Time-Frequency Connectedness and Extreme Dependencies in Stock Sector Markets of the Chinese and U.S. Economies

Purpose – This study examines the predictability of comparable bivariate sectors in the U.S. and Chinese stock markets, including industries such as healthcare, utilities, telecom, energy, and real estate, during periods of high market turbulence. Additionally, it analyzes the spillover effects between U.S. and Chinese sectors across varying investment time horizons, ranging from short-term to long-term. To provide deeper insights, the study also investigates the dependence structure between the two countries' sectoral stock markets. Design/methodology/approach– This study employs two methodologies to examine both static and dynamic connectedness across short-, medium-, and long-term financial cycles. These methods are the time-varying parameter vector autoregressive frequency connectedness (TVP-VAR-BK) approach proposed by Baruník and Křehlík (2018) and the Cross Quantilogram (CQ) technique. Findings – The results show that the interrelationship among stock sector returns is sensitive to major events, particularly in the short term. Moreover, China’s energy sector is the main contributor to volatility in US industry returns across all time horizons. The US industry sector consistently acts as a net transmitter of shocks to the network regardless of the investment horizon. Interestingly, US sector returns tend to transmit volatilities, while Chinese sector returns are mostly net recipients of shocks in the long term. Finally, according to the cross-quantilogram results, the optimal opportunity for portfolio diversification arises when an investor selects a similar sector from both US and Chinese markets, and the two markets are in opposite return phases (i.e., one bullish, the other bearish). Practical implications – Our findings provide valuable insights for speculators, institutional investors, and policymakers. For equity investors, the results offer practical guidance on portfolio diversification and effective hedging strategies across different market horizons. Additionally, they help investors identify the dependence structure during bearish and bullish market conditions, enabling the classification of assets as diversifiers, hedgers, or safe havens. For policymakers, the findings shed light on the sources of asset contagion, offering critical information to design strategies and reforms aimed at reducing the vulnerability of assets that serve as net shock receivers. Originality/value –Using the methodology developed by Baruník and Křehlík (2018), we examine the size and direction of connectedness across different time horizons (short, medium, and long terms). For robustness, we employ the Cross Quantilogram technique to evaluate the upper and lower dependence between US and Chinese sectors, considering various market conditions (bearish, bullish, and normal scenarios) by analyzing different quantiles.