We propose two nonparametric transition density-based speciþcation tests for continuous-time diffusion models. In contrast to marginal density as used in the literature, transition density can capture the full dynamics of a diffusion process, and in particular, can distinguish processes with...

This paper considers a class of nonstationary Gaussian processes with possible long-range dependence (LRD) and intermittency. The author proposes a new estimation method to simultaneously estimate both the LRD and intermittency parameter. An application of the proposed estimation method to a...

Understanding the behaviour of market prices is not simple. Stock market prices tend to have complicated distributions with strong skewness and fat tails. One important step in forecasting tomorrow’s price is to estimate the volatility, i.e. how much tomorrow’s price is expected to differ...

Some popular parametric diffusion processes have been assumed as such underlying diffusion processes. This paper considers an important case where both the drift and volatility functions of the underlying diffusion process are unknown functions of the underlying process, and then proposes using...

The implied signal extraction filters in unobserved components models depend on key signal-noise ratios. This paper examines how these ratios change with the observation interval. The analysis is based on continuous time models and is carried out for both stocks and flows. As a by-product, a...

In this paper a real analysis approach to stock price modelling is considered. A stock price and its return are defined in a duality to each other provided there exist suitable limits along a sequence of nested partitions of a time interval, mimicking sum and product integrals. It extends the...