CAViaR-based Forecast for Oil Price Risk

• Main Authors: Dashan HUANG, YU, Baimin, FABOZZI, Frank, FUKUSHIMA, Masao
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2009
• Subjects: VaR; CAViaR; Oil price risk; Mixed data regression; Agribusiness; Finance and Financial Management
• Online Access: https://ink.library.smu.edu.sg/lkcsb_research/4780
• Institution: Singapore Management University

As a benchmark for measuring market risk, value-at-risk (VaR) reduces the risk associated with any kind of asset to just a number (amount in terms of a currency), which can be well understood by regulators, board members, and other interested parties. This paper employs a new VaR approach due to Engle and Manganelli [Engle, R.F., Manganelli, S., 2004. CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles. Journal of Business and Economic Statistics 22, 367–381] to forecasting oil price risk. In doing so, we provide two original contributions by introducing a new exponentially weighted moving average CAViaR model and developing a mixed data regression model for multi-period VaR prediction.