| Instantaneous spot rates are key endogenous variables when we study the term structure of interest rates, and because of its basic affections on term structure of interest rates and modern finance, many people want to describe its dynamic change rules. However, we find short-term memory models (such as Vasicek,CIR and CKIS models) are used most frequently, in addition, we use such as conditional heteroscedastic models and stochastic volatility models to describe abnormal disturbance, and volatility clusters in interest rates.Nowadays many people find there are always long period correlativity say, long-term memory, existed in time series of finance. We use such as ARMA models to describe short memory, as to long memory we use a fractional integrated process I ( d ) to do this job, here d is a real number. Excess kurtosis and fat tail characteristics in time series of interest rates indicates that the series is a nonlinear stochastic process which can be caused by conditional heteroscedastic( ARCH) or caused by long memory. While most studies on interest rates at home focused on the former. So our question is whether there is long memory in Chinese interest rates and how to describe it?As many people in China used short memory models to study short market interest rate, in this article, we introduce long memory models of time series, ways to test long memory and model estimation. As short repo rates in China are quit representative of short market rates, we test and compared long and short term memories in 7-day and 14-day short repo rates, and using a ARFIMA -GARCHmodel to describe both long and short memories in interest rates.The major contributions of this dissertation are: (1) first use long memory models to study time series of short market interest rates in China; (2) using score test together with estimateing ARFIMA model test long memory in time series; (3) estimate ARFIMA-GARCH model Hosking's iteration method which has been improved by us. |
PDF Full Text Request