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.
Basher, S. and Sadorsky, P. (2016). "Hedging Emerging Market Stock Prices with Oil, Gold, VIX, and Bonds: A Comparison Between DCC, ADCC and GO-GARCH", Energy Economics, 54, 235-247.
AbstractWhile much research uses multivariate GARCH to model volatility dynamics and risk measures, one particular type of multivariate GARCH model, GO-GARCH, has been underutilized. This paper uses DCC, ADCC and GO-GARCH to model volatilities and conditional correlations between emerging market stock prices, oil prices, VIX, gold prices and bond prices. A rolling window analysis is used to construct out-of-sample one-step-ahead forecasts of dynamic conditional correlations and optimal hedge ratios. In most of the situations we study, oil is the best asset to hedge emerging market stock prices. Hedge ratios from the ADCC model are preferred (most effective) for hedging emerging market stock prices with oil, VIX, or bonds. Hedge ratios estimated from the GO-GARCH are most effective for hedging emerging market stock prices with gold in some instances. These results are reasonably robust to choice of model refits, forecast length and distributional assumptions.
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. (2014). "Modeling Volatility And Conditional Correlations Between Socially Responsible Investments, Gold and Oil", Economic Modelling, 38, 609-618.
AbstractSocially responsible investing (SRI) is one of the fastest growing areas of investing. While there is a considerable literature comparing SRI to various benchmarks, very little is known about the volatility dynamics of socially responsible investing. In this paper, multivariate GARCH models are used to model volatilities and conditional correlations between a stock price index comprised of socially responsible companies, oil prices, and gold 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. From a risk management perspective, SRI offers very similar results in terms of dynamic conditional correlations, hedge ratios, and optimal portfolio weights as investing in the S&P 500. For example, SRI investors can expect to pay a similar amount to hedge their investment with oil or gold as investors in the S&P 500 would pay. These results can help investors and portfolio managers make more informed investment decisions.
Sadorsky, P. (2012). "Modeling Renewable Energy Company Risk", Energy Policy, 40, 39-48.
AbstractThe renewable energy sector is one of the fastest growing components of the energy industry and along with this increased demand for renewable energy there has been an increase in investing and financing activities. The tradeoff between risk and return in the renewable energy sector is, however, precarious. Renewable energy companies are often among the riskiest types of companies to invest in and for this reason it is necessary to have a good understanding of the risk factors. This paper uses a variable beta model to investigate the determinants of renewable energy company risk. The empirical results show that company sales growth has a negative impact on company risk while oil price increases have a positive impact on company risk. When oil price returns are positive and moderate, increases in sales growth can offset the impact of oil price returns and this leads to lower systematic risk.
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.
AbstractIn this paper, multivariate GARCH models are used to model conditional correlations and to analyze the volatility spillovers between oil prices and the stock prices of clean energy companies and technology companies. Four different multivariate GARCH models (BEKK, diagonal, constant conditional correlation, and dynamic conditional correlation) are compared and contrasted. The dynamic conditional correlation model is found to fit the data the best and generates results showing that the stock prices of clean energy companies correlate more highly with technology stock prices than with oil prices. On average, a $1 long position in clean energy companies can be hedged for 20 cents with a short position in the crude oil futures market.
Basher, S., Haug, A. and Sadorsky, P. (2012). "Oil Prices, Exchange Rates and Emerging Stock Markets", Energy Economics, 34(1), 227-240.