Dr Dooruj Rambaccussing received his Doctorate in August 2012 from the University of Exeter. Prior to joining us, he was working as an Associate Research Fellow at the Xfi Centre for Finance and Investment. He has also been a teaching on Econometric and Statistics courses at both undergraduate and postgraduate levels for 4 years now.  His research focusses on trading strategies, asset allocation, forecasting combination and tests of long memory.

Research/ Training

Econometrics:

Long Memory

Forecast Combination

Structural Breaks

State Space Models

Finance:

Insider trading

Present value Models

Commodity Markets

FE1012

FE2035

FEM120

FE1024

FEM222 

A Test of long memory based on self similarity (joint with J.Davidson)

Modelling the Persistence in Expected Returns, European Financial Management doctoral papers, June 2011

Fractional Integration, Return Predictability and Unconditional risk: Evidence from African Stock Markets, African Review of Economics and Finance, Vol 1, No2 June 2010

African Econometric Society

European Financial Management

Euro Area Business Cycle Network

Modeling the persistence in Expected Returns:

The major contribution of this paper is to explicitly model the persistence in the time series of expected returns. The series of expected returns is derived from a state space representation of the present value identity relating expected returns and expected dividend (earnings) growth to the observed price dividend (earnings) ratio. The state space model is adjusted in order to include the possibility of expected returns following anautoregressive fractional integrated (ARFIMA) process which captures thepersistence of the process. The new ARFIMA model performs moderately compared to the simple autoregressive process, which may be due tothe presence of di§erent regimes and structural breaks. The expected returns series is used in three applications namely in predictive regressions,analysing the relationship between consumption and discount rates and also in a market timing strategy.

A Test of Long Memory based on Self Similarity

This paper develops a new test of true versus spurious long memory, based on log periodogram estimation of the long memory parameter using skip-sampled data. A correction factor is derived to overcome the bias in this estimator due to aliasing. The test is implemented using the bootstrap, with the distribution under the null hypothesis approximated using the sieve-autoregression to approximate short-run dependence following fractional differencing. The procedure is designed to be used in the context of rejection in a conventional test of significance of the long memory parameter, and in this context the test is consistent. Its properties are investigated in a set of Monte Carlo experiments.