Risk evaluation and management based on high-frequency data and jump variation

2014 Fiscal Year Final Research Report

• Project/Area Number: 25780154
• Research Category: Grant-in-Aid for Young Scientists (B)
• Allocation Type: Multi-year Fund
• Research Field: Economic statistics
• Research Institution: Kushiro Public University of Economics
• Principal Investigator: UBUKATA MASATO 釧路公立大学, 経済学部, 准教授 (00467507)
• Project Period (FY): 2013-04-01 – 2015-03-31
• Keywords: 金融高頻度データ / 実現ボラティリティ / インプライドボラティリティ / ジャンプ / 実現共分散 / 最適ヘッジ比率

Outline of Final Research Achievements

In the literature of risk evaluation and management, we construct an implied jump risk variable from option prices and applies it to time series models for realized volatility. Out-of-sample forecasting evidence suggests that the implied large jump risk variables could be useful in forecasting a realized volatility during the 2008-09 financial crisis.
Second, we investigate the performance of a conditional hedging model using realized covariance measure with noisy high-frequency data. The out-of-sample results show that the model overall performs well for a short hedge in the period without unpredictably large fluctuations in returns such as the Lehman aftermath and the economic impact of the Great East Japan Earthquake.

Free Research Field

計量ファイナンス