Deferred fees for universities

I will argue for a simpler, fairer, more fiscally responsible and flexible form of university funding and student support. This system is designed to encourage a diverse higher education sector where high quality provision can flourish. The main points of the new system are: 1. Make student financial support available to cover all tuition and a modest cost of living. 2. Allow graduates to repay according to earnings with protection for poorer graduates. 3. Call HEFCE teaching grants “scholarships” and make students aware of their value. 4. Cap the level of funded fees plus HEFCE grant at the current level. 5. Allow universities to charge deferred fees. a. When they are paid the money goes to the student’s university not to the state. These fees have no fiscal implications. b. Bring some of the cash flow from deferred fees forward by working with a bank. 6. In the long-run move to making the cost of living support simpler by a. Providing more realistic cost of living support for all students. b. Removing means-tested university bursaries for cost of living expenses. c. Removing means-tested grants to students provided by the state. This builds on England’s higher education structure. The changes are simple to implement. It would set up a stable funding structure for our universities & provide the financial support our students need.

Realising the future: forecasting with high frequency based volatility (HEAVY) models

This paper studies in some detail a class of high frequency based volatility (HEAVY) models. These models are direct models of daily asset return volatility based on realized measures constructed from high frequency data. Our analysis identifies that the models have momentum and mean reversion effects, and that they adjust quickly to structural breaks in the level of the volatility process. We study how to estimate the models and how they perform through the credit crunch, comparing their fit to more traditional GARCH models. We analyse a model based bootstrap which allow us to estimate the entire predictive distribution of returns. We also provide an analysis of missing data in the context of these models.ARCH models; bootstrap; missing data; multiplicative error model; multistep ahead prediction; non-nested likelihood ratio test; realised kernel; realised volatility.

Testing the Assumptions Behind the Use of Importance Sampling

Importance sampling is used in many aspects of modern econometrics to approximate unsolvable integrals. Its reliable use requires the sampler to possess a variance, for this guarantees a square root speed of convergence and asymptotic normality of the estimator of the integral. However, this assumption is seldom checked. In this paper we propose to use extreme value theory to empirically assess the appropriateness of this assumption. We illustrate this method in the context of a maximum simulated likelihood analysis of the stochastic volatility model.Extreme value theory; Importance sampling; Simulation; Stochastic Volatility.

Inference for Adaptive Time Series Models: Stochastic Volatility and Conditionally Gaussian State Space Form

In this paper we replace the Gaussian errors in the standard Gaussian, linear state space model with stochastic volatility processes. This is called a GSSF-SV model. We show that conventional MCMC algoritms for this type of model are ineffective, but that this problem can be removed by reparameterising the model. We illustrate our results on an example from financial economics and one from the nonparametric regression literature. We also develop an effective particle filter for this model which is useful to assess the fit of the model.Markov chain Monte Carlo, particle filter, cubic spline, state space form, stochastic volatility.