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189 Results

Volatility Forecasting when the Noise Variance Is Time-Varying

Staff Working Paper 2013-48 Selma Chaker, Nour Meddahi
This paper explores the volatility forecasting implications of a model in which the friction in high-frequency prices is related to the true underlying volatility. The contribution of this paper is to propose a framework under which the realized variance may improve volatility forecasting if the noise variance is related to the true return volatility.

Which Parametric Model for Conditional Skewness?

Staff Working Paper 2013-32 Bruno Feunou, Mohammad R. Jahan-Parvar, Roméo Tedongap
This paper addresses an existing gap in the developing literature on conditional skewness. We develop a simple procedure to evaluate parametric conditional skewness models. This procedure is based on regressing the realized skewness measures on model-implied conditional skewness values.
Content Type(s): Staff research, Staff working papers Topic(s): Econometric and statistical methods JEL Code(s): C, C2, C22, C5, C51, G, G1, G12, G15

Volatility and Liquidity Costs

Staff Working Paper 2013-29 Selma Chaker
Observed high-frequency prices are contaminated with liquidity costs or market microstructure noise. Using such data, we derive a new asset return variance estimator inspired by the market microstructure literature to explicitly model the noise and remove it from observed returns before estimating their variance.

Are Product Spreads Useful for Forecasting? An Empirical Evaluation of the Verleger Hypothesis

Staff Working Paper 2013-25 Christiane Baumeister, Lutz Kilian, Xiaoqing Zhou
Notwithstanding a resurgence in research on out-of-sample forecasts of the price of oil in recent years, there is one important approach to forecasting the real price of oil which has not been studied systematically to date.
August 15, 2013

CSI: A Model for Tracking Short-Term Growth in Canadian Real GDP

Canada’s Short-Term Indicator (CSI) is a new model that exploits the information content of 32 indicators to produce daily updates to forecasts of quarterly real GDP growth. The model is a data-intensive, judgment-free approach to short-term forecasting. While CSI’s forecasts at the start of the quarter are not very accurate, the model’s accuracy increases appreciably as more information becomes available. CSI is the latest addition to a wide range of models and information sources that the Bank of Canada uses, combined with expert judgment, to produce its short-term forecasts.
August 15, 2013

The Accuracy of Short-Term Forecast Combinations

This article examines whether combining forecasts of real GDP from different models can improve forecast accuracy and considers which model-combination methods provide the best performance. In line with previous literature, the authors find that combining forecasts generally improves forecast accuracy relative to various benchmarks. Unlike several previous studies, however, they find that, rather than assigning equal weights to each model, unequal weighting based on the past forecast performance of models tends to improve accuracy when forecasts across models are substantially different.
August 15, 2013

Big Data Analysis: The Next Frontier

The formulation of monetary policy at the Bank of Canada relies on the analysis of a broad set of economic information. Greater availability of immediate and detailed information would improve real-time economic decision making. Technological advances have provided an opportunity to exploit “big data” - the vast amount of digital data from business transactions, social media and networked computers. Big data can be a complement to traditional information sources, offering fresh insight for the monitoring of economic activity and inflation.

What Central Bankers Need to Know about Forecasting Oil Prices

Staff Working Paper 2013-15 Christiane Baumeister, Lutz Kilian
Forecasts of the quarterly real price of oil are routinely used by international organizations and central banks worldwide in assessing the global and domestic economic outlook, yet little is known about how best to generate such forecasts. Our analysis breaks new ground in several dimensions.
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