Staff research

We investigate the uncertainty around stock returns at different investment horizons. Since a return is either a loss or a gain, we categorize return uncertainty into two components—loss uncertainty and gain uncertainty. We then use these components to evaluate investment.

Canadian interest rates show a secular decline since the 1980s. Long-term survey-based forecasts of interest rates also declined, but less so and were more gradual. Our model-based estimates show an endpoint shifting over time in three phases: a decline between 1990 and 1995, a period of stability between 1996 and 2007, and a further decline since 2008. The current endpoint estimate remains clouded with uncertainty; this is an active area of research.

We show that a large share of low-frequency (quarterly) movements in Canadian government bond yields can be explained by macroeconomic news, even though high-frequency (daily) changes are driven by other shocks. Furthermore, we show that US macro news—not domestic news— explains most of the quarterly variation in Canadian bond yields.

Many reports and analyses interpret the release of new economic data based on the headline surprise—for instance, total inflation, real GDP growth and the unemployment rate. However, we find that headline news alone cannot adequately explain the responses of market prices to new information. Rather, market prices react more strongly, on average, to non-headline news such as the composition of GDP growth, quality of jobs created and revisions to past data. Thus, tracking the impact of non-headline information released on the news day is crucial in analyzing how markets interpret and react to new economic data.

Specifications of the Federal Reserve target rate that have more realistic features mitigate in-sample over-fitting and are favored in the data.

We decompose total variance into its bad and good components and measure the premia associated with their fluctuations using stock and option data from a large cross-section of firms.

This paper provides a novel methodology for estimating option pricing models based on risk-neutral moments. We synthesize the distribution extracted from a panel of option prices and exploit linear relationships between risk-neutral cumulants and latent factors within the continuous time affine stochastic volatility framework.

Advances in variance analysis permit the splitting of the total quadratic variation of a jump diffusion process into upside and downside components. Recent studies establish that this decomposition enhances volatility predictions, and highlight the upside/downside variance spread as a driver of the asymmetry in stock price distributions.

In this note, we find that market participants react to an unexpected change in the tone of Canadian monetary policy statements. When the market perceives that the Bank of Canada plans to tighten (or alternatively, loosen) the monetary policy earlier than previously expected, the Canadian dollar appreciates (or depreciates) and long-term Government of Canada bond yields increase (or decrease). The tone of a statement is particularly relevant to the market when the policy rate has been unchanged for some time.

We estimate a continuous-time model with stochastic volatility and dynamic crash probability for the S&P 500 index and find that market illiquidity dominates other factors in explaining the stock market crash risk. While the crash probability is time-varying, its dynamic depends only weakly on return variance once we include market illiquidity as an economic variable in the model.