Huiming Zhu

Hunan University · College of Business Administration

Previous studies focused on the fundamental channels of the interaction between the equity market and credit default swap (CDS) market. This paper finds another channel, investor sentiment, that contributes to the impact of the equity market on the CDS market under different time horizons and market conditions within the framework of wavelet quanti...

This study investigates the frequency-domain causality and quantile connectedness between online investors' fear sentiment and cryptocurrency returns. We propose cross-quantile coherency and networks to examine the frequency-domain nonlinear interdependence. First, we find that investor fear sentiment and cryptocurrency returns exhibit bidirectiona...

This paper investigates the heterogeneous dependence between global crude oil futures and China’s biofuel feedstock commodities under different market conditions. Quantile-on-quantile regression and the causality-in-quantiles test are employed to capture comprehensive and informative relationships. The empirical results are as follows: First, there...

This study examines the time-frequency co-movement and network connectedness between green bonds and other financial assets in China. We propose wavelet coherence and multiscale TVP-VAR to explore the time-frequency co-movement and spillover connectedness. The empirical results are as follows. First, green bonds positively co-move with conventional...

Using macroeconomic and financial conditions to forecast credit default swap (CDS) spreads is a challenging task. In this paper, we propose the Merton-LSTM model, a modified LSTM model formed by integrating with the Merton determinants model, to forecast the CDS indices. We provide the rigorous math behind the Merton-LSTM model, which demonstrates...

This study explores, through the lens of the resource-based view (RBV) and Schumpeter’s innovation theories, the role of innovation in sustainable performance in the context of small and medium-sized enterprises (SMEs) that practice the circular economy (CE). Drawing from the RBV theory, we find that R&D and patents are positively related to sustai...

This study examines the time-frequency extreme risk spillover network among cryptocurrency coins, decentralized finance and non-fungible tokens via wavelet-based quantile causality analysis. We derive the following empirical results. First, long-lived coins dominate cryptocurrencies’ upside and downside risk networks. Second, yield farming tokens e...

This article examines the transmission mechanism of economic policy uncertainty (EPU), investor sentiment and Chinese financial assets from time-frequency and static-dynamic perspectives. The multiscale connectedness method based on time-varying parameter vector autoregression (TVP-VAR) is introduced to explore the time-frequency and static-dynamic...

This article investigates the influencing factors of China’s outward foreign direct investment (OFDI) in RCEP countries across quantiles. We apply a panel quantile regression to seek the factors that influence China’s OFDI in RCEP countries with the most and least OFDI from China. Our findings are outlined as follows. First, influencing factors of...

The focus of this research is to determine the factors that influence individuals' acceptance of electronic banking services offered by Yemeni banks. Therefore, our proposed study has four salient variables: security and privacy, perceived risks and benefits, website usability, and electronic banking awareness. The measurement model was created usi...

This article investigates the time-frequency causality and dependence structure of Chinese industry stock returns on crude oil shocks and China's economic policy uncertainty (EPU) across quantiles over the period from January 2001 to June 2021. We use wavelet-based decomposition series to establish a multiscale causality-in-quantiles test and a qua...

This article investigates the time-frequency connectedness of categorical policy uncertainty, geopolitical risk and Chinese commodity markets by applying the vector autoregression method for the period from August 2004 to March 2021. Specifically, our research employs rolling window analysis of wavelet decomposition series to uncover the dynamic pr...

This study investigates the time-varying interdependence relationships between green bonds and green equity returns in China before and during the COVID-19 period. The rolling-window Copula Quantile-on-Quantile regression method has been employed to capture the dynamic dependence structure of the asset returns. The empirical results are as follows:...

This paper studies the multiscale features of extreme risk spillover among global stock markets over various time-frequency horizons. We propose multiscale risk spillover indexes based on GARCH-EVT-VaR, maximal overlap discrete wavelet transform method, and forecast-error-variance decompositions. We further construct multiscale risk spillover netwo...

This paper employs the wavelet-based quantile method to examine the time and frequency effect of investor sentiment, economic policy uncertainty, and crude oil on emerging and developed stock markets over the monthly sample range from September 2005 to December 2020. We first explore the relationship between various markets, and our empirical resul...

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 var...

This study explores the dependence and causality between rare earth elements prices and trade policy uncertainty of both China and the US at different frequencies and market conditions. To this end, we employ the wavelet analysis and nonparametric causality-in-quantiles tests in the time-frequency domain. Our empirical evidence indicates that the d...

We propose the rolling tail-event driven network technique (RTENET) to measure the dynamic nonlinear tail risk spillover of 20 US commodity futures. In addition, we investigate the effect of economic policy uncertainty (EPU) on risk spillover based on quantile-on-quantile regression (QQR). We find that the risk spillover effect increases sharply an...

This article develops multi-quantile VaR Granger causality to investigate the extreme risk spillover of investor attention to energy-intensive and green enterprises in China. We construct investor attention indices from the Baidu index by crawling each stock’s code and abbreviation. Our findings are outlined as follows. First, investor attention co...

The seven leading industrial countries, called the G7, are becoming a pivotal group to fulfil their emissions-reduction commitments to manage the climate crisis. This paper investigates the relationships between R&D intensity, globalization, and carbon emissions in the context of the G7 countries for the period from 1970 to 2017. Our analysis, whic...

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 inve...

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 r...

This paper explores the heterogeneous impact of R&D on firm growth in Chinese manufacturing industry during the periods 2012–2017. We divide the full sample into sub-samples according to different ownership, firm size and sectors, respectively, which help comprehensive observe the impact of R&D on firm growth. The results indicate that R&D has a si...

This paper investigates the relationship between investor attention and the major cryptocurrency markets by wavelet-based quantile Granger causality. The wavelet analysis illustrates the interdependence between investor attention and the cryptocurrency returns. Multi-scale quantile Granger causality based on wavelet decomposition further demonstrat...

This paper uses the quantile-on-quantile regression to examine the predictive power of transaction activity for Bitcoin returns over the period from January 2013 to December 2018. We measure the Bitcoin transaction activity using trading volumes, the number of unique Bitcoin transactions, and the number of unique Bitcoin addresses. Considering the...

This paper investigates volatility dependence between global crude oil and China's agriculture futures by employing a quantile-on-quantile approach. The time-varying parameter stochastic volatility in mean model is used to evaluate the conditional volatility. The empirical results demonstrate the heterogeneous dependence between crude oil volatilit...

This paper illustrates the direct and indirect effects of democracy on CO2 emissions in the BRICS countries (Brazil, Russia, India, China, and South Africa) from 1992 to 2018. In view of the distribution heterogeneity of CO2 emissions, the panel quantile regression model is especially used to explore the nexus among different variables. Furthermore...

In this article, the quantile time-frequency method is utilized to study the dependence of Chinese commodities on the international financial market. The impacts of risk management and diversification benefits of different portfolios are examined by calculating the reduction in downside risk. Moreover, we estimate and compare Sharpe Ratios (SRs) an...

This article examines the co-movement relationship among representative cryptocurrencies from the perspectives of returns and volatility. Wavelet coherence and the correlation network are introduced to explore the interdependence of cryptocurrencies, and then risk reduction and downside risk reduction are used to test the hedging effects of Bitcoin...

The transport sector is becoming a key sector for China to accomplish its targets for reducing carbon emission intensity (CEI). Identifying the dominant factors driving CEI of the transport sector is important for CEI mitigation. This paper applied dynamic panel quantile regression to explore the effect of driving factors on CEI in the Chinese tran...

This article explores the effects of China’s economic policy uncertainty (EPU) on its fiscal policy, monetary policy and a wide range of macro-economic variables using a time-varying parameter FAVAR model. Based on monthly data from 07/2003 to 08/2017, the time-varying structure of the model allows us to capture the time-varying characteristics of...

This paper investigates the effect of economic policy uncertainty (EPU) on China’s agricultural and metal commodity futures returns across quantiles. We address this issue using the panel quantile regression approach, which allows for a more complete analysis of various conditions in the commodity market (i.e. bearish, normal, and bullish markets)....

This paper explores the impacts of positive and negative changes in crude oil price and exchange rate variables on raw material procurement prices and product ex-factory prices of China’s industrial enterprises. We run the nonlinear autoregressive distributed lag (NARDL) model for the full sample from January 2000 to June 2019, and find the existen...

The causal relationships between spot and futures crude oil prices have attracted the attention of many researchers in the past several decades. Most of the studies, however, do not distinguish among the various oil market situations in analyses of linear and nonlinear causalities. In light of the fact that a booming or depressing oil market produc...

The environmental impacts of foreign direct investment (FDI) and foreign trade have attracted much attention recently. This paper employs panel quantile regression to explore the effects of FDI and foreign trade on Chinese provincial CO 2 emissions for the period of 1997-2014. The results indicate that the effect of FDI on CO 2 emissions is negativ...

This paper explores the dependence between global crude oil and Chinese commodity futures markets across different quantiles of the return distributions. Based on weekly data from 11 June 2004 to 7 July 2017, we address this issue by applying a quantile regression method. This technique provides a more detailed investigation of the dependence. More...

This paper empirically examines the effects of urbanization and income inequality on CO2 emissions in the BRICS economies (i.e., Brazil, Russia, India, China, and South Africa) during the periods 1994–2013. The method we used is the panel quantile regression, which takes into account the unobserved individual heterogeneity and distributional hetero...

This paper investigates the effects of the European Union Emission Allowance (EUA)’s price on European carbon-intensive industries’ stock returns based on daily data from April 25, 2005 to July 24, 2017. Utilizing a multifactor market model specification and a panel quantile regression approach, the study tests whether the influences of the EUA pri...

This paper investigates the extreme risk spillover of international crude oil to stock returns for 529 firms listed on the A-share market of the Shanghai stock exchange. We apply a kernel-based nonparametric method to test quantile-on-quantile Granger causality from crude oil to firm returns. From the perspective of firm-level analysis, the finding...

The rapid growth of housing prices has attracted the attention of the whole of society in China. This article adopts the dynamic panel quantile regression to investigate the impact of income, economic openness and interest rates on housing prices in China, based on the panel data of 35 major cities from 2002 to 2012. Compared with previous studies,...

This paper employs the quantile regression techniques to examine the dependence structure between economic policy uncertainty (EPU) and stock market returns in G7 and BRIC. We find new evidence to support the view that EPU will reduce stock market returns, with the exception of France and the UK. Our results show that eight out of ten stock markets...

This paper investigates the impact of crude oil shocks and China's economic policy uncertainty on stock returns at different locations on the return distributions. Based on monthly time series data from 1995:1 to 2016:3, we address this issue by employing the quantile regression technique. This approach enables a more detailed investigation in diff...

This paper investigates the implications of strategic interaction (i.e., competition) between two CARA insurers on their reinsurance-investment policies. The two insurers are concerned about their terminal wealth and the relative performance measured by the difference in their terminal wealth. The problem of finding optimal policies for both insure...

This paper investigates behaviour of stock price synchronicity to oil shocks across quantiles for Chinese oil firms. The spillover effects of the oil market on a firm are segregated into firm-specific and market-wide information. First, our results report a higher level of synchronicity by dynamic conditional correlations than by R-square since the...

This study investigates the impact of foreign direct investment (FDI), economic growth and energy consumption on carbon emissions in five selected member countries in the Association of South East Asian Nations (ASEAN-5), including Indonesia, Malaysia, the Philippines, Singapore and Thailand. This paper employs a panel quantile regression model tha...

The asymmetric effects of oil price shocks on stock returns have attracted the attention of many researchers in the past several decades. Most of these researchers’ studies, however, do not separate out the sources of oil price shocks when examining the asymmetric effects. In this article, we address this limitation using a two-stage Markov regime-...

This paper uses quantile impulse response approach to investigate the impact of oil price shocks on Chinese stock returns. This allows us to uncover asymmetric effects of oil price shocks on stock market returns by taking into account the different quantiles of oil price shocks. Our results show that the responses of Chinese stock market returns to...

The determinants of exchange rates have attracted considerable attention among researchers over the past several decades. Most studies, however, ignore the possibility that the impact of oil shocks on exchange rates could vary across the exchange rate returns distribution. We employ a quantile regression approach to address this issue. Our results...

This article surveys the asymmetric spillover effects between the mainland China-based Shanghai Composite Index (SCI) and the Hong Kong based Hang Seng Index (HSI) using a quantile lagged regression model. Compared to previous studies, this article, based on data before and after the 2008 global financial crisis, presents a more detailed analysis,...

This paper investigates the quantile behaviour of cointegration between the silver and gold prices by employing the quantile ARDL model. Our empirical results suggest that the existence of cointegration is mainly due to the tail quantiles outside the interquartile range, revealing the quantile-dependent (time-varying) cointegrating coefficients whi...

We study the optimal proportional reinsurance and investment problem in a general jump-diffusion financial market. Assuming that the insurer’s surplus process follows a jump-diffusion process, the insurer can purchase proportional reinsurance from the reinsurer and invest in a risk-free asset and a risky asset, whose price is modelled by a general...

This work investigates an optimal financing and dividend problem for an insurer whose surplus process is modulated by an observable continuous-time and finite-state Markov chain. We assume that the insurer should never go bankrupt by issuing new equity. The goal of the insurer is to maximize the expected present value of the dividends payout minus...

We study the optimal proportional reinsurance and investment problem in a general jump-diffusion financial market. Assuming that the insurer’s surplus process follows a jump-diffusion process, the insurer can purchase proportional reinsurance from the reinsurer and invest in a risk-free asset and a risky asset, whose price is modelled by a general...

This paper explores the dependence between real crude oil price changes and Chinese real industry stock market returns based on the monthly data from 1994/03 to 2014/06. We address this issue using the quantile regression approach, enabling a more detailed investigation of structure and degree of dependence. Empirical results reveal that the reacti...

The asymmetric effects of oil price shocks on stock returns have attracted the attention of many researchers in the past several decades. Most of these researchers' studies, however, do not separate out the sources of oil price shocks when examining the asymmetric effects. In this paper, we address this limitation using a two-stage Markov regime-sw...

This article investigates the relationship between real crude oil price changes and the Chinese real stock market at the industry level. Our study uses monthly data over the period 1994:03 to 2013:12. Based on input–output (IO) tables, this article will explore more details for the driving factors of sensitivity to oil price changes. We divide thes...

This article investigates the relationship between real crude oil price changes and the Chinese real stock market at the industry level. Our study uses monthly data over the period 1994:03 to 2013:12. Based on input–output (IO) tables, this article will explore more details for the driving factors of sensitivity to oil price changes. We divide thes...

The determinants of CO2 emissions have attracted many researchers over the past few decades. Most of studies, however, ignore the possibility that effect of independent variables on CO2 emissions could vary throughout the CO2 emission distribution. We address this issue by applying quantile regression methods. We examine whether greater democracy a...

Because the test power of the traditional panel unit root tests is unstable and the choice of the null hypothesis of traditional panel unit root tests is subjective, this paper proposed a Bayesian quantile unit root test for panel data based on asymmetric Laplace distribution. On the basis of quantile autoregression panel data model, the full condi...

This paper investigates implications of strategic interaction (competition) between two CARA insurers on their reinsurance and investment policies. The two insurers are concerned about their terminal wealth as well as the relative performance measured by the difference between their terminal wealth. The problem of finding optimal policies for the b...

We constructed a Bayesian quantile linear regression model based on Gibbs-DA sampling algorithm for the uncertainty risks of quintile regression model parameters. According to the normal-exponential representation property of asymmetric Laplace distribution, we established a working likelihood function for the quantile regression model with latent...

We focus on the problem of simultaneous variable selection and estimation for nonlinear models based on modal regression (MR), when the number of coefficients diverges with sample size. With appropriate selection of the tuning parameters, the resulting estimator is shown to be consistent and to enjoy the oracle properties.

By combining basis function approximations and smoothly clipped absolute deviation (SCAD) penalty, this paper proposes a robust variable selection procedure for a partially varying coefficient single-index model based on modal regression. The proposed procedure simultaneously selects significant variables in the parametric components and the nonpar...

In order to study the regional capital liquidity, Bayesian panel smooth transition regression models were established to address uncertain risk of parameters estimation in PSTR models. Based on the analysis of model statistic structure and the selection of parameters prior, the Metropolis-Hasting within Gibbs sampling method was utilized to estimat...

A large body of literature studies on the relationship between health care expenditure (HCE) and GDP have been analyzed using data intensively from developed countries, but little is known for other regions. This paper considers a semiparametric panel data analysis for the study of the relationship between per capita HCE and per capita GDP for 42 A...

We focus on the expected discounted penalty function of a compound Poisson risk model with random incomes and potentially delayed claims. It is assumed that each main claim will produce a byclaim with a certain probability and the occurrence of the byclaim may be delayed depending on associated main claim amount. In addition, the premium number pro...

Variable selection is the issue of major concern in practical regressions. This note provides a simple and efficient method to examine the robustness of predictor variables in cross-country economic growth models. Our results confirm the general findings of Sala-i-Martin et al. (2004), indicating the importance of a number of same predictor variabl...

Variable selection is the issue of major concern in practical regressions. This note provides a simple and efficient method to examine the robustness of predictor variables in cross-country economic growth models. Our results confirm the general findings of Sala-i-Martin et al. (2004), indicating the importance of a number of same predictor variabl...

This paper aims to investigate the possible linkage and non-linear interaction between crude oil and stock markets using cointegration and causality tests. Daily data on world crude oil prices and the stock indices of Japan, Taiwan, South Korea, Australia, Indonesia, India, Singapore and Malaysia are selected for this study. Unlike the previous lit...

Bayesian nonparametric methods provide a natural setting for quantile regression, which offers great flexibility in assessing covariate effects on the response. In this paper, a method of estimating conditional quantile functions via Fourier series is proposed from a Bayesian perspective, which involves fewer smoothing parameter selection than that...

To address the problem of modelling under the condition of random parameters using Probit quantile regression, this paper proposed Bayesian Probit quantile regression model based on the Metropolis-Hastings algorithm. According to the Probit quantile structure, the M-H algorithm was ulitized to simulate the posterior marginal distribution by choosin...