PROPOSAL OF VOLATILITY FORECASTING MODEL : ENHANCING OF ASCAViaR MODEL

DOI Web Site 8 References
  • Suzuki Naoaki
    Department of Industrial Management and Engineering, Faculty of Engineering, Tokyo University of Science
  • Asahi Yumi
    Department of Management Science, Tokyo University of Science
  • Yamaguchi Toshikazu
    Department of Management Science, Tokyo University of Science

Bibliographic Information

Other Title
  • ボラティリティ予測モデルの提案 : ASCAViaRモデルの拡張
  • ボラティリティ ヨソク モデル ノ テイアン ASCAViaR モデル ノ カクチョウ

Search this article

Description

The volatility became an important index in Investment Science. Since the volatility attracts attention, much effort has been put into adding the volatility forecast accuracys. The generalized autoregressive conditional heteroskedasticity (GARCH) models are used the volatility forecast widely. They rely on the assumption of distribution function, therefore the volatility forecast may be error if distribution function changes with time. By contrast, Taylor proposed method of volatility forecasts from conditional autoregressive value at risk (CAViaR) models in 2005. Those models need not assume the distribution function. Many kinds of CAViaR models are presented, however the volatility forecast from Asymmetric Slope CAViaR (ASCAViaR) model is the most accurately. In the existing study, ASCAViaR model has constant expected value. This study aimed at adding the volatility forecast accuracy, and proposed changed expected value ASCAViaR model. This model has changeability expected value. This study compared the volatility forecast accuracy from changed expected value ASCAViaR model with those from existing ASCAViaR model and GARCH model. This study used three stock indices (the Japanese JASDAQ, the Japanese TOPIX and the U.S. S & P). For all indices, there were two forecast periods (10days and 20days).

Journal

References(8)*help

See more

Keywords

Details 詳細情報について

Report a problem

Back to top