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.

A Nonparametric Bayesian Approach to Copula Estimation

We propose a novel Dirichlet-based P\'olya tree (D-P tree) prior on the copula and based on the D-P tree prior, a nonparametric Bayesian inference procedure. Through theoretical analysis and simulations, we are able to show that the flexibility of the D-P tree prior ensures its consistency in copula estimation, thus able to detect more subtle and complex copula structures than earlier nonparametric Bayesian models, such as a Gaussian copula mixture. Further, the continuity of the imposed D-P tree prior leads to a more favorable smoothing effect in copula estimation over classic frequentist methods, especially with small sets of observations. We also apply our method to the copula prediction between the S\&P 500 index and the IBM stock prices during the 2007-08 financial crisis, finding that D-P tree-based methods enjoy strong robustness and flexibility over classic methods under such irregular market behaviors

Autoregressive conditional root model

In this paper we develop a time series model which allows long-term disequilibriums to have epochs of non-stationarity, giving the impression that long term relationships between economic variables have temporarily broken down, before they endogenously collapse back towards their long term relationship. This autoregressive root model is shown to be ergodic and covariance stationary under some rather general conditions. We study how this model can be estimated and tested, developing appropriate asymptotic theory for this task. Finally we apply the model to assess the purchasing power parity relationship.Cointegration; Equilibrium correction model; GARCH; Hidden Markov model; Likelihood; Regime switching; STAR model; Stochastic break; Stochastic unit root; Switching regression; Real Exchange Rate; PPP; Unit root hypothesis.

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.