Mini-computer Fermat number transform and application to digital signal processing.

Source: Masters Abstracts International, Volume: 40-07, page: . Thesis (M.A.Sc.)--University of Windsor (Canada), 1979

Modelling and forecasting with financial duration data using non-linear model

The class of autoregressive conditional duration (ACD) models plays an important role in modelling the duration data in economics and finance. This paper presents a non-linear model to allow the first four moments of the duration to depend nonlinearly on past information variables. Theoretically the model is more general than the linear ACD model. The proposed model is fitted to the data given by the 3534 transaction durations of IBM stock on five consecutive trading days. The fitted model is found to be comparable to the Weibull ACD model in terms of the in-sample and out-of-sample mean squared prediction errors and mean absolute forecast deviations. In addition, the Diebold-Mariano test shows that there are no significant differences in forecast ability for all models

Bond option pricing under the CKLS model

Consider the European call option written on a zero coupon bond. Suppose the call option has maturity T and strike price K while the bond has maturity S T . We propose a numerical method for evaluating the call option price under the Chan, Karolyi, Longstaff and Sanders (CKLS) model in which the increment of the short rate over a time interval of length dt , apart from being independent and stationary, is having the quadratic-normal distribution with mean zero and variance dt. The key steps in the numerical procedure include (i) the discretization of the CKLS model; (ii) the quadratic approximation of the time-T bond price as a function of the short rate rT at time T; and (iii) the application of recursive formulas to find the moments of r(t+dt) given the value of r(t). The numerical results thus found show that the option price decreases as the parameter in the CKLS model increases, and the variation of the option price is slight when the underlying distribution of the increment departs from the normal distribution