Implications of Nonlinear Dynamics for Financial Risk Management
发现四种货币期货的日对数价格变化不满足独立同分布，条件方差可预测，并用自回归波动模型分解出可预测部分和不可预测部分，得到条件密度，用于金融风险管理。
This paper demonstrates that when log price changes are not IID, their conditional density may be more accurate than their unconditional density for describing short-term behavior. Using the BDS test of independence and identical distribution, daily log price changes in four currency futures contracts are found to be not IID. While there appear to be no predictable conditional mean changes, conditional variances are predictable, and can be described by an autoregressive volatility model that seems to capture all the departures from independence and identical distribution. Based on this model, daily log price changes are decomposed into a predictable part, which is described parametrically by the autoregressive volatility model, and an unpredictable part, which can be modeled by an empirical density, either parametrically or nonparametrically. This two-step seminonparametric method yields a conditional density for daily log price changes, which has a number of uses in financial risk management.