This paper discusses a problem concerning intertemporal decision-making under uncertainty when its subject has psychological biases. Here, we consider an investment company as a decision maker that invests money from investors in a financial asset and pays some dividend every period depending on...

This paper provides an analytic method of filtering for partially observed diffusions, which can be also used for parameter estimation with the quasi-maximum likelihood method. The filtering is shown to have consistency in a weak sense. In addition, using the stochastic volatility models, a...

We propose an analytical approximation of the term structure of interest rates under general diffusion processes of the short-rate and state variables. A method of approximating conditional moments as the solution to a system of ordinary differential equations is applied to the pricing of bonds....

The paper presents a method for estimating nonparametrically the states of one-dimensional diffusion processes. Once certain nuisance parameters have been estimated from the time series, states of a diffusion process can be estimated by the Kalman filter algorithm, so that the method is also...

In this note we investigate asymptotic properties of an estimator, called the Euler estimator, which is obtained by maximizing the likelihood function of the process discretized by the Euler method. By linking the Euler estimator of the coefficients of the drift function of a stochastic...

We examine by numerical experiments the accuracy of an analytical approximation for the nonlinear term structure of interest rates, which is obtained by applying the local linear approximation to a generally specified process of the short rate. Under various short-rate models, we compare...

This paper provides a nonparametric model of multi-step ahead forecasting in diffusion processes. The model is constructed from the local linear model with the Gaussian kernel. The paper provides simulation studies to evaluate its performance of multi-step ahead forecasting by comparing with the...