Exploring Multifractal Asymmetric Detrended Cross-Correlation Behavior in Semiconductor Stocks

This study investigates the multifractal behavior of four leading semiconductor stocks—Intel (INTC), Advanced Micro Devices (AMD), Nvidia (NVDA), and Broadcom (AVGO)—in relation to key financial assets, including the Dow Jones Industrial Average (DJI), the Euro–U.S. Dollar exchange rate (EUR), gold (XAU), crude oil (WTI), and Bitcoin (BTC), using Multifractal Asymmetric Detrended Cross-Correlation Analysis (MF-ADCCA). The analysis is based on daily price return time series from January 2015 to January 2025. Results reveal consistent evidence of multifractality across all asset pairs, with the generalized Hurst exponent exhibiting significant variability, indicative of complex and nonlinear stock price dynamics. Among the semiconductor stocks, NVDA and AVGO exhibit the highest levels of multifractal cross-correlation, particularly with DJI, WTI, and BTC, while AMD consistently shows the lowest, suggesting comparatively more stable behavior. Notably, cross-correlation Hurst exponents with BTC are the highest, reaching approximately 0.54 for NVDA and AMD. Conversely, pairs with EUR display long-term negative correlations, with exponents around 0.46 across all semiconductor stocks. Multifractal spectrum analysis highlights that NVDA and AVGO exhibit broader and more pronounced multifractal characteristics, largely driven by higher fluctuation intensities. Asymmetric cross-correlation analysis reveals that stocks paired with DJI show greater persistence during market downturns, whereas those paired with XAU demonstrate stronger persistence during upward trends. Analysis of multifractality sources using surrogate time series confirms the influence of fat-tailed distributions and temporal linear correlations in most asset pairs, with the exception of WTI, which shows less complex behavior. Overall, the findings underscore the utility of multifractal asymmetric cross-correlation analysis in capturing the intricate dynamics of semiconductor stocks. This approach provides valuable insights for investors and portfolio managers by accounting for the multifaceted and asset-dependent nature of stock behavior under varying market conditions.