Expected inflation and U.S. stock sector indices: A dynamic time-scale tale from inflationary and deflationary crisis periods

This paper analyses the co-movement between changes in expected inflation and U.S. stock sector returns utilizing a wavelet local multiple correlation approach, which records temporal evolution and potential correlation dynamics at various frequencies. Using daily data from January 2, 2003 to December 30, 2022, we find insignificant correlations in the short term but heterogeneous correlations in longer time periods. After the deflationary GFC period, quantitative easing has turned the long-term correlation negative in some sectors, and since COVID-19, the correlation has been positive. However, energy and materials are pro-inflation sectors in the medium and long term. © 2023 Elsevier Inc.

Higher-order moments and co-moments' contribution to spillover analysis and portfolio risk management

Unlike volatility, the skewness and kurtosis of asset returns are often neglected in the analysis of spillovers and risk management, although they capture the return asymmetry and fat-tailedness, respectively, arising from the non-normality of returns. In this paper, we provide evidence of the relevance and utility of considering spillovers in volatility and higher-order moments (skewness, and kurtosis) and co-moments (covariance, co-skewness, and co-kurtosis), and their implications for hedging. Using high-frequency data on the US stock, crude oil, and gold markets, a time-varying spillover approach and portfolio analysis, we reveal the following results. Firstly, besides volatility and covariance, co-skewness and co-kurtosis are relevant spillover transmitters across the stock, crude oil, and gold markets. Secondly, the level of total spillover increases when including not only covariance but also co-skewness and co-kurtosis, suggesting the relevance of considering higher order co-moments beyond volatility when studying spillovers. Thirdly, the inclusion of co-moments in the spillover analysis generates a significant improvement in hedging for all pairs, which is reflected in the significant increase in the utility function when co-skewness and co-kurtosis are considered. This result is noted when the COVID-19 sub-period is considered separately, except for oil‑gold. Overall, the findings matter for the system of interconnectivity across various assets and emphasize the implications and contributions of higher-order moments and co-moments to portfolio allocation and financial risk management. © 2023 Elsevier B.V.

Linkages between DeFi assets and conventional currencies: Evidence from the COVID-19 pandemic

This paper examines the static and dynamic returns connectedness between four renowned DeFi assets, namely, Chainlink, Maker, Basic Attention Token, and Synthetix, and four internationally important conventional currencies, being they Chinese Yuan, Japanese Yen, Euro, and Pound Sterling. We use the time-varying parameter vector autoregressions framework combined with the connectedness approach based on the generalized forecast error variance decomposition. Our static connectedness analysis evidences a low connection of the DeFi markets with the conventional currency markets. The results of our dynamic analysis reveal that the return spillovers are time-varying, with an abrupt increase in connectedness between the DeFi and currency markets in early 2020, during the initial escalation of the pandemic. However, the spillover from the Chinese Yuan to the system does not exhibit any hike due to the COVID-19-triggered meltdown, highlighting a pandemic-caused decoupling of the Chinese financial system from the other centralized and decentralized markets. We observe unprecedentedly high spillovers from the system to the DeFi markets at the beginning of the pandemic. However, we still find that the DeFi markets act predominantly as net innovation transmitters during the first COVID-19 year. Moreover, we detect the existence of a pairwise-like relationship between the net return spillover profiles and report on inversely symmetric profiles for the Maker - Euro, Basic Attention Token - Japanese Yen, and the Chainlink - Pound Sterling pairs. Given the time-varying transmission-reception patterns for all markets, investors and policymakers can make use of our spillover analysis to improve portfolio allocation and regulatory decisions. © 2022 Elsevier Inc.

Co-movements among precious metals and implications for portfolio management: A multivariate wavelet-based dynamic analysis

This paper investigates the co-movements among precious metals (gold, silver, platinum, and palladium) across time-frequency domains and investment horizons and its implications for dynamic hedging, asset allocation, and utility gains. Based on a multiple wavelet coherence analysis, combined with Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroscedasticity (DCC-GARCH). The uni-directional co-movement is sequential, running from gold to the remaining metals. Silver leads platinum and palladium in returns, and platinum leading palladium. Interestingly, silver and platinum combined contribute to the variability of gold, while gold, silver, and palladium contribute to the variability of platinum, suggesting that some lead-lag relationships are complex and strengthened by multivariate relationships among the metals. The lead-lag dependence is high at medium and long investment horizons and responds to economic, political, and pandemic shocks to the global economy. We find that a wavelet-based dynamic hedging strategy performs better than a conventional hedging strategy. Portfolio weights from the bivariate, three-dimensional, and four-dimensional wavelet analyses vary across investment horizons. Finally, utility gains are higher at long horizons for all multi-dimensional risk strategies, whereas the utility gains are lower with the onslaught of the COVID-19 pandemic. © 2021 Elsevier Ltd

Good and bad high-frequency volatility spillovers among developed and emerging stock markets

Purpose This paper examines dynamic return spillovers and connectedness networks among international stock exchange markets. The authors account for asymmetry by distinguishing between positive and negative returns. Design/methodology/approach This paper employs the spillover index of Diebold and Yilmaz (2012) to measure the volatility spillover index for total, positive and negative volatility. Findings The results show time-varying and asymmetric volatility spillovers among the stock markets under investigation. During the coronavirus disease 2019 (COVID-19) pandemic, bad volatility spillovers are more pronounced and dominated over good volatility spillovers, indicating contagion effects. Originality/value The presence of confirmed COVID-19 cases positively (negatively) affects the good and bad spillovers under low and intermediate (upper) quantiles. Both types of spillovers at various quantiles agree also influenced by the number of COVID-19 deaths.