C - Mathematical and Quantitative Methods - Bank of Canada
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Bank of Canada RSS Feedsen2024-03-29T15:56:47+00:00Pricing Interest Rate Derivatives in a Non-Parametric Two-Factor Term-Structure Model
https://www.bankofcanada.ca/1999/11/working-paper-1999-19/
Diffusion functions in term-structure models are measures of uncertainty about future price movements and are directly related to the risk associated with holding financial securities. Correct specification of diffusion functions is crucial in pricing options and other derivative securities. In contrast to the standard parametric two-factor models, we propose a non-parametric two-factor term-structure model that […]1999-11-14T11:08:54+00:00enPricing Interest Rate Derivatives in a Non-Parametric Two-Factor Term-Structure Model1999-11-14Econometric and statistical methodsMarket structure and pricingWorking Paper 1999-19 https://www.bankofcanada.ca/wp-content/uploads/2010/05/wp99-19.pdfPricing Interest Rate Derivatives in a Non-Parametric Two-Factor Term-Structure ModelJohn KnightFuchun LiMingwei YuanNovember 1999CC1C14C2C22GG1G13Estimating One-Factor Models of Short-Term Interest Rates
https://www.bankofcanada.ca/1999/11/working-paper-1999-18/
There currently exists in the literature several continuous-time one-factor models for short-term interest rates. This paper considers a wide range of these models that are nested into one general model. These models are approximated using both a discrete-time model and a model that accounts for aggregation effects over time, and are estimated by both the […]1999-11-04T10:41:22+00:00enEstimating One-Factor Models of Short-Term Interest Rates1999-11-04Financial marketsInterest ratesWorking Paper 1999-18 https://www.bankofcanada.ca/wp-content/uploads/2010/05/wp99-18.pdfEstimating One-Factor Models of Short-Term Interest RatesDes Mc ManusDavid WattNovember 1999CC5C52GG1G10Forecasting GDP Growth Using Artificial Neural Networks
https://www.bankofcanada.ca/1999/01/working-paper-1999-3/
Financial and monetary variables have long been known to contain useful leading information regarding economic activity. In this paper, the authors wish to determine whether the forecasting performance of such variables can be improved using neural network models. The main findings are that, at the 1-quarter forecasting horizon, neural networks yield no significant forecast improvements. […]1999-01-21T08:20:41+00:00enForecasting GDP Growth Using Artificial Neural Networks1999-01-21Econometric and statistical methodsMonetary and financial indicatorsWorking Paper 1999-3 https://www.bankofcanada.ca/wp-content/uploads/2010/05/wp99-3.pdfForecasting GDP Growth Using Artificial Neural NetworksGreg TkaczSarah HuJanuary 1999CC4C45EE3E37E4E44