| Budget Amount *help |
¥3,670,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥270,000)
Fiscal Year 2007: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2006: ¥2,500,000 (Direct Cost: ¥2,500,000)
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| Research Abstract |
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.
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