Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Frontiers Media SA  

This study explores the relationship between ESG investments and carbon emissions in China. Our results show that 1% increase in environmental investments would cause 0.246% decrease in CO₂ emissions and 0.558% decrease in carbon emission intensity. The impact of ESG investment is heterogeneous across the developed and underdeveloped regions. Environmental investments in the advanced eastern region have significantly improved carbon productivity. In contrast, environmental investments in the central and western regions significantly reduced carbon emissions, but they have little impact on carbon productivity.

DOI： 10.3389/fenvs.2022.977049

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Authorship：Corresponding author   Language：English   Publisher：FRONTIERS MEDIA SA  

This study investigates whether the El Nino Southern Oscillation (ENSO) affects primary commodity prices over time. We employ a wavelet approach that allows us to disentangle the time and frequency domains and to uncover time-varying nonlinear relationships at different frequency levels. Moreover, we adopt partial wavelet coherence (PWC) and eliminate macroeconomic effects on commodity prices. We observe that ENSO is associated with agricultural, food, and raw material commodity prices at lower frequencies of 32-64 and 64-128 months. These results are stronger from 2000 onward, which are not observed using a conventional wavelet method. Our results suggest a recent strong relationship between ENSO and commodity prices, which has important implications for policymakers regarding climate change risk.

DOI： 10.3389/fenvs.2022.893879

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Language：English   Publisher：ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD  

The COVID-19 pandemic has caused stock market crashes and collapse of economic activities in many countries. As a result, many investors changed their stock and bond market expectations. This study investigates whether the number of COVID-19 confirmed cases influences the forward-looking stock-bond correlations. We apply a quantile approach that is beneficial to explore non-linear relationships between the forward-looking stock-bond return correlations and the COVID-19 cases. The correlations are estimated using the DCC-GARCH model for 21 financial markets from three regions (North American, Asia-Pacific, and Europe). We present empirical evidence that there are heterogeneous responses across regions and countries. Specifically, the negative stock-bond correlations weaken as the number of COVID-19 cases in the regions of North America (the U.S. and Canada) and Asia-Pacific (Australia and Japan) increases. Our results suggest that the number of COVID-19 cases is not important. Investors sell risky stocks and buy safe Treasury bonds at the beginning of the pandemic, while they adjust their portfolios risk levels when they obtain more information. Our result also highlights that this pattern is not observed in European countries.

DOI： 10.1080/13504851.2021.1985060

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Other Link： https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-19K13738/

Language：English   Publishing type：Research paper (scientific journal)   Publisher：PHYSICA-VERLAG GMBH & CO  

This study examines the co-movement and causality relationship between prices of crude oil, precious metals, and agricultural commodities. We use a novel approach called wavelet coherence analysis, which allows the measurement of co-movements in the time-frequency space based on the daily prices of commodities. We decompose data from September 1986 to September 2017 into 12 levels and 5 subperiods to find more generalized and convincing results. We confirm that commodity prices are in-phase and co-move. Particularly, the coherence is the largest in the long term and rises sharply in the mid-term during the crisis period. The heterogeneous directions of arrows provide strong evidence that the causality relationship between commodity prices varies over time for different frequencies. We find that the mixed commodities portfolio can provide diversification benefits in the mid-term horizons. The findings of this study can guide investors who want to benefit from diversification while investing in commodity markets.

DOI： 10.1007/s00181-018-1551-3

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：{MDPI} {AG}  

© 2020 by the authors. This paper examines the dynamic dependence structure of crude oil and East Asian stock markets at multiple frequencies using wavelet and copulas. We also investigate risk management implications and diversification benefits of oil-stock portfolios by calculating and comparing risk and tail risk hedging performance. Our results provide strong evidence of time-varying dependence and asymmetric tail dependence between crude oil and East Asian stock markets at different frequencies. The level and fluctuation of their dependencies increase as time scale increases. Furthermore, we find the time-varying hedging benefits differ at investment horizons and reduced over the long run. Our results suggest that crude oil could be used as a hedge and safe haven against East Asian stock markets, especially in the short- and mid-term.

DOI： 10.3390/en13020294

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Other Link： http://www.lib.kobe-u.ac.jp/handle_kernel/90007002

Language：English   Publisher：WILEY  

© 2020 John Wiley & Sons, Ltd. We propose moving average threshold heterogeneous autoregressive (MAT-HAR) models as a novel combination of heterogeneous autoregression (HAR) and threshold autoregression (TAR). The MAT-HAR has multiple groups of lags of a target series, and a threshold term can appear in each group. The threshold is a moving average of lagged target series, which guarantees time-varying thresholds and simple estimation via least squares. We show via Monte Carlo simulations that the MAT-HAR has sharp in-sample and out-of-sample performance. An empirical application on the industrial production of Japan suggests that significant threshold effects exist, and the MAT-HAR has a higher forecast accuracy than the HAR.

DOI： 10.1002/for.2671

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Other Link： https://onlinelibrary.wiley.com/doi/full-xml/10.1002/for.2671

Language：English   Publishing type：Research paper (scientific journal)   Publisher：{MDPI} {AG}  

This paper proposes a novel approach, based on convolutional neural network (CNN) models, that forecasts the short-term crude oil futures prices with good performance. In our study, we confirm that artificial intelligence (AI)-based deep-learning approaches can provide more accurate forecasts of short-term oil prices than those of the benchmark Naive Forecast (NF) model. We also provide strong evidence that CNN models with matrix inputs are better at short-term prediction than neural network (NN) models with single-vector input, which indicates that strengthening the dependence of inputs and providing more useful information can improve short-term forecasting performance.

DOI： 10.3390/jrfm12010009

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：MDPI  

Housing prices in China have been rising rapidly in recent years, which is a cause for concern for China's housing market. Does bank credit influence housing prices? If so, how? Will the housing prices affect the bank credit system if the market collapses? We aim to study the dynamic relationship between housing prices and bank credit in China from the second quarter of 2005 to the fourth quarter of 2017 by using a time-varying parameter vector autoregression (VAR) model with stochastic volatility. Furthermore, we study the relationships between housing prices and housing loans on the demand side and real estate development loans on the supply side, separately. Finally, we obtain several findings. First, the relationship between housing prices and bank credit shows significant time-varying features; second, the mutual effects of housing prices and bank credit vary between the demand side and supply side; third, influences of housing prices on all kinds of bank credit are stronger than influences in the opposite direction.

DOI： 10.3390/jrfm11040090

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：{MDPI} {AG}  

We study the dependence structure of share price returns among the Beijing Bank, Ningbo Bank, and Nanjing Bank using copula models. We use the normal, Student's t, rotated Gumbel, and symmetrized Joe-Clayton (SJC) copula models to estimate the underlying dependence structure in two periods: one covering the global financial crisis and the other covering the domestic share market crash in China. We show that Beijing Bank is less dependent on the other two city banks than Nanjing Bank, which is dependent on the other two in share price extreme returns. We also observe a major decrease of dependency from 2007 to 2018 in three one-to-one dependence structures. Interestingly, contrary to recent literatures, Ningbo Bank and Nanjing Bank tend to be more dependent on each other in positive returns than in negative returns during the past decade. We also show the dynamic dependence structures among three city banks using time-varying copula.

DOI： 10.3390/jrfm11040057

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：ELSEVIER SCIENCE INC  

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.

DOI： 10.1016/j.najef.2017.12.003

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

This paper proposes a novel approach based on a supervised Generative Adversarial Networks (GANs) model that forecasts the crude oil prices with Adaptive Scales Continuous Wavelet Transform (AS-CWT). In our study, we first confirmed that the possibility of using Continuous Wavelet Transform (CWT) to decompose an oil price series into various components, such as the sequence of days, weeks, months and years, so that the decomposed new time series can be used as inputs for a deep-learning (DL) training model. Second, we find that applying the proposed adaptive scales in the CWT method can strengthen the dependence of inputs and provide more useful information, which can improve the forecasting performance. Finally, we use the supervised GANs model as a training model, which can provide more accurate forecasts than those of the naive forecast (NF) model and other nonlinear models, such as Neural Networks (NNs), and Deep Belief Networks (DBNs) when dealing with a limited amount of oil prices data.

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：ELSEVIER SCIENCE BV  

This paper examines the interdependence and causality relationship between oil and East Asian stock returns from 1992 to 2015 and provides a fresh perspective on portfolio diversification benefits using wavelet coherence analysis. We find that oil prices and the East Asian stock market move in phase, and oil prices lead to stock returns in the long run. We provide evidence that oil can reduce the risk in the short run, and the degree of risk reduction of oil-stock portfolios decreases over the long term. This study provides information that can guide investors in diversification efforts while investing in oil and East Asian stock markets. (C) 2017 Elsevier B.V. All rights reserved.

DOI： 10.1016/j.intfin.2017.02.001

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：ELSEVIER SCIENCE BV  

We contribute to the literature on the co-movement between the crude oil price and the exchange rate markets by studying their dynamics in the time and frequency domain. Employing the Wavelet coherence framework, we find that the degree of co-movement between the crude oil price and the exchange rates deviates over time. Additionally, we find strong but not homogenous links around the year 2008 for all the countries included in the study and from 2005 onwards for the oil-exporting countries. However, the strong interdependence area is limited for the oil-importing countries. Moreover, we observe a negative relationship between the returns of the crude oil price and the exchange rates for the oil-exporting countries, while the relationships for the oil-importing countries are uncertain. Our results present new and interesting implications for investors and policy makers.

DOI： 10.1016/j.iref.2017.03.015

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD  

This study investigates the dynamic conditional correlations (DCCs) between eight emerging East Asian stock markets and the US stock market and analyses the dynamic equicorrelation among these nine stock markets. We find a significant increase in the conditional correlations and equicorrelation in the first phase of the global financial crisis. We refer to this finding as contagion from the US stock market to the emerging East Asian markets. We also find an additional significant process of increasing correlations and equicorrelation (herding) in the second phase of the global financial crisis. Further, we employ two new models, namely DCCX-MGARCH (a DCC Multivariate GARCH model with exogenous variables) and DECOX-MGARCH (a dynamic equicorrelation multivariate GARCH model with exogenous variables), to identify the channels of contagion. We find that an increase in the VIX Index increases the conditional correlations and equicorrelation, while increases in TED spreads decrease the conditional correlations of six emerging East Asian countries with the USA. We compare the accuracy of the conditional correlation estimates of the DCC and DCCX models (or DECO and DECOX models) by constructing a loss function. We find that the DCCX (DECOX) model provides more accurate conditional correlation estimates than the DCC (DECO) model by extracting additional information from exogenous variables.

DOI： 10.1080/00036846.2016.1145349

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：ELSEVIER SCIENCE BV  

Based on the wavelet decomposition approach, we study co-movement among foreign exchange markets using the returns of exchange rates (GBP/USD, EUR/USD, and JPY/USD). We focus on the interdependence among returns of exchange rates during the recent global financial crisis and European debt crisis. We use a wavelet analysis because of its ability to decompose signals into high and low frequencies. This approach allows us to study shorter time periods independently of longer time periods. The results reveal strong interdependence between the euro and pound sterling at all frequency bands of scale over the sample period. With regard to the yen pound pairwise, covariation is localized at high scales. Further, we find that interdependence is more pronounced during crises. (c) 2016 Elsevier B.V. All rights reserved.

DOI： 10.1016/j.econmod.2016.01.022

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Publishing type：Part of collection (book)   Publisher：Financial Linkages, Remittances, and Resource Dependence in East Asia  

DOI： 10.1142/9789814713405_0004

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Other Link： http://orcid.org/0000-0003-1498-0188

Language：English   Publishing type：Research paper (scientific journal)   Publisher：ELSEVIER SCIENCE BV  

This study investigates dependence structures among international stock markets, including developed, emerging, and frontier markets, using the hierarchical Archimedean copula model. Empirical results indicate that emerging markets show the strongest dependence with European markets. Frontier markets show the weakest dependence with other market. After the global financial crisis, the lower dependence structure among the international stock markets has changed. Negative news have a larger impact on the degree of dependence than positive news. Contagion effect is observed in both the global financial crisis and the EU debt crisis. (C) 2015 Elsevier B.V. All rights reserved.

DOI： 10.1016/j.econmod.2015.08.017

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