A quantile-copula approach to dependence between financial assets

This article unveils the dependence structure between United States stock prices, crude oil prices, exchange rates, and U.S. interest rates. In particular, we employ linear and nonlinear estimation methods, such as quantile regression and the quantile-copula approach. Over the 1998–2017 period, we find that there is a positive relationship between the dollar value and the S&P 500 stock price, with the exception of the lower and upper tails of the stock return distribution. Further evidence is obtained on the dependence structure between other asset returns. The stock returns are negatively related to oil prices but positively to U.S. interest rates. Our results highlight the way that financial assets are linked, which have implications for risk management and monetary policy.     

The impact of oil on equity returns of Canadian and U.S. Railways and airlines

The purpose of this paper is to investigate the relationship between oil price movements and equity returns of railways and airline in Canada and the U.S. Using a robust set of oil measures, which includes both West Texas Intermediate (WTI) and Western Canadian Select (WCS) data, this research finds that railways and airlines react uniquely to oil price movements. Specifically, equity returns of railways in Canada and airlines in the U.S. tend to be negatively impacted by positive movements in WTI. Equity returns of airlines in Canada and railways in the U.S show limited evidence of any impact. Additional estimations suggest that equity returns of airlines react asymmetrically and that information regarding oil price movements may gradually diffuse over time. With the changing North American energy landscape (e.g., oil sands and shale oil), the increased reliance on transporting crude oil via railways should lead academics and practitioners to further research in this area.     

Sticky prices, fair wages, and the co-movements of unemployment and labor productivity growth

This paper studies the co-movements of unemployment and labor productivity growth for the U.S. economy. Measures of co-movements in the frequency domain indicate that co-movements between variables differ strongly according to the frequency. First, long-term and business cycle co-movements are larger than short-term co-movements. Second, co-movements are negative in the short and long run, but positive over the business cycle. A New Keynesian model that combines nominal rigidity on the goods market (sticky prices) and real rigidity on the labor market (fair wages) is shown to be quantitatively consistent with the observed co-movements both in the long term and over the business cycle. However, the model fails to explain the short-term co-movements.     

The asymmetric effect of crude oil prices on stock prices in major international financial markets

The aim of this paper is to explore the potential asymmetric impacts of positive and negative shocks in crude oil prices on stock prices in six major international financial markets which include China, Hong Kong, America, Japan, Britain, and Germany. We test for these asymmetric effects on 8 major international financial markets indices over the 2007M01–2020M03 periods. Our independent measures include the prices of Brent crude oil futures and West Texas Intermediate (WTI) futures. We use the nonlinear ARDL (NARDL) model proposed by Shin et al. (2014), which can capture both short- and long-run nonlinearities through positive and negative partial sum decompositions of the explanatory variables. This research finds that positive and negative fluctuations of oil price have asymmetric effects on stock price index in four financial markets, but the performance of the asymmetry is different. Specifically, the impacts of volatility in oil prices on two indices of Chinese stock prices are different, and the asymmetric effects of oil price volatility on stock price indices in China and other financial markets are significantly different.     

Spillovers and directional predictability between international energy commodities and their implications for optimal portfolio and hedging

This study sheds a new light on the dependence and the directional predictability between eight major energy price returns, using the Cross-Quantilogram (CQ) and the Partial CQ (PCQ) analysis. The energy prices cover the time series for the U.S. natural gas and seven internationally traded crude oil types. The results reveal a significant directional predictability running from most of energy commodities returns to the OPEC basket and the very light Tapis crude oil returns. However, the quantile predictability in both directions is enabled only for the relations between the light Brent and the light WTI, and between the OPEC basket and the Malaysian Tapis. The time-varying predictability analysis reveals that there is a significant upper quantile dependence between these international energy commodities. Finally, we find that the TAPIS can be a good hedging vehicle for other energy markets. These findings may be instructive for both policymakers (in terms of financial stability) and market participants (in terms of performance).     

Multi-scale interactions between economic policy uncertainty and oil prices in time-frequency domains

Dynamic interactions between policy uncertainty and economic activity, including oil prices, have attracted increasing amounts of scholarly interest, but few studies have considered the inherent feature that the entire market is composed of different stakeholders operating in different time horizons. To fill this gap and address this issue, this paper proposes a multi-scale correlation framework. Specifically, we use the wavelet coherence method and scale-by-scale linear Granger causality tests to explore the co-movement and causality of pairs of economic policy uncertainty indices of G7 countries, China, Brazil, and Russia and West Texas Intermediate (WTI) oil prices. Our results show that the interaction between economic policy uncertainty and oil prices in the short-term is weak but gradually strengthens towards the long-term, especially when significant historical political or financial events occurred. Moreover, a consistent conclusion is that the interaction is negative in the medium-term, while it is positive in the long-term. Further, Granger causality tests at different time-scales show that no Granger causality from economic policy uncertainty to oil prices exists in the short-term for all sample countries, except the US, while there is a strong unidirectional or bidirectional Granger causality for all researched countries in the medium- and the long-term.     

Relationship between oil,stock prices and exchange rates: A vine copula based GARCH method

In this paper, we apply a vine copula approach to investigate the dynamic relationship between energy, stock and currency markets. Dependence modeling using vine copulas offers a greater flexibility and permits the modeling of complex dependency patterns for high-dimensional distributions. Using a sample of more than 10 years of daily return observations of the WTI crude oil, the Dow Jones Industrial average stock index and the trade weighted US dollar index returns, we find evidence of a significant and symmetric relationship between these variables. Considering different sample periods show that the dynamic of the relationship between returns is not constant over time. Our results indicate also that the dependence structure is highly affected by the financial crisis and Great Recession, over 2007–2009. Finally, there is evidence to suggest that the application of the vine copula model improves the accuracy of VaR estimates, compared to traditional approaches.     

Time-frequency connectedness of crude oil,economic policy uncertainty and Chinese commodity markets: Evidence from rolling window analysis

This article investigates the time–frequency connectedness of economic policy uncertainty (EPU), WTI crude oil and Chinese commodity markets during the period between 2004 and 2020. Rolling window wavelet vector autoregression and connectedness networks are developed to evaluate the time-varying characteristics of the connectedness. The empirical results are as follows: First, the total connectedness between EPU, oil and commodities becomes stronger as the time scale increases. Second, the net connectedness of EPU and WTI in the system is positive, indicating that EPU and WTI are contributors to information and will affect financial markets across time scales. Third, the connectedness remains at a high level during financial crises across all scales, and the contribution of EPU and crude oil to commodities increases significantly. Specifically, compared with other commodity sectors, grains are greatly affected by EPU under the condition that the energy sector is seriously affected by crude oil. Overall, investors and policy makers should consider connectedness in terms of time and frequency when making a decision.     

Multivariate contemporaneous-threshold autoregressive models

This paper proposes a contemporaneous-threshold multivariate smooth transition autoregressive (C-MSTAR) model in which the regime weights depend on the ex-ante probabilities that latent regime-specific variables exceed certain threshold values. A key feature of the model is that the transition function depends on all the parameters of the model as well as on the data. Since the mixing weights are also a function of the regime-specific noise covariance matrix, the model can account for contemporaneous regime-specific co-movements of the variables. The stability and distributional properties of the proposed model are discussed, as well as issues of estimation, testing and forecasting. The practical usefulness of the C-MSTAR model is illustrated by examining the relationship between US stock prices and interest rates.     

Conditional correlations and volatility spillovers between crude oil and stock index returns

This paper investigates the conditional correlations and volatility spillovers between the crude oil and financial markets, based on crude oil returns and stock index returns. Daily returns from 2 January 1998 to 4 November 2009 of the crude oil spot, forward and futures prices from the WTI and Brent markets, and the FTSE100, NYSE, Dow Jones and S&P500 stock index returns, are analysed using the CCC model of Bollerslev (1990), VARMA-GARCH model of Ling and McAleer (2003), VARMA-AGARCH model of McAleer, Hoti, and Chan (2008), and DCC model of Engle (2002). Based on the CCC model, the estimates of conditional correlations for returns across markets are very low, and some are not statistically significant, which means the conditional shocks are correlated only in the same market and not across markets. However, the DCC estimates of the conditional correlations are always significant. This result makes it clear that the assumption of constant conditional correlations is not supported empirically. Surprisingly, the empirical results from the VARMA-GARCH and VARMA-AGARCH models provide little evidence of volatility spillovers between the crude oil and financial markets. The evidence of asymmetric effects of negative and positive shocks of equal magnitude on the conditional variances suggests that VARMA-AGARCH is superior to VARMA-GARCH and CCC.     

Time-frequency effect of crude oil and exchange rates on stock markets in BRICS countries: Evidence from wavelet quantile regression analysis

This study examines the effects of oil prices and exchange rates on stock market returns in BRICS countries (Brazil, Russia, China, India and South Africa) from a time–frequency perspective over the period 2009–2020. We use wavelet decomposition series to develop a threshold rolling window quantile regression to detect time–frequency effects at various scales. The empirical results are as follows. First, our findings confirm that the effects of both crude oil prices and exchange rates on BRICS stock returns are asymmetric. Positive shocks of crude oil have a greater impact on a bull market, whereas negative shocks have a greater impact on a bear market. Second, there is a short-term enhancement effect of crude oil and exchange rate on BRICS stock markets. In addition, volatility in the macro financial environment also exacerbates the impacts of oil prices and exchange rates on the stock market, and these fluctuations are heterogeneous. Overall, these findings provide useful insights for international investors and policy makers.     

Oil price shocks and credit spread: Structural effect and dynamic spillover

This study analyzes the heterogeneous response of U.S. credit spread to global oil price shocks by building an extended structural vector autoregressive model (SVAR), which can distinguish among the U.S. and non-US oil supply shocks, aggregated demand shocks and oil market-specific demand shocks behind the real oil prices. Meanwhile, a spillover index model developed by Diebold and Yilmaz (2012) (hereafter D.Y. (2012)) is used to estimate the link between oil price shocks and the U.S. credit spread over time. The results show that (i) the credit spread does not respond to global oil supply shocks and non-US oil supply shocks, but has a negative reaction to the U.S. oil supply shocks, aggregate demand shocks, and oil-market-specific demand shocks. (ii) There exists a close connectedness between oil price shocks and the U.S. credit spread, and the link fluctuates cyclically and relates to the economic cycle and the U.S. shale oil revolution. (iii) The spillover from different oil price shocks to the U.S. credit spread shows significant heterogeneity over time. Our findings suggest that policymakers and investors can better track the U.S. credit spread changes using oil price information.     

Retrieving the implicit risk neutral density of WTI options with a semi-nonparametric approach

This paper contributes to the literature on the estimation of the Risk Neutral Density (RND) function by proposing a log-semi-nonparametric (log-SNP) distribution as the implicit RND when the Gram-Charlier model is used for option pricing. The performance of the model is compared to the lognormal (Black Scholes) benchmark for a sample of option prices for West Texas Intermediate (WTI) crude oil that were traded in the period between January 2016 and December 2017. Results show that the lognormal specification tends to systematically undervalue option prices and that the proposed log-SNP distribution, which explicitly adjusts for negative skewness and excess kurtosis, results in markedly improved accuracy, especially in periods of market instability. As a result, the implied skewness and excess kurtosis are relevant sources of information on market expectations that should be used for hedging and risk management purposes.     

How does investor attention matter for crude oil prices and returns? Evidence from time-frequency quantile causality analysis

This paper studies the time–frequency, nonlinear quantile relationship between investor attention (GSVI) and crude oil over the period from January 2000 to April 2020. To do so, the wavelet coherency, wavelet-based causality-in-quantiles test and quantile-on-quantile method are employed. The results indicate that first, the correlation between investor attention and crude oil is relatively high, and the highly correlated regions are concentrated from 8 to 16 months. In most cases, the GSVI is negatively correlated with the crude oil market. Additionally, under extreme market conditions, the explanatory ability is stronger than in the normal market, and it is greater in the low-frequency domain than in the high-frequency domain. Finally, investor attention has an apparent asymmetric impact on crude oil prices and returns at each scale, displaying a positive effect on the low quantiles of crude oil but a negative effect on the high quantiles across all quantiles of the GSVI. In the short term, when crude oil prices and returns are in a bear market, the larger volume of the GSVI has a greater impact on them. Moreover, the impact becomes greatest under extreme market conditions.     

The daily relationship between U.S. asset prices and stock prices of American countries

The daily transmission of U.S. comprehensive stock indices to foreign stock markets has been studied extensively, but the transmission may just be that the foreign stock prices respond to news underlying the change in the U.S. stock indices. Besides the regularly economic announcements, news relevant for the U.S. economy may include qualitative news and non-economic events. Due the daily nature of the news, there is no appropriate reference as to its impact on the U.S. or foreign economy and the only accessible reference probably is the change in the financial asset prices. But the U.S. stock index is general in nature and cannot be used to offer specific information about the U.S. economy. Some U.S. asset prices other than stock indices may reveal more specific information about the U.S. economy. Looking into the daily relationship between these U.S. asset prices and stock indices of four American countries in two periods with drastically different economic conditions, this study finds that the daily relationship between these U.S. asset prices and foreign stock prices is consistent with the prevailing U.S. economic fundamentals. From the relationship we identify some U.S. economic conditions foreign stock prices respond to. These economic conditions include real economic shocks, monetary policies, and business default risks.     

What determines the long-term correlation between oil prices and exchange rates?

In this study, we obtain the long-term correlation between oil prices and exchange rates by employing the dynamic conditional correlation-mixed data sampling (DCC-MIDAS) model. We then identify the factors that influence the long-term correlation using panel data analysis. We find that the long-run correlations between oil prices and exchange rates are negative for all oil-exchange rate markets except Japan. We also find that both inflation and term spread have negative effects, while the risk-free interest rate has a positive effect on the long-term correlation between oil prices and exchange rates. Importantly, the empirical results show that an increase in inflation will significantly damage the real value of the currency itself.     

两岸经贸、产业结构与经济周期协动性——基于VECM模型的实证分析

在相关理论和两岸经贸发展特点的基础上,分析两岸经济周期协动性的传递因素和机制,实证研究两岸贸易强度、直接投资强度和产业结构相似度与经济周期协动性的因果关系,结果显示：虽然两岸贸易与台商投资是两岸经贸往来的主要形式,但这对两岸经济周期协动性的变化并未产生直接作用,说明两岸之间并未形成实质性的经济互动;产业结构相似度是两岸经济周期协动性变化的主要原因,这预示随着两岸经贸的密切发展、产业合作的深入进行和大陆产业结构的升级,两岸经济结构特征逐渐趋同且紧密联接。     

Stock prices and domestic and international macroeconomic activity: a cointegration approach

This paper supports the existence of a significant, long-run relationship between stock prices and domestic and international economic activity in six European economies. Johansen Cointegration tests demonstrate that stock price levels are significantly related to industrial production, business surveys of manufacturing orders, short- and long-term interest rates as well as foreign stock prices, short-term interest rates and production. Variance decomposition methods support the strong explanatory power of macroeconomic variables in contributing to the forecast variance of stock prices. Hence, stock prices are determined by macroeconomic activity.     

Macroeconometric model comparison and evaluation techniques: A practical appraisal

This paper examines four model comparison techniques in the setting of an actual large-scale U.S. macroeconomic model. The techniques include formal hypothesis tests, mean squared error analysis, and two new tests based on the complementary conditions that the structural and reduced form disturbances should be orthogonal to the predetermined variables. The latter two tests can be conveniently implemented by regressing either structural residuals or reduced form forecasting errors on a vector of predetermined variables. The empirical example tests restrictions on the simultaneous feedback effects of prices, wage rates, and interest rates.     

Oil prices,stock returns,and exchange rates: Empirical evidence from China and the United States

Employing the diagonal BEKK model as well as the dynamic impulse response functions, this study investigates the time-varying trilateral relationships among real oil prices, exchange rate changes, and stock market returns in China and the U.S. from February 1991 to December 2015. We highlight several key observations: (i) oil prices respond positively and significantly to aggregate demand shocks; (ii) positive oil supply shocks adversely and significantly affect the Chinese stock market; (iii) oil price shocks persistently and significantly impact the trade-weighted US dollar index negatively; (iv) the US and China stock markets correlate positively just as the dollar index and the exchange rate does; (v) a significant parallel inverse relation exists between the US stock market and the dollar and between the China stock market and the exchange rate; and (vi) the Chinese stock market is more volatile and responsive to aggregate demand and oil price shocks than the US stock market in recent years.