Person:

Shephard, Neil

Integer-valued trawl processes are a class of serially correlated, stationary and infinitely divisible processes that Ole E. Barndorff-Nielsen has been working on in recent years. In this Chapter, we provide the first analysis of likelihood inference for trawl processes by focusing on the so-called exponential-trawl process, which is also a continuous time hidden Markov process with countable state space. The core ideas include prediction decomposition, filtering and smoothing, complete-data analysis and EM algorithm. These can be easily scaled up to adapt to more general trawl processes but with increasing computation efforts.

This paper introduces a new class of multivariate volatility models which is easy to estimate using covariance targeting, even with rich dynamics. We call them rotated ARCH (RARCH) models. The basic structure is to rotate the returns and then to Öt them using a BEKK-type parameterization of the time-varying covariance whose long-run covariance is the identity matrix. The extension to DCC-type parameterizations is given, introducing the rotated conditional correlation (RCC) model. Inference for these models is computationally attractive, and the asymptotics are standard. The techniques are illustrated using data on some DJIA stocks.

This paper introduces a new continuous-time framework for modelling serially correlated count and integer-valued data. The key component in our new model is the class of integer-valued trawl processes, which are serially correlated, stationary, infinitely divisible processes. We analyse the probabilistic properties of such processes in detail and, in addition, study volatility modulation and multivariate extensions within the new modelling framework. Moreover, we describe how the parameters of a trawl process can be estimated and obtain promising estimation results in our simulation study. Finally, we apply our new modelling framework to high-frequency financial data.

This paper uses tax and student loan administrative data to measure how the earnings of English graduates around 10 years into the labour market vary with gender, institution attended subject and socioeconomic background. The English system is competitive to enter, with some universities demanding very high entrance grades. Students specialise early, nominating their subject before they enter higher education (HE). We find subjects like Medicine, Economics, Law, Maths and Business deliver substantial premiums over typical graduates, while disappointingly, Creative Arts delivers earnings which are roughly typical of non-graduates. Considerable variation in earnings is observed across diff erent institutions. Much of this is explained by student background and subject mix. Based on a simple measure of parental income, we see that students from higher income families have median earnings which are around 25% more than those from lower income families. Once we control for institution attended and subject chosen this premium falls to around 10%.