Welcome to the new Schulich Peer-Reviewed Publication Database!
The database is currently in beta-testing and will be updated with more features as time goes on. In the meantime, stakeholders are free to explore our faculty’s numerous works. The left-hand panel affords the ability to search by the following:
- Faculty Member’s Name;
- Area of Expertise;
- Whether the Publication is Open-Access (free for public download);
- Journal Name; and
- Date Range.
At present, the database covers publications from 2012 to 2020, but will extend further back in the future. In addition to listing publications, the database includes two types of impact metrics: Altmetrics and Plum. The database will be updated annually with most recent publications from our faculty.
If you have any questions or input, please don’t hesitate to get in touch.
Ahmed, W., Sadorsky, P. and Sharma, A. (2018). "Optimal Hedge Ratios for Clean Energy Equities", Economic Modelling, 72, 278-295.
AbstractClean energy equities represent a relatively new class of assets to invest in, and these assets can be very volatile. An understanding of how investors in clean energy stocks can hedge their investment is essential for risk management. In this study, we use daily data covering the period March 03, 2008 to October 31, 2017, to examine how crude oil, US-bonds, gold, VIX, OVX and European carbon prices can be used to hedge an investment in clean energy equities. We apply three variants of multivariate GARCH models (DCC, ADCC and GO-GARCH) to estimate time-varying optimal hedge ratios. The results suggest that VIX is the best asset to hedge clean energy equities followed by crude oil and OVX. This is a new result relative to the existing literature on clean energy stock prices and one that is of interest to current and future investors in clean energy stocks.
Sadorsky, P. (2014). "Modeling Volatility And Correlations Between Emerging Market Stock Prices and the Prices Of Copper, Oil and Wheat", Energy Economics, 43, 72-81.
AbstractIncreased financial integration between countries and the financialization of commodity markets are providing investors with new ways to diversify their investment portfolios. This paper uses VARMA-AGARCH and DCC-AGARCH models to model volatilities and conditional correlations between emerging market stock prices, copper prices, oil prices and wheat prices. The dynamic conditional correlation model is found to fit the data the best and used to generate dynamic conditional correlations, hedge ratios and optimal portfolio weights. Emerging market stock prices and oil prices display leverage effects where negative residuals tend to increase the variance (conditional volatility) more than positive ones. Correlations between these assets increased considerably after 2008, and have yet to return to their pre 2008 values. On average, oil provides the cheapest hedge for emerging market stock prices while copper is the most expensive but given the variability in the hedge ratios, one should probably not put too much emphasis on average hedge ratios.
Sadorsky, P. (2012). "Correlations And Volatility Spillovers Between Oil Prices and The Stock Prices of Clean Energy and Technology Companies", Energy Economics, 34(1), 248-255.