Zhige Wu is an Associate Professor of Economics at the College of Economics, Central South University of Forestry & Technology. He also served as a postdoctoral fellow at Jinan University during 2020-2022. His research interests are in financial economics, energy economics, agricultural economics, and applied econometrics, particularly with a primary focus on pricing and hedging of energy commodity assets, energy transition policies and financing strategies, agri-environmental technologies and policies, and structure and performance of agri-food value chains. He has published 20 research articles in Energy Economics, Canadian Journal of Agricultural Economics, Journal of Futures Markets, and Applied Economics, among other journals. He earned his undergraduate degree in International Economics and Trade from the Beijing Institute of Petrochemical Technology, master degrees in Economics from both Hunan University and the University of Calgary, and obtained his Ph.D. in Economics from the University of Guelph.

Featured Publications

The Impact of Local Ethanol Production on the Corn Basis in Ontario

This paper investigates the factors affecting the corn basis in Ontario with particular emphasis on the effect of ethanol production given the projected detrimental effect its expansion could have on the red meat sector. We estimate a location-specific and panel vector error correction models (VECM) for seven elevators in Ontario from 2006 to 2013. We find a long-run equilibrium relationship exists between the basis and factors affecting local supply and demand including ethanol capacity and that the direction of causality is from these factors to changes in corn price. A one-time increase in ethanol capacity of 100 million liters is projected to increase the basis by approximately 30 cents per bushel within two years. However, the impact is insignificant for elevators located in the livestock-intensive regions of the province. The demand for corn as livestock feed is a determinant of the local corn price for all elevators. The decline in the number of hogs and beef cattle along with the 50% increase in corn supply have resulted in the observed decline in the local corn price despite the significant increase in demand from ethanol.

Asymmetric spot‐futures price adjustments in grain markets

Recent volatility in food prices in the grain market has generated much interest among agricultural market participants. This study examines the nonlinear dynamic relationship between spot and futures prices in grain markets. The empirical results provide strong evidence of price asymmetries. The corn spot price adjusts faster to futures price increases than futures price decreases, whereas the soybean spot price adjusts faster to futures price decreases than futures price increases. Although this asymmetric adjustment is found for a single market in Ontario, Canada, the results may also provide insights on the spot‐futures price convergence issues in other commodity markets.

Fuel-feed-livestock price linkages under structural changes

The large-scale diversion of crops into mandates-driven biofuels since early 2000s, has raised concerns about impacts of biofuel policies on food prices. This study examines crude oil-corn-livestock dynamic linkages from January 1987 until December 2019 in Ontario, Canada. A signiffcant structural break is identiffed in March 2011 as biofuel policy impacts become fully implemented and splits the three-decade period into pre- and post-break sub-periods. A nonlinear autoregressive distributed lag (NARDL) approach is employed since it allows prices to be tied by asymmetric relationships both in the short- and long-run. The NARDL model bounds test results indicate that crude oil and corn prices have a long-run connection with livestock prices in both sub-periods. In the post-break period, corn price has an asymmetric effect on cattle price in the long-run, with negative shocks in the corn price leading to a greater intensity on the cattle price than positive shocks. The presence of short-run asymmetry is evident in the impacts of crude oil price on both cattle and hog prices. However, the above asymmetric effect is insigniffcant in the pre-break period.

Do climate risks affect dirty–clean energy stock price dynamic correlations?

Prior studies have extensively exhibited an interest in exploring the connectedness between dirty and clean energy stock prices alongside the drivers of such price connectedness, shedding light on hedging strategies for finance practitioners. Nevertheless, no empirical research has examined whether climate risks, the emerging indicator for investors to handle the divestment of dirty energy stocks, have affected the time-varying dirty–clean energy equity price nexus. This study fflls this gap by innovatively identifying dynamic conditional correlations (DCCs) between dirty and clean energy stock prices. An ARDL/NARDL model is applied to assess whether the climate risks affect such correlations by controlling for business cycles, funding liquidity, USD values, and oil market sentiments. Overall, we detect an undeniable negative impact of climate risks on the positive dirty–clean energy price dynamic correlations. Additionally, the NARDL model results reveal that a rise in federal fund rates exerts higher effects on the dirty–clean energy stock price comovements. Our ffndings suggest the strengthened potential of hedging clean energy stocks against dirty energy equities in case of escalating climate risks and heightened fossil fuel price volatilities. Furthermore, substantial attention is required to account for monetary policies’ asymmetric effects on clean energy investment.