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78 Results

On the Tail Risk Premium in the Oil Market

Staff Working Paper 2017-46 Reinhard Ellwanger
This paper shows that changes in market participants’ fear of rare events implied by crude oil options contribute to oil price volatility and oil return predictability. Using 25 years of historical data, we document economically large tail risk premia that vary substantially over time and significantly forecast crude oil futures and spot returns.

A Three‐Frequency Dynamic Factor Model for Nowcasting Canadian Provincial GDP Growth

Staff Discussion Paper 2017-8 Tony Chernis, Gabriella Velasco, Calista Cheung
This paper estimates a three‐frequency dynamic factor model for nowcasting Canadian provincial gross domestic product (GDP). Canadian provincial GDP is released by Statistics Canada on an annual basis only, with a significant lag (11 months).

Assessing the Predictive Ability of Sovereign Default Risk on Exchange Rate Returns

Staff Working Paper 2017-19 Claudia Foroni, Francesco Ravazzolo, Barbara Sadaba
Increased sovereign credit risk is often associated with sharp currency movements. Therefore, expectations of the probability of a sovereign default event can convey important information regarding future movements of exchange rates.

Markov‐Switching Three‐Pass Regression Filter

We introduce a new approach for the estimation of high-dimensional factor models with regime-switching factor loadings by extending the linear three-pass regression filter to settings where parameters can vary according to Markov processes.
Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods JEL Code(s): C, C2, C22, C23, C5, C53

Assessing the Business Outlook Survey Indicator Using Real-Time Data

Staff Discussion Paper 2017-5 Lise Pichette, Marie-Noëlle Robitaille
Every quarter, the Bank of Canada conducts quarterly consultations with businesses across Canada, referred to as the Business Outlook Survey (BOS). A principal-component analysis conducted by Pichette and Rennison (2011) led to the development of the BOS indicator, which summarizes survey results and is used by the Bank as a gauge of overall business sentiment.

A Dynamic Factor Model for Nowcasting Canadian GDP Growth

Staff Working Paper 2017-2 Tony Chernis, Rodrigo Sekkel
This paper estimates a dynamic factor model (DFM) for nowcasting Canadian gross domestic product. The model is estimated with a mix of soft and hard indicators, and it features a high share of international data.

A General Approach to Recovering Market Expectations from Futures Prices with an Application to Crude Oil

Staff Working Paper 2016-18 Christiane Baumeister, Lutz Kilian
Futures markets are a potentially valuable source of information about price expectations. Exploiting this information has proved difficult in practice, because time-varying risk premia often render the futures price a poor measure of the market expectation of the price of the underlying asset.

New Housing Registrations as a Leading Indicator of the BC Economy

Staff Discussion Paper 2016-3 Calista Cheung, Dmitry Granovsky
Housing starts and building permits data are commonly used as leading indicators of economic activity. In British Columbia, all new homes must be registered with the Homeowner Protection Office, a branch of BC Housing, before the issuance of building permits and the start of construction.

Nowcasting BRIC+M in Real Time

Emerging-market economies have become increasingly important in driving global GDP growth over the past 10 to 15 years. This has made timely and accurate assessment of current and future economic activity in emerging markets important for policy-makers not only in these countries but also in advanced economies.

Model Averaging in Markov-Switching Models: Predicting National Recessions with Regional Data

Staff Working Paper 2015-24 Pierre Guérin, Danilo Leiva-Leon
This paper introduces new weighting schemes for model averaging when one is interested in combining discrete forecasts from competing Markov-switching models. In particular, we extend two existing classes of combination schemes – Bayesian (static) model averaging and dynamic model averaging – so as to explicitly reflect the objective of forecasting a discrete outcome.
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