The paper revisits dynamic term structure models (DTSMs) and proposes a new way in dealing with the limitation of the classical affine models. In particular, this paper expands the flexibility of the DTSMs by applying a fractional Brownian motion as the governing force of the state variable...

We measure the economic capital stock of money implied by the Divisia monetary aggregate service flow, in a manner consistent with asset pricing theory. Based on Barnett’s [4] definition of the economic stock of money, we estimate the expected discounted flow of expenditure on the...

Understanding the value a customer has to a business is a fundamental problem. Accurate valuations are critical for setting appropriate levels of investment for targeted marketing and for the setting of individual customer service levels. Traditionally semi-qualitative methods using results from...

We use parametric power ARCH models of the conditional variance of inflation and monthly data in Germany, the Netherlands and Sweden for the period 1962-2004 to examine the relationship between inflation and inflation uncertainty. In two out of the three countries inflation significantly raises...

I consider two filtering algorithms (quadrature and mixture Gaussian) based on numerical integration for maximum likelihood estimation of stochastic volatility models with leverage. These algorithms extend straightforwardly to stochastic volatility models with non-Gaussian innovations. A small...

In this paper we explore the forecasting value of historical volatility (extracted from daily return series), of implied volatility (extracted from option pricing data) and of realised volatility (computed as the sum of squared high frequency returns within a day). First we consider unobserved...

This paper focuses on the estimation of mutual fund styles by return-based style analysis. Usually, the investment style is assumed to be either constant through time, or time variation is implicitly accounted for by using rolling regressions. The former assumption is often contradicted by data...