian, Ghanaian and Kenyan fixed-income investors diversifying bond portfolios in the Malaysian sovereign Sukuk market.Design/methodology/approachThe paper uses wavelet coherence and a multivariate generalized autoregressive conditional heteroscedastic (GARCH) model. The data cover the period from September 2013 to January 2019.FindingsThe findings obtained from the wavelet coherence model reveal evidence of portfolio diversification opportunities for African fixed-income investors in the Malaysian sovereign Sukuk market. These opportunities are more significant in the short- and medium-term investment horizons than in the long-term. Also, the results of multivariate GARCH show that the Malaysian Sukuk market has a negative unconditional correlation with the South African bond market, signifying better diversification benefits for these investors.Practical implicationsThe findings have implications for both fund managers and investors intending to include Sukuk in a diversified portfolio to reduce their risks and maximize their return from bonds.Originality/valueTo the best knowledge of the authors, this is the first study to examine the opportunities for African investors in the Malaysian Sukuk market.