Spillovers between Bitcoin and other assets during bear and bull markets

This article contributes to the embryonic literature on the relations between Bitcoin and conventional investments by studying return and volatility spillovers between this largest cryptocurrency and four asset classes (equities, stocks, commodities, currencies and bonds) in bear and bull market conditions. We conducted empirical analyses based on a smooth transition VAR GARCH-in-mean model covering daily data from 19 July 2010 to 31 October 2017. We found significant evidence that Bitcoin returns are related quite closely to those of most of the other assets studies, particularly commodities, and therefore, the Bitcoin market is not isolated completely. The significance and sign of the spillovers exhibited some differences in the two market conditions and in the direction of the spillovers, with greater evidence that Bitcoin receives more volatility than it transmits. Our findings have implications for investors and fund managers who are considering Bitcoin as part of their investment strategies and for policymakers concerned about the vulnerability that Bitcoin represents to the stability of the global financial system.     

Spillovers of volatility index: evidence from U.S., European,and Asian stock markets

We investigated the cross-market relations of volatility indexes with U.S. and non-U.S. stock market returns. We found that the pervasive VIX influence at both U.S. and non-U.S. stock markets. The VSTOXX and VKOSPI capture the major shocks to the global economy and show movements similar to the VIX. The empirical findings indicate that volatility index changes are important in explaining stock returns. We also examined spillover effects across volatility indexes. The VIX is a main transmitter, and the VKOSPI the main receiver, of these spillovers. The results point to a leading role for the VIX in the international market.     

Volatility spillovers and connectedness among credit default swap sector indexes

This article studies volatility spill-over effects and market connectedness using daily data of credit default swap spreads for U.S. companies over a period from 2007 to 2012. We quantify volatility spillovers by means of an unconditional analysis performed using the entire sample, and a conditional analysis which estimates the model using a rolling window. As our database contains the global financial crisis (GFC), we are able to determine how volatility spillovers spread in the economy during the recent market turmoil. Our unconditional results confirm that the Financials sector was a main contributor to the overall market volatility along with the Consumer Goods, Consumer Services and Basic Materials sectors. The conditional analysis clearly identifies that the Financials was the major feeding sector of volatility spill-over effects, and that the market volatility was successively driven by Technology and Basic Materials over a rather short period of time, followed by Consumer Goods and Consumer Services over a prolonged period of time. Our results illustrate indirect linkages between the sectors that conveyed shocks during the GFC.     

Volatility transmission to the fine wine market

The goal of this paper is to explore volatility transmission from various markets to the fine wine market. Knowledge of these channels for transmitting volatility to the wine market allows practitioners to anticipate the future volatility and the consequences of a shock on the wine market, to develop their investment strategy and diversify their risk. We especially analyse the impact of U.S. markets (i.e. art, commodities, credit, financial and real estate) during the 2007–2017 period. We shed additional light on how the volatility of the fine wine market varies during an extended period including a financial crisis. Our results indicate that, in the short-term, volatility is transmitted with a negative effect through the financial and commodity markets and with a positive effect through the art, residential real estate, and credit default markets. In the long-term, the wine market is impacted by all other markets. We show that correlations are time-varying.     

Translating financial integration into correlation risk: A weekly reporting's viewpoint for the volatility behavior of stock markets

We apply the multivariate extension of GARCH-type models in order to assess the systematic and systemic risks as well as the joint volatility behaviors of the U.S. and three European financial markets (Andersen et al., 2010). Therefore, we can appraise the co-movements of the four previous financial markets as well as the joint behavior of their respective volatilities (i.e. systemic risk). Moreover, the resulting conditional variance and covariance metrics allow for handling volatility spillovers (i.e. contagion risk in terms of transmitting volatility shocks from one market place to another market place). Indeed, results highlight the unprecedented high systemic risk levels (i.e. joint increased volatility levels) as well as a high contagion risk (i.e. volatility spillover) during the subprime mortgage market crisis. The transmission process of volatility shocks reveals to be simultaneous across financial markets due to a strong arbitrage activity and electronic trading practices among others. Most importantly, the estimated conditional correlations exhibit an upward sloping trend, which underlines an increase in the correlation risk between financial markets in the late nineties or early 2000. Thus, our major findings are twofold. First, we characterize the dynamic correlation risk across financial markets. Second, we also confirm the increasing and nonlinear trend in the correlation risk, which we are able to quantify.     

Asymmetric volatility spillovers between the U.K. regional worker flows and vacancies

This article investigates volatility spillovers between the U.K. regional job finding, job separation and vacancy rates. Employing a large Bayesian logistic smooth transition vector autoregression model, we find high volatility spillovers between the U.K. regional labour markets. Analyses of net spillovers show that, in general, shocks to job separation rates tend to spread into job finding and vacancy rates, while vacancy rates are usually at the receiving end of shocks transmitted from the job separations and job findings. To shed further light on the shock propagation mechanism, we also look into more detailed matters such as the differences in spillovers between regions within the same regime, and that of the same region but in different regimes.     

Quantile spillovers and dependence between Bitcoin,equities and strategic commodities

This paper has two aims. We first examine the dynamic spillovers between Bitcoin and 12 developed equities, gold, and crude oil for different market conditions using a Bayesian Time-Varying Parameter Vector Autoregressive (TVP-VAR) model with daily spot prices. Our econometric approach enables us to capture the left and right tails as well as the shoulders of the return distribution corresponding to volatility spillovers under the bear, normal, and bull market states among these financial assets. We quantify and trace the dependence and directional predictability from Bitcoin to other assets using the sample cross-quantilogram. Our key findings offer convincing evidence of time variation in the level of volatility. Spillovers between Bitcoin and other financial assets intensify during extreme global market conditions. Secondly, results from the cross-quantilogram indicate strong dependence and positive directional predictability between Bitcoin and most equities and crude oil when market returns are bullish. However, during the bearish market period, there is negative dependence and predictability from Bitcoin to stocks in Finland, the Netherlands, the U.S.A, and the crude oil market only. This implies that Bitcoin can act as a hedge to stocks in Finland, the Netherlands, the U.S.A, and the crude oil market. However, insignificant dependence and directional predictability from Bitcoin to the remaining assets indicate that Bitcoin may act as a safe-haven to these assets during bearish markets. Our findings hold important implications for both international investors and portfolio managers who consider Bitcoin as part of their portfolio diversification and other investment strategies.     

Impacts of China's crash on Asia-Pacific financial integration: Volatility interdependence,information transmission and market co-movement

This paper examines the price and volatility dynamics between China and major stock markets in the Asia-Pacific, investigating the effects of the Chinese stock market crash (2015–2016) for the first time. Employing the Bayesian VAR and BEKK GARCH, we observe that price and volatility spillover behaviours are different during the stable and stress periods. Particularly, price spillovers from China to other regional markets are more significant during a bullish period, showing that ‘good news’ emanating from China has strong impacts on its neighbours during better market condition. In the turbulent period, we observe strong shock spillover effects and enhanced volatility spillovers from China to most Asia-Pacific stock markets. This is because China, as an important trading partner and strategic financial centre shows to exert significant influence on the Asia-Pacific region through various economic channels. We also find that the Asia-Pacific stock markets spill over their shocks to China during the crisis, indicating that China is becoming more integrated with the regional financial markets.     

中美股市杠杆效应与波动溢出效应——基于GARCH模型的实证分析

本文基于极大似然函数值准则和赤池信息准则,从众多非对称GARCH模型中选择最优模型来研究中美股市杠杆效应和波动溢出效应。结果表明:沪市和深市都表现出显著的杠杆效应,与美国股市相比沪市和深市杠杆效应较弱;沪市和深市之间存在显著的双向波动溢出效应,且沪市对深市的波动溢出效应更显著;美国股市与中国股市之间不存在显著的波动溢出效应。     

期货市场与现货市场之间的价格研究--中国农产品市场的经验

本文借助于信息共享模型与波动溢出效应模型对我国大豆和小麦的期、现货市场之间的价格发现进行了多层次的实证研究,定量描述了期、现货市场在价格发现中作用的大小,深入刻画了我国农产品期、现货市场之间的动态关系.研究结果显示:大豆期、现货价格之间存在双向引导关系,小麦仅存在期货对现货的单向引导关系;期、现货市场均扮演着重要的价格发现角色,且期货市场在价格发现中处于主导地位;期、现货市场之间均存在双向波动溢出关系,但现货市场来自期货市场的波动溢出效应均强于期货市场来自现货市场的波动溢出效应;并且,随着期货市场的发展,期、现货市场之间的波动溢出程度均呈逐渐增强态势.     

Return and volatility spillovers between china and world oil markets

We examine return and volatility spillovers between China and world oil markets. This topic is of great importance because China is the world's second-largest oil importer and has exhibited substantial growth in oil consumption. Extending Diebold and Yilmaz's (2012) method of catching spillover dynamics, it is found that return and volatility spillovers between China and world oil markets are bi-directional and asymmetric. The Chinese oil market is highly affected by world oil markets and exerts an influence on world oil markets, although to a lesser extent. Moreover, the volatility spillover index has increased significantly since the peak of the last financial crisis in September 2008. Although the US oil market impacts China's market most in terms of spillover, the influence of China's oil market on the world oil market has intensified in recent years.     

Financialization of agricultural commodities: Evidence from China

Speculation in the commodity futures market distorts commodity prices, driving them away from rational levels. This phenomenon, which is known as the financialization of commodities, has raised significant concerns in recent years. Particularly, in the agricultural market, ‘financialized’ commodities have been blamed for high world food prices. In this paper, we examine the financialization of agricultural commodities in China. To do so, a time-varying copula is employed to investigate the dependence structure between commodities and stock markets. Four insightful results are obtained. First, positive correlations between agricultural commodities and stock markets demonstrate the financialization of agricultural commodities. Second, the identified correlations are time-varying and idiosyncratic with respect to products. Third, the agricultural commodity market is more closely correlated with the domestic stock market than with the overseas market. Fourth, a growing dependence between commodities and the stock markets is detected and the co-movement became stronger after the global financial crisis.     

Price volatility spillovers between supply chain and innovation of financial pledges in China

This study investigates the price volatility spillover of commodities in China using the Granger causality test and the variable structure Copula model, by employing data covering the daily average transaction prices of silver, copper, aluminum, rebar, and fuel oil from 2009 to 2017. The results show that the causal direction of price volatility spillover is uncertain, and that the impact from the correlation coefficients of copper and aluminum is the highest. Hence, there is a clear long-run price volatility spillover between silver and fuel oil as well as between silver and aluminum. Macroeconomic influences on different combinations of commodities present similar changing structure points, such as the debt crisis in Europe, the reduction of the quantitative easing monetary policy proposed by the U.S. Federal Reserve, and the promotion of RMB internationalization. Overall, our findings provide a theoretical reference for commercial banks to make full use of the volatility spillover effect between commodity pledges and their combinations, in order to achieve the goal of avoiding any price volatility risk.     

Policy modeling on the GDP spillovers of carbon abatement policies between China and the United States

This paper simulates the GDP spillover effects between China and U.S. caused by the implementation of different climate protection policies. It is based on a combination of several climate protection models, which are the State-contingent Model and the Demeter Model, and the GDP Spillovers Model, known as the Mundell–Fleming model. From the simulation results, it is concluded that whether the United States implements policies on increasing carbon sink or not makes very little difference on the total output in the U.S. and the GDP spillovers toward China. However, the spillover impact of American carbon abatement policies on China experiences a varying trend that rises from negative to positive. These simulation results show that the climate protection policies of one country will have positive impact on the GDP spillovers of another country in the long term. This paper is focused on two conditions while simulating the GDP for both China and the U.S. Condition A within the simulations ignores the impact of GDP spillovers of foreign countries, while condition B takes the impact of GDP spillovers of foreign countries into consideration. Furthermore, this paper presents the simulated GDP of China and the U.S. under different scenarios and analyzes the level of GDP spillovers between the two countries. This paper concludes that carbon abatement policies in the U.S. have a larger and more noticeable GDP spillover effects to China.     

Jumps and volatility dynamics in agricultural commodity spot prices

The spot commodities market exhibits both extreme volatility and price spikes, which lead to heavy-tailed distributions of price change and autocorrelation. This article uses various Lévy jump models to capture these features in a panel of agricultural commodities observed between January 1990 and February 2014. The results show that Levy jump models outperform the continuous Gaussian model. Our results prove that assuming a constant volatility or even a deterministic volatility and drift structure of agricultural commodity spot prices is not realistic and is less efficient than the stochastic assumption. The findings demonstrate an interesting correlation between volatility and jumps for a given commodity i, but no relationship between the volatility of commodity i and the probability of jumps of commodity j.     

Assessing volatility transmission between Brent and stocks in the major global oil producers and consumers – the multiscale robust quantile regression

This paper investigates the volatility transmission effect between Brent oil futures and stock markets in the major global oil producing and consuming countries – the U.S., Russia, China and Saudi Arabia. In that process, we employ a mixture of novel and elaborate methodologies – wavelet signal decomposing procedure, GARCH model with complex distribution and recently developed robust quantile regression. Our results indicate that the effect is stronger in short-term horizon than in midterm and long-term in most cases. The magnitude is much stronger in turbulent times, whereas in tranquil times, this effect is very weak. We find that Russian RTS index endures the strongest volatility transmission effect from oil market. Surprisingly, Saudi stock market does not suffer heavy spillover effect even in the periods of increased market unrest. In the U.S. and China, the effect is much stronger from stocks to oil than vice-versa, and this particularly applies for the U.S. case.

     

Volatility spillovers of unconventional monetary policy to emerging market economies

Emerging market economies (EMEs) have experienced waves of market volatility since the global financial crisis, with some commentators ascribing this at least partly related to monetary policy decisions in advanced economies. This paper examines volatility spillovers from changes in the size of the balance sheets of the Federal Reserve (FED) and European Central Bank (ECB) to EMEs from 2003 to 2018. We find that volatility spillovers to EME currency markets are greater in magnitude from the FED, while EME stock and bond markets are also vulnerable to volatility spillovers in a similar magnitude from both the ECB and the FED. We find only limited evidence of volatility transmission to the real economy of EMEs following the monetary policy actions of the FED and ECB. Finally, we show that the proportion of the volatility in EMEs that is accounted for by changes in FED and ECB balance sheets shifts over time. Our paper has important policy implications for EMEs, notably in respect of volatility transmission channels.     

Volatility spillovers across daytime and overnight information between China and world equity markets

This article explores the transmission of daytime and overnight information in terms of returns and volatility between Chinese and Asian, European and North American main stock markets. We propose a bivariate analysis with China as benchmark. By testing the constancy of the conditional correlations, we use an extended constant or dynamic conditional correlation GARCH model. The empirical findings show that across the daytime information transmissions, the relationships between China and Asian markets are closer than China and non-Asian markets, whereas through the overnight information transmissions these relationships are inverse. The analysis provides, before the crisis, that the overnight volatility spillover effects are from China to the United States and the United Kingdom. During the crisis, China affects the United Kingdom in terms of daytime volatility spillovers, whereas in terms of overnight volatility spillovers China affects the United States and is influenced by Japan. After the crisis, daytime volatility spillovers are from Taiwan to China, whereas the overnight volatility spillover effects are from China to the United States and the United Kingdom.     

Dynamic causality between the U.S. stock market,the Chinese stock market and the global gold market: implications for individual investors’ diversification strategies

This paper proposes a generalization of the prior VAR and EGARCH model to explore the linkage between returns and volatility transmissions in the U.S. stock market, the Chinese stock market, and the global gold market from 10 July 1996 to 20 July 2018. We found that past returns of the U.S. stock market can predict the current returns of the other two markets, and that significant reciprocal volatility transmission existed within and across all three markets. We further implemented average out-of-sample (OOS) forecasting to show that a risk-adjusted portfolio, such as mean-variance with sample estimator, does not outperform an equal-weighted portfolio. This provides insights for individual investors and helps to explain the ongoing disagreement in the portfolio literature concerning the effectiveness of risk-adjusted portfolios and equal-weighted portfolios when the number of assets is small.     

Measuring the effectiveness of non-price export promotion using a supply-side approach

In this paper we assess the effectiveness of the Market Promotion Program (MPP) in increasing U.S. exports and benefitting U.S. agricultural producers and food processors. Export shipments are linked to producer welfare using Kohli's (1978) profit maximization (GNP function) approach to modeling international trade. Using estimated profit functions in conjunction with a synthetic export demand function for processed agricultural products, we compute changes in farm and food processing sector profits that result from alternative own-price and advertising elasticities of export demand with and without the MPP subsidy. This approach allows us to investigate aggregate welfare effects of nonprice promotion without requiring the difficult task of estimating the export demand effects of market promotion activities for numerous commodities and importing countries. First version received: April 1999/Final version received: June 2000