JOB MARKET PAPER

“Commodity Price Super-cycle and Dynamic Connectedness”

• Abstract: This study examines long-term cycles in commodity prices and their interconnectedness with macroeconomic activity. A broad set of commodity prices shows evidence of two major super-cycles from 1960 to 2023, which are closely aligned with shifts in global economic conditions. The analysis focuses on long-run relationships among commodity prices and between commodity prices and industrial production, using the Vector Error Correction Model (VECM). To further investigate the dynamics of cyclical behavior in commodity prices, the Diebold and Yilmaz (2012) connectedness framework is applied. The results show that commodity prices tend to move together over the long term, and that their connectedness increases sharply during periods of economic shocks such as oil crises and financial crises. By extracting the super-cyclical component of dynamic connectedness using a rolling sample window and comparing it with the super-cycle component of commodity prices, the study finds strong co-movement between them, suggesting the presence of a “co-super-cycle.” Notably, energy commodities have acted as dominant transmitters of super-cyclical dynamics since the early 2000s. These findings highlight the structural influence of macroeconomic shocks and emphasize the importance of a system-wide approach to commodity market analysis.

WORK IN PROGRESS

“Testing and Identification of Common Trends and Factors in Agricultural Prices” with Barry Goodwin & Daisoon Kim

• Abstract: This study investigates the extent to which common factors drive commodity prices, focusing on the nature and number of common trends in nonstationary price data. Using a dataset of 39 monthly commodity prices from January 1960 to January 2020, the analysis applies recently developed tests for common trends that allow for both stochastic and deterministic components. The decomposition framework separates global, block-specific, and idiosyncratic factors, and relates the global factor to key macroeconomic variables such as industrial production, interest rates, and financial market volatility. The results indicate that variables including the Kilian index and OECD industrial production significantly contribute to the co-movement of commodity prices. Moreover, the presence of common cycles suggests that commodity prices are influenced by broader cyclical forces, highlighting the relevance of global economic and financial conditions for understanding commodity price dynamics. In particular, grains such as soybean, wheat, and corn exhibit the highest sensitivity to the global factor, while oil prices have become increasingly influential in driving commodity price co-movement since the 2000s.

“Semi-Conductors Price Cycle: CPU Price”