Search

Wavelets are mathematical expansions that transform data from the time domain into different layers of frequency levels. Compared to standard Fourier analysis, they have the advantage of being localized both in time and in the frequency domain, and enable the researcher to observe and analyze data at different scales.

This paper examines asset allocation strategies in an extreme value at risk (VaR) framework in which the risk measure is the p-quantile from the extreme value distribution. The main focus is on the allocation problem faced by an extremely risk-averse institution, such as a central bank.

This paper uses a smooth transition error-correction model (STECM) to model the one-year and five-year mortgage rate changes. The model allows for a non-linear adjustment process of mortgage rates towards their long-run equilibrium.

This paper describes a new test for evaluating conditional density functions that remains valid when the data are time-dependent and that is therefore applicable to forecasting problems. We show that the test statistic is asymptotically distributed standard normal under the null hypothesis, and diverges to infinity when the null hypothesis is false.

This paper evaluates the forecasting performance of factor models for Canadian inflation. This type of model was introduced and examined by Stock and Watson (1999a), who have shown that it is quite promising for forecasting U.S. inflation.

Affine models describe the stylized time-series properties of the term structure of interest rates in a reasonable manner, they generalize relatively easily to higher dimensions, and a vast academic literature exists relating to their implementation. This combination of characteristics makes the affine class a natural introductory point for modelling interest rate dynamics.

This paper evaluates linear and non-linear forecast-combination methods. Among the non-linear methods, we propose a nonparametric kernel-regression weighting approach that allows maximum flexibility of the weighting parameters.

This study tests for a structural break in the volatility of real GDP growth in Canada following the methodology of McConnell and Quiros (1998). A break is found in the first quarter of 1991.

This paper proposes a class of linear signed rank statistics to test for a random walk with unknown drift in the presence of arbitrary forms of conditional heteroscedasticity.

Artificial neural networks (ANN) are employed for high-frequency Canada/U.S. dollar exchange rate forecasting. ANN outperform random walk and linear models in a number of recursive out-of- sample forecasts.