Time-frequency co-movement and network connectedness between green bond and financial asset markets: Evidence from multiscale TVP-VAR analysis

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 bonds across time scales and negatively co-move with stocks and commodities. Second, there is a significant network connectedness of green bonds with conventional bonds in the short term, and the connectedness with stocks and commodities gradually strengthens with the increase in time scales. Third, the dynamic spillover between green bonds and other assets is much greater in the long and medium terms than in the short term. Finally, under crisis shocks, the spillovers spike temporarily in the short term, while they are persistent and at a high level in the long term. Overall, some practical implications are proposed for investors and policymakers.     

Dependence and risk spillovers among clean cryptocurrencies prices and media environmental attention

This paper examines the relationships among cryptocurrency environmental attention and clean cryptocurrencies prices using Time-Varying Parameter Vector Auto-Regression (TVP-VAR) and wavelets techniques. Results show strong connectedness among these variables, implying that the prices of clean cryptocurrencies are influenced by attention on cryptocurrency sustainability. Connectedness is stronger with positive shocks on environmental attention than negative shocks. Also, in the short-term, clean cryptocurrencies prices lead environmental attention, especially after 2021. However, there are notable periods when environmental attention led clean cryptocurrency prices before 2021. In the long-term, clean cryptocurrencies such as Hedera, Polygon, Cosmos, IOTA, TRON, Stellar, Tezos and Ripple lead environmental attention. In the presence of bitcoin, the degrees of connectedness increased across both shocks on cryptocurrency environmental attention. In all cases, the bitcoin market is the main destination of shocks from the system. We highlight some crucial implications of these results.     

The intermediating role of the Chinese renminbi in Asian currency markets: Evidence from partial wavelet coherence

This study presents an innovative perspective on the dynamic interdependence of Asian currency markets, focusing particularly on the intermediating role of the Chinese renminbi (CNY) in introducing the co-movement between non-major Asian currencies. In this regard, the multivariate factor stochastic volatility (SV) model is estimated and continuous wavelet analysis is applied. The novelty of this study is that it employs wavelet coherence analysis to identify the localized time-varying co-movement of Asian currencies and their lead–lag relations specific to a particular scale and thus investment horizon. Furthermore, the CNY’s intermediating role in inducing co-movement between Asian currencies is examined by applying dynamic partial correlation analysis based on the multivariate factor SV model and partial wavelet coherence analysis, which evaluate the degree of the co-movement between Asian currencies after controlling for the common influence of the CNY. The results clearly indicate the prominent role of the CNY in facilitating region-wide connectedness of Asian currency markets.     

Multiscale features of extreme risk spillover networks among global stock markets

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 networks to visualize risk spillovers at different scales. Our findings show that the US and the UK are detected as the centers of risk spillovers, while Asian stock markets are mainly at the edge of the risk spillover network. The topological properties are unevenly spread over each time scale. The network tends to be closer not only at the short-term scale but also during the financial crisis. For individual features, the US and the UK are super-spreaders of risk spillover at each time scale, while most developing markets mainly act as absorbers. The role of European stock markets is complex at different scales.     

Is the cryptocurrency market a hedge against stock market risk? A Wavelet and GARCH approach

This study analyses six major cryptocurrencies and four global stock markets to explore the role of cryptocurrencies as a hedge, safe haven, and diversifier in stock markets. The study employs ADCC-GARCH and Wavelet Coherence Technique, using daily data from 4 January 2017 to 28 February 2023. The study has found that stock returns and unstable cryptocurrency returns have high volatility persistence in the long run. Besides, while unstable digital currencies (Bitcoin, Ethereum, Binance Coin, and Dogecoin) serve as a hedge during stable economic periods, they have not been a hedge during economic turmoil in the stock markets. Conversely, stablecoins (Tether and USD Coin) have been shown to have acted as a hedge during normal economic times and have offered a safe haven during economic downturns. Except for Tether, all cryptocurrencies' diversification capacity is time-varying. In stable economic conditions, they serve as diversifiers, but during turmoil, they do not. However, Tether serves as a diversifier regardless of the financial situation. Finally, the present investigation is expected to offer crucial information on hedge, safe haven and diversification for quasi-investors.     

The COVID-19 pandemic uncertainty,investor sentiment,and global equity markets: Evidence from the time-frequency co-movements

We use daily data of the Google search engine volume index (GSVI) to capture the pandemic uncertainty and examine its effect on stock market activity (return, volatility, and illiquidity) of major world economies while controlling the effect of the Financial and Economic Attitudes Revealed by Search (FEARS) sentiment index. We use a time–frequency based wavelet approach comprising wavelet coherence and phase difference for our empirical assessment. During the early spread of the COVID-19, our results suggest that pandemic uncertainty, and FEARS sentiment strongly co-move, and increased pandemic uncertainty leads to pessimistic investor sentiment. Furthermore, our partial wavelet analysis results indicate a synchronization relationship between pandemic uncertainty and stock market activities across G7 countries and the world market. Our results are robust to the inclusion of alternative pandemic fear measure in the form of equity market volatility infectious disease tracker. The pandemic uncertainty and associated sentiment implications could be one plausible reason for increased volatility and illiquidity in the market, and hence, policymakers should look upon this issue for the financial market stability perspective.     

Long memory in the high frequency cryptocurrency markets using fractal connectivity analysis: The impact of COVID-19

In this paper, we study the long memory behavior of the hourly cryptocurrency returns during the COVID-19 pandemic period. Initially, we apply different tests against the spurious long memory, with the results indicating the presence of true long memory for most cryptocurrencies. Yet, using the multivariate test, the series are found to be contaminated by level shifts or smooth trends. Then, we adopt the wavelet-based multivariate long memory approach suggested by Achard and Gannaz (2016) to model their long memory connectivity. The findings indicate a change in persistence for all series during the sample period. The fractal connectivity clustering indicates a similarity among Ethereum (ETH) and Litecoin (LTC), Monero (XMR), Bitcoin (BTC), and EOC token (EOS), while Stellar (XLM) is clustered away from the remaining series, indicating the absence of any interdependence with other crypto returns. Overall, shocks arising from COVID-19 crisis have led to changes in long-run correlation structure.     

Multi-scale systemic risk and spillover networks of commodity markets in the bullish and bearish regimes

This paper proposes a new network topology approach based on the STVAR model to identify asymmetric impacts of market conditions on multi-scale systemic risk spillovers of commodity markets. The results show that bearish market conditions enlarge low-frequency systemic risk spillovers in commodity markets, and bullish market conditions have more striking impacts on high-frequency systemic risk spillovers. Furthermore, the center of risk spillover networks varies across the market conditions and frequencies. Specifically, at the high-frequency level, sugar is the largest risk transmitter in bad regimes, and heating oil is in the center of the network in good regimes. At the intermediate frequency level, soybean becomes a more important risk transmitter in both regimes. In other cases, heating oil is the center of risk spillover networks.