Perry Sadorsky

The Relationship Between Environmental Commitment and Managerial Perceptions of Stakeholder Importance

Do firms committed to stewardship of the natural environment differ from less environmentally committed firms in their perceptions of the relative importance of different stakeholders in influencing their environmental practices? Using cluster analysis on six responses to questions describing a firms practices, we classified 400 firms into four environmental profiles: reactive, defensive, accommodative, and proactive. Results indicate that firms with more proactive profiles do differ from less environmentally committed firms in their perceptions of the relative importance of different stakeholders.

Oil price shocks and stock market activity

Results from a vector autoregression show that oil prices and oil price volatility both play important roles in affecting real stock returns. There is evidence that oil price dynamics have changed. After 1986, oil price movements explain a larger fraction of the forecast error variance in real stock returns than do interest rates. There is also evidence that oil price volatility shocks have asymmetric effects on the economy.

Risk factors in stock returns of Canadian oil and gas companies

Abstract This paper uses a multifactor market model to estimate the expected returns to Canadian oil and gas industry stock prices. Results are presented to show that exchange rates, crude oil prices and interest rates each have large and significant impacts on stock price returns in the Canadian oil and gas industry. In particular, an increase in the market or oil price factor increases the return to Canadian oil and gas stock prices while an increase in exchange rates or the term premium decreases the return to Canadian oil and gas stock prices. Furthermore, the oil and gas sector is less risky than the market and its moves are pro-cyclical. This suggests that Canadian oil and gas stocks may not be a good hedge against inflation.

Oil Prices, Exchange Rates and Emerging Stock Markets

While two different streams of literature exist investigating 1) the relationship between oil prices and emerging market stock prices and 2) the relationship between oil prices and exchange rates, relatively little is known about the dynamic relationship between oil prices, exchange rates and emerging market stock prices. This paper proposes and estimates a structural vector autoregression model to investigate the dynamic relationship between these variables. Impulse responses are calculated in two ways (standard and projection based methods). The model supports stylized facts. In particular, positive shocks to oil prices tend to depress emerging market stock prices and US dollar exchange rates in the short run. The model also captures stylized facts regarding movements in oil prices. A positive oil production shock lowers oil prices while a positive shock to real economic activity increases oil prices. There is also evidence that increases in emerging market stock prices increases oil prices.

The macroeconomic determinants of technology stock price volatility

Abstract Stock prices reflect the value of anticipated future profits of companies. Since business cycle conditions impact the future profitability of firms, expectations about the business cycle will affect the current value of firms. This paper uses daily and monthly data from July 1986 to December 2000 to investigate the macroeconomic determinants of US technology stock price conditional volatility. Technology share prices are measured using the Pacific Stock Exchange Technology 100 Index. One of the novel features of this paper is to incorporate a link between technology stock price movements and oil price movements. The empirical results indicate that the conditional volatilities of oil prices, the term premium, and the consumer price index each have a significant impact on the conditional volatility of technology stock prices. Conditional volatilities calculated using daily stock return data display more persistence than conditional volatilities calculated using monthly data. These results further our understanding of the interaction between oil prices and technology share prices and should be of use to investors, hedgers, managers, and policymakers.

The empirical relationship between energy futures prices and exchange rates

Abstract This paper investigates the interaction between energy futures prices and exchange rates. Results are presented to show that futures prices for crude oil, heating oil and unleaded gasoline are co-integrated with a trade-weighted index of exchange rates. This is important because it means that there exists a long-run equilibrium relationship between these four variables. Granger causality results for both the long- and short-run are presented. Evidence is also presented that suggests exchange rates transmit exogenous shocks to energy futures prices.

Time-varying risk premiums in petroleum futures prices

This paper uses an ARMAX-ARCH model to estimate the conditional expected returns of petroleum futures prices under time-varying risk. Empirical results suggest that macroeconomic risk factors have significant forecast power in petroleum futures markets. The conditional expected returns for petroleum futures prices are quite large. Results from a small forecasting experiment indicate that the out-of-sample forecasts from an ARMAX-ARCH model generally outperform a random walk for all forecast horizons. Regression-based tests for market timing indicate that the model captures both the correct sign and the correct magnitude. Net trading profits are positive in all cases.

Stochastic volatility forecasting and risk management

This paper compares the forecasting performance of the range-based stochastic volatility model with a number of other well-known forecasting models. Each forecasting model is applied to a financial data set that includes daily futures prices on, the S&P 500, ten year US government bond series, crude oil prices, and the foreign currency exchange rate between the Canadian and US dollar. Forecasts are evaluated out of sample using forecast summary statistics as well as value at risk measures like conditional coverage, independence and unconditional coverage. Overall the forecast summary statistics show that for each financial series, moving average, exponential smoothing and AR5 models to be better at forecasting the log range than the stochastic volatility model. Value at risk calculated from the stochastic volatility models does not reject independence in each of the four financial series studied but does reject conditional and unconditional coverage in all of the series studied. The empirical density model does not reject unconditional coverage in three out of the four financial series studied. All of the parametric models reject conditional coverage. These results show how difficult it is to design a good parametric value at risk model.

Multifactor risk and the stock returns of Canadian paper and forest products companies

Abstract This paper uses a multifactor market model to estimate the expected returns to Canadian paper and forest products stock prices. Results are presented to show that market returns, exchange rates and commodity prices each have large and significant effects on stock price returns in the Canadian paper and forest products industry. In particular, an increase in the market or commodity price factor increases the return to Canadian paper and forest products stock prices while an increase in exchange rates decreases the return to Canadian paper and forest products stock prices. Furthermore, the paper and forest products sector is riskier than the market and its moves are pro-cyclical.

The oil price exposure of global oil companies

This study investigates the impact that global oil market risk factors have on the oil price risk of oil company stock prices. Results indicate that oil prices and market risk are both positive and statistically significant priced risk factors. Oil price risk is negatively impacted by increases in oil reserves. Oil price risk is positively impacted by increases in oil production. Oil price risk is more sensitive to changes in production rates than to changes in reserve additions rates.