New evidence of extreme risk transmission between financial stress and international crude oil markets

We aim to analyze the risk transmission between financial stress and crude oil under different shocks, with applying a novel Granger causality test. Recent works suggest that this risk transmission is mixed, however, scholars mainly focus on their average causality but neglect the extreme causality and its time-varying characteristic. Using the weekly data of the financial stress index and WTI prices spanning from 1994 to 2020, we employ the extreme time-domain and frequency-domain Granger causality test to conduct our research. Results obtained from the time-domain test imply that their causality generally originates from extreme shocks rather than non-extreme shocks, which hasn’t been found before. For further distinguishing the long-run and short-run effects of these shocks, we apply the frequency-domain test and discover that these causalities are mainly found for long the run. Thus, investors and policy-makers may benefit from monitoring financial stress, especially under long-term extreme conditions.     

International nonlinear causality between stock markets

In this paper, we test for linear and nonlinear Granger causality between the French, German, Japanese, UK and US daily stock index returns from 1973 to 2003. We find a strong contemporaneous linear dependence between European countries and a directional linear dependence from the US towards the other markets. Besides, linear causality increases after 1987, a finding consistent with the expected effects of financial liberalization of the 1980s and the 1990s. Above all, we document the presence of bidirectional nonlinear causality between daily returns. To check for spurious nonlinear causality, we filter out heteroskedasticity using a FIGARCH model. The dramatic decrease in the number of significant nonlinear causality lags confirms that heteroskedasticity played a major part in the previous findings. We then check if a few structural breaks can explain the remaining nonlinear causality. We find that a large number of nonlinear relationships vanish when we control for structural breaks, whereas linear causality remains.     

Time-varying pattern causality inference in global stock markets

Causality analysis can reveal the intrinsic interactions in financial markets. Though Granger causality test and transfer entropy method have successfully determined positive and negative causal interactions, they fail to reveal a more complex causal interaction, dark causality. Moreover, the causal relationship between variables may be time-varying. Thus, in this work, we are dedicated to determining the nature of causal interaction and explore the time-varying causality in global stock markets. To achieve this goal, pattern causality (PC) theory, cross-convergent mapping (CCM) theory, the sliding window method and complex networks are applied. By them, three causal interactions with different strength are revealed in global stock markets, and the causal strength is time-varying in different periods both in simulated systems and financial markets. While the dominant causal interaction is stable except for some stock pairs in frontier and emerging markets. In total, we determine the positive dominant causality in global stock markets; that is, the overall consistent trend among stocks can be explored. Additionally, we discover some exceptions that show negative dominant causality, where the reverse trend can be revealed among them; moreover, their dominant causality is time-varying. These uncertainties should receive great attention from investors and government managers.     

Does insurance activity promote economic growth? Further evidence based on bootstrap panel Granger causality test

This study applies the bootstrap panel Granger causality test to test whether insurance activity promotes economic growth, using data from 10 OECD countries over the period of 1979–2006. Empirical results indicate that one-way Granger causality running from all insurance activities to economic growth for France, Japan, Netherlands, Switzerland, and the UK, and economic growth Granger causes insurance activities in Canada (for life insurance), Italy (for total and life insurance) and the USA (for total and non-life insurance). There is a two-way Granger causality between life insurance activity and economic growth in the USA, while no causality between insurance activities and economic growth is found in Belgium (for all insurance), Canada (for total and non-life insurance), Italy (for non-life insurance) and Sweden (for life insurance). Our results also confirm the finding of Ward and Zurbruegg [Does insurance promote economic growth? Evidence from OECD economies. Journal of Risk and Insurance 67, no. 4: 489–506] showing that the insurance–growth nexus varies across countries, since their paper have previously demonstrated heterogeneity in this vein. In an analysis of a broader, though overlapping 17-country sample and taking into account banking activities, the results suggest the importance of including banking activities when investigating the insurance–growth relationship.     

Interbank liquidity risk transmission to large emerging markets in crisis periods

In this paper, we conduct two investigations regarding funding liquidity risk in large emerging economies: Brazil, Russia, India, China, and South Africa — BRICS. In the first, we track the relevance of monetary policy decisions originating in developed economies for interbank funding liquidity risk in BRICS economies during crisis periods by applying a time-varying parameter model in a Bayesian framework. The results indicate weak associations between interbank credit market and US monetary policy and market conditions. In contrast, the Federal Reserve's National Financial Conditions Index (NFCI) — a representative of the health of both real and financial sectors in the US — matters more. The temporal patterns of the results imply that key central banking decisions precede or coincide with low degrees of associations. In the second, we examine whether interbank credit crunch exerts an influence on market liquidity risk in BRICS economies using a Granger causality approach. The results reveal that interbank credit crunch depresses market liquidity in the corresponding domestic market and that the state of fear and credit market conditions in the US exert some influence in this regard. Overall, our findings hint at judicious market intervention and liquidity management by BRICS central banks.     

Interdependence and dynamic linkages between the emerging stock markets of South Asia

The present article examines the dynamic linkages between the stock markets of Bangladesh, India, Pakistan and Sri Lanka using a temporal Granger causality approach by binding the relationship among the stock price indices within a multivariate cointegration framework. We also examine the impulse response functions. Our main finding is that in the long run, stock prices in Bangladesh, India and Sri Lanka Granger‐cause stock prices in Pakistan. In the short run there is unidirectional Granger causality running from stock prices in Pakistan to India, stock prices in Sri Lanka to India and from stock prices in Pakistan to Sri Lanka. Bangladesh is the most exogenous of the four markets, reflecting its small size and modest market capitalization.     

Investigating volatility spillover of energy commodities in the context of the Chinese and European stock markets

This paper investigates spillover from energy commodities to Shanghai stock exchange and European Stock market, and identifies possible risks transmission and portfolio diversification opportunities. The study is conducted on daily spot prices of carbon (CO2) emission, natural gas and crude oil from 16 December 2010 to 29 December 2022, employing Granger causality test, dynamic conditional correlation (DCC), Diebold-Yilmaz (2012) and Barunik-Krehlic (2017) models. Results identify higher volatility and imply greater connectedness in the longer run. Additionally, natural gas is witnessed as the highest contributor of the shocks and crude oil as the highest receiver of the shocks from the network connection. Further results suggest for investment in energy commodities in shorter run rather than long run for efficient portfolio diversification. Results from this study are expected to have practical implications for portfolio managers, investors, and market regulators, given the suggestion of this study to incorporate energy stocks for efficient diversification of risk.     

Does country risks predict stock returns and volatility? Evidence from a nonparametric approach

We use the k-th order nonparametric causality test at monthly frequency over the period of 1984:1–2015:12 to analyze whether aggregate country risk, and its components (economic, financial and political) can predict movements in stock returns and volatility of eighty-three developed and developing economies. The nonparametric approach controls for the existing misspecification of a linear framework of causality, and hence, the weak evidence of causality obtained under the standard Granger tests cannot be relied upon. When we apply the nonparametric test, we find that, while there is no evidence of predictability of squared stock returns barring one case, at times, there are nearly 50 percent of the countries where the aggregate risks and its components tend to predict stock returns and realized volatility.     

What role do futures markets play in Bitcoin pricing? Causality,cointegration and price discovery from a time-varying perspective?

Recent papers that have explored spot and futures markets for Bitcoin have concluded that price discovery takes place either in the spot, or the futures market. Here, we consider the robustness of previous price discovery conclusions by investigating causal relationships, cointegration and price discovery between spot and futures markets for Bitcoin, using appropriate daily data and time-varying mechanisms. We apply the time-varying Granger causality test of Shi, Phillips, and Hurn [2018]; time-varying cointegration tests of Park and Hahn [1999], and time-varying information share methodologies, concluding that futures prices Granger cause spot prices and that futures prices dominate the price discovery process.     

Cointegration and causality in the Asian and emerging foreign exchange markets: Evidence from the 1990s financial crises

This article examines the cointegration level, changes in the existence and directions of causality of the foreign exchange (FX) rates in the Asian and emerging markets during the 1990s financial crises. Engle and Granger's simple bivariate and Johansen's multivariate cointegrations are applied to the FX rates for the 1994 Mexican, 1997 Asian, 1998 Russian, and 1999 Brazilian crises. In addition, the article conducts the Granger causality test and impulse response analysis to examine the causality pattern in all the FX rates. The analysis shows most of the pre-Mexican causality disappears and significant numbers of new causality emerge in the 1994 Mexican crisis while the 1997 Asian crisis generates significant spillover effects into the later part of the 1998 Russian and 1999 Brazilian crises.     

Does economic policy uncertainty affect cryptocurrency markets? Evidence from Twitter-based uncertainty measures

Using daily data from August 9, 2015, to July 7, 2020, this study examines the effects of economic policy uncertainty (EPU) on the returns of four cryptocurrencies: Bitcoin, Ethereum, Litecoin, and Ripple. To this end, two new measures of EPU (Twitter-based economic uncertainty and Twitter-based market uncertainty) are considered. A Granger causality test using the recursive evolving window approach shows a significant causality between the Twitter-based EPU measures and the BTC/USD exchange rate from October 2016 to July 2017. Moreover, a significant causality was noted from the EPU measures to the ETH/USD exchange rate from June 2019 to February 2020 and from the EPU measures to the XRP/USD exchange rate from January 2020 to February 2020. The Twitter-based EPU measures primarily positively affect the returns of the related cryptocurrencies during these periods. These results are robust to different measures of Twitter-based EPU and different econometric techniques. Potential implications, including the COVID-19 era, are also discussed.     

VaR与CVaR在商业银行风险度量中的比较分析及应用

本文系统地阐述了时下银行流行的VaR(ValueatRisk)风险度量技术,并分析了该理论存在的缺陷和使用上的局限性,从而提出以CVaR(ConditionalValueatRisk)模型作为风险度量的替代方法,详细分析了CVaR的原理、特长以及在银行业应用前景,包括风险度量、绩效分析和行为指引等方面的突出作用。最后研究了CVaR在我国商业银行的具体应用。     

Random walk and monetary causality in five exchange markets

Time series behavior of monthly spot exchange rates for the French franc, the Deutsche mark, the Italian lira, the Japanese yen, and the UK pound, all priced in relation to the US dollar, shows the robustness of the random walk hypothesis. Incremental efficiency is investigated by a new test procedure, based on the reduction of the forecast error variance, which is a direct implementation of the definition of Granger causality. Exchange markets are found to be not only money efficient, but also monetary efficient in that they are efficient with respect to real income and market interest rates in addition to money stock.     

Extreme downside risk spillover from the United States and Japan to Asia-Pacific stock markets

This paper proposes a binary response model approach to measure and forecast extreme downside risks in Asia-Pacific markets given information on extreme downside risks in the U.S. and Japanese markets. The extreme downside risk of a market is measured as the occurrence of extreme downside movement—market returns falling below left-tail Value at Risk in a Markov switching framework. The empirical findings are consistent with the following notions. First, extreme downside movements of the S&P 500 and Nikkei 225 are significantly predictive for the likelihood of extreme downside movements in all the investigated Asia-Pacific markets. Second, the majority of Asia-Pacific markets become more sensitive to Japan's extreme downside risk when the Japanese market switches into high volatility periods, whereas the U.S. spillover effect is intensified only on Taiwan during high volatility periods in the U.S. Third, mainland China is the least sensitive to extreme downside risk in the U.S. and Japan, Australia is the most sensitive to the U.S., and Singapore is the most sensitive to Japan.     

Causality and cointegration of stock markets among the United States,Japan and the South China Growth Triangle

This paper explores the causality and cointegration relationships among the stock markets of the United States, Japan and the South China Growth Triangle (SCGT) region. Applying the recently advanced unit root and cointegration techniques that allow for structural breaks over the sample period (October 2, 1992 to June 30, 1997), we find that there exists no cointegration among these markets except for that between Shanghai and Shenzhen. By invoking the Granger causality test and considering the non-synchronous trading problem, we will show that stock price changes in the US have more impact on SCGT markets than do those of Japan. More specifically, price changes in the US can be used to predict those of the Hong Kong and Taiwan markets on next day. Similarly, price changes on the Hong Kong stock market lead the Taiwan market by 1 day. Furthermore, the stock returns of the US and Hong Kong markets are found to be contemporaneous. Finally, there is a significant feedback relationship between the Shanghai and the Shenzhen Stock Exchanges.     

Causal relationship between spot and futures prices with multiple time horizons: A nonparametric wavelet Granger causality test

This study investigates the causal information flow between 45 major daily spot returns and their corresponding futures in developing, emerging, and commodity indices through a novel nonparametric wavelet Granger causality test (NWGC) that is capable of detecting causality patterns in various time scales without any stationarity assumption or multivariate autoregressive modeling requirement. We provide new evidence for a complex causality pattern phenomenon. First, there may not be just one dichotomous answer about the Granger causality test for each market data in a time domain, as markets exhibit different causal information flows for different time scales. Second, each market may show distinct causality patterns compared to other markets.     

Structural change and lead-lag relationship between the Nikkei spot index and futures price: a genetic programming approach

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In this paper we adopt a nonparametric genetic programming (GP) approach to identify the structural changes in the Nikkei spot index and futures price. Due to the dominance of the ‘normal’ period in sample data, the lead-lag relationship identified in the spot-futures system based on conventional methods such as the test for Granger causality pertains to the normal period and may not be applicable in an ‘extreme’ period. Using GP we identify the lead-lag relationship based on the chronological ordering of the structural changes in the spot and futures markets. Our results show that in recent periods, major market changes originated from the spot market and spread over to the futures market.     

股票市场发展对广东产业结构优化的实证分析

本文采取新的产业结构优化综合指标体系,运用格兰杰因果检验方法,实证分析股票市场发展对广东产业结构优化的影响,以此验证股票市场对产业结构优化是否存在积极的效应。研究结果表明:股票市场对广东省产业结构的优化存在积极影响,但效应并不强,对此本文提出了相应的对策建议。     

Income Growth,Energy Consumption,and Carbon Emissions: The Case of China

The paper examines the long-run relationship among per capita income, energy consumption, and carbon emissions in China during 1953–2008. We employ the Toda—Yamamoto procedure to test the Granger causality in a vector autoregressive (VAR) model with levels. We find strong evidence of bilateral Granger causality running between income and energy consumption and unilateral Granger causality running from carbon emissions to income. We further provide policy suggestions to address the energy and sustainable development dilemma in China. These include diversifying the energy mix and exploiting renewable energy, enhancing energy efficiency, transforming the economic growth pattern, and reducing the reliance on resource- and energy-intensive industries.     

Co-movements of sector index returns in the world's major stock markets in bull and bear markets: Portfolio diversification implications

In this paper, principal components analysis and Granger causality tests are used to study the portfolio diversification implications of the co-movements of sector indexes in the US, UK, German, French, and Japanese stock markets in bull and bear markets. We find that, in a bull market, investors can obtain more benefit with global diversification than with domestic diversification even if they invest in the same sector in different countries as opposed to investing in different sectors within the same country. In a bear market, the sectors of different countries tend to be more closely correlated and country diversification opportunities are limited.