An Analysis of the Financial Market Volatility via Nonlinear Time Series Models

基于非线性时间序列模型的金融市场波动分析

基本信息

批准号：
18530231
负责人：
ISHIDA Isao
金额：
$ 2.35万
依托单位：
The University of Tokyo
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
2006
资助国家：
日本
起止时间：
2006 至 2007
项目状态：
已结题

项目摘要

This research project empirically investigates the time series behavior of financial market volatility Based on the weak convergence theory that approximately links discrete-time and continuous-time stochastic processes, various extensions of the standard GARCH(1,1) model, including nonlinear models with flexible conditional variance functions of a neural network type, are newly developed and applied to daily data of major stock market indices including both developed markets and emerging markets. The obtained empirical results indicate that the elasticity of volatility of volatility with respect to the current level of volatility is much higher than previously believed for most indices, implying that financial market volatility increases very rapidly in response to shocks. This finding has important implications for risk management, derivatives pricing and hedging, and monetary policy. In the second part of the project, the ARFIMA-GARCH model is applied to the so-called daily realized volatility (RV) constructed as a daily sum of squared five-minute high-frequency returns on the Nikkei 225 stock market index. Consistent with the extant literature, it is found that the Nikkei 225 RV is highly predictable with a long-memory property, meaning that its autocorrelations decay very slowly. The GARCH component of the ARFIMA GARCH model reveals that the volatility of volatility, more specifically the conditional variance of the Nikkei 225 RV, is stochastically changing through time with a component that is to some extent predictable. The continuous sample path variation component of the RV, which is free of the contributions of large jumps in the index value, is also studied. The results are similar to those for the standard RV.

该研究项目基于薄弱的收敛理论，凭经验研究了金融市场波动的时间序列行为，该理论近似地连接了离散的时间和连续的时间随机过程，标准GARCH（1,1）模型的各种扩展，包括具有神经网络类型的灵活条件差异功能的非线性模型，包括新的每日股票索引市场的日常数据，包括新闻的股票索引和既开发的每日数据。获得的经验结果表明，相对于当前波动性水平的波动性波动率的弹性比以前认为的大多数指数要高得多，这意味着金融市场波动率在响应冲击时迅速增加。这一发现对风险管理，衍生品定价和对冲以及货币政策具有重要意义。在项目的第二部分中，Arfima-Garch模型应用于所谓的每日实现的波动率（RV），该动力（RV）每天在Nikkei 225股票市场上构建的每日平方五分钟高频回报。与现存的文献一致，发现Nikkei 225 RV具有长期内存的高度可预测，这意味着其自相关衰变非常缓慢。 Arfima Garch模型的GARCH成分表明，波动率的波动率，更具体地说是Nikkei 225 RV的条件差异，随着时间的流逝，随着时间的流逝而随着时间的流逝而随着时间的流逝而变化，而该组件在某种程度上是可以预见的。还研究了RV的连续样品路径变化分量，该变化不含索引值中大型跳跃的贡献。结果与标准RV的结果相似。