Publications Database

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.


Search Results

Henriques, I. and Sadorsky, P. (2018). "Can Bitcoin Replace Gold in an Investment Portfolio?", Journal of Risk and Financial Management, 11(3), 1-19.

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Abstract Bitcoin is an exciting new financial product that may be useful for inclusion in investment portfolios. This paper investigates the implications of replacing gold in an investment portfolio with bitcoin (“digital gold”). Our approach is to use several different multivariate GARCH models (dynamic conditional correlation (DCC), asymmetric DCC (ADCC), generalized orthogonal GARCH (GO-GARCH)) to estimate minimum variance equity portfolios. Both long and short portfolios are considered. An analysis of the economic value shows that risk-averse investors will be willing to pay a high performance fee to switch from a portfolio with gold to a portfolio with bitcoin. These results are robust to the inclusion of trading costs.

Henriques, I. and Sadorsky, P. (2018). "Investor Implications of Divesting from Fossil Fuels", Global Finance Journal, 38, 30-44.

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Abstract There is a growing movement for both individual investors and large institutions to divest from oil companies, and from fossil fuel producers in general. This paper investigates the implications of doing so, by comparing three portfolios: (1) a portfolio that includes fossil fuel producing companies and utilities, (2) a portfolio that replaces fossil fuel producing companies and utilities with clean energy companies, and (3) a portfolio without fossil fuel producing companies, utilities, or clean energy companies. Using a range of measures, we find that portfolios that divest from fossil fuels and utilities and invest in clean energy perform better than those with fossil fuels and utilities. We also find that risk-averse investors would be willing to pay a fee to make this switch, even when trading costs are included.

Sadorsky, P. (2014). "Modeling Volatility And Conditional Correlations Between Socially Responsible Investments, Gold and Oil", Economic Modelling, 38, 609-618.

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Abstract Socially 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. (2014). "Carbon Price Volatility and Financial Risk Management", The Journal of Energy Markets, 7(1), 83-102.

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Abstract Carbon dioxide emissions represent a new traded asset that, in addition to reducing carbon dioxide emissions through cap-and-trade initiatives, can offer financial risk diversification benefits. In this paper, multivariate generalized auto-regressive conditional heteroscedasticity (GARCH) models are used to model conditional correlations between carbon prices, oil prices, natural gas prices and stock prices. Compared with the diagonal or dynamic conditional correlation model, the constant conditional correlation model is found to fit the data the best and is used to generate hedge ratios and optimal portfolios. Carbon does not appear to be useful for hedging oil or the S&P 500 index but does seem to be useful for hedging natural gas. The average weight for the carbon/natural gas portfolio indicates that for a US$1 portfolio, 29 cents should be invested in carbon and 71 cents invested in natural gas. Hedge ratios and optimal portfolio weights vary considerably over the sample period, indicating that financial positions should be monitored frequently.