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....

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...

This paper presents an empirical analysis of stochastic features of volatility in the Japanese stock price index, or TOPIX, using high-frequency data sampled every 5 min. The process of TOPIX is modeled by a stochastic differential equation with the time-homogeneous drift and diffusion...

This paper discusses nonparametric estimation of nonlinear dynamical system models by a method of metric-based local linear approximation. We assume no functional form of a given model but estimate it from experimental data by approximating the curve implied by the function by the tangent plane...