I completed my PhD in Economics with specialisation in Econometrics at Boston College in 2003. Prior to joining UNSW, I held several teaching and research appointments at Boston College and University of Montreal. I have also been a visiting academic at the Risk Management Institute in Singapore, Department of Economics in San Diego USA, Department of Economics at University College Dublin, Ireland, and European Centre for Applied Research in Economics and Statistics (ECARES) in Brussels.
My main research expertise is on modelling, estimating and making correct statistical analysis in big systems. Big-data presents technical challenges (hence curse of dimensionality) to the existing statistical tools that are used in economics and finance. My work falls in the new line of research that aims at turning this curse into a blessing. I have been published in top tier field journals. My contribution to the theoretical developments in econometrics has direct empirical implications. For example, a hedge fund manager wants to know what are the drivers of the market risk. Once these risk factors are identified, the hedge fund manager can build a portfolio to diversify away this risk. In big-data world, there are potentially hundreds/thousands of sources of systematic risk. The statistical tool I develop use this large volumes of information and selects the main key risk drivers without the fund manager having to make an uninformed ad-hoc guess. The technique is accurate and reliable and can be applied in many scenarios in Finance and Economics. Currently, I am working on identifying key drivers of growth in the real estate market in Australian Capital cities. For the Sydney area, the work is building a big data spatial econometric model to uncover what drives the high premium some suburbs earn their homeowners.
In another. area interest, I have published in A* journal in Finance where I establish new evidence of dependence of risk aversion on the Business cycle. Aggregate risk aversion it seems does vary with periods of economic booms and busts. Consumers perception of risk and wiliness to engage in risky ventures is conditional of the health of the economy.
Statistical inference sometimes has to be performed in small samples and asymptotic tools are no longer reliable. I have expertise in using simulation methods like the Bootstrap to study the statistical properties of key estimators like the Generalized Method of Moments estimator in models of practical importance in consumer behaviours. These models include the rational expectation model of the consumption asset pricing model.
Certificate of Outstanding Contribution in Reviewing, Emerging Markets Review, 2018
Best Paper Award, V IIth Spring Meeting of Young Economists
(1) Ouysse, R., 2021. Asset pricing with endogenous beliefs-dependent risk aversion. Journal of Financial Econometrics, Accepted Jan 2021 [A*]
(2) Ouysse, R., 2016. Bayesian model averaging and principal component regression forecasts in a data rich environment. International Journal of Forecasting, 32(3), pages 763-787. [Q1, IF 1.33, 5YIF 1.94, 87/333, cited 5, SSRN 230]
(3) Ouysse, R., 2014. On the performance of block-bootstrap continuously updated GMM for a class of non-linear conditional moment models: Moving block boot- strap infer- ence under weak identification. Computational Statistics, 29(1-2), pages: 233-261.[Q2, IF 0.403, 5YIF 0.558, SSRN 14]
(4) Ouysse, R., 2013. A fast iterated bootstrap procedure for approximating the small- sample bias. Communications in Statistics - Simulation and Computation, 42(7), pages: 1472-1494.
(5) Ouysse, R., 2011. Computationally efficient approximation for the double bootstrap mean bias correction. Economics Bulletin, 31(3) pages: 2388-2403.cited 3
(6) Ouysse, R and Kohn, R., 2010. Bayesian Selection of Risk Factors and Estimation of Factor Betas and Risk Premiums in the APT model. Computational Statistics and Data Analysis, 54, pages: 3249-3268.[Q1, IF 1.40, 5YIF 1.51, 34/122 cited 18, SSRN 791]
(7) Ouysse, R., 2010. Finite Sample Properties of Bootstrap GMM for Nonlinear Con- ditional Moment Models, InterStat Journal.
(8) Ouysse, R., 2006. Consistent Variable Selection in Large Panels when Factors are Observable. Journal of Multivariate Analysis, 97: 946-984.[Q1, IF 0.934 5YIF 1.153, 50/122, cited 12, SSRN 45]
(9) Ouysse, R., 2006. Approximate Factor Models: Finite Sample Distributions. Journal of Statistical Computation and Simulation, 76(4), pages: 279-303. Cited 2.
(10) Ouysse, R. Constrained principal components estimation of large approximate factor models. Journal of Econometrics (A*).
(11) Ouysse, R. Asset pricing with endogenous state-dependent risk aversion. Journal of Financial Econometrics (A*). Revision Due Feb 2020.
(12) Ouysse, R. Constrained principal components estimation of large approximate factor models. Journal of Applied Econometrics (A*)
(13) Ouysse, R. Predictability of Housing Prices in the Australian Capital Cities in a data rich environment. J ournal of Housing Economics (A)
(14) Ouysse, R.& Shi, S. & Mangionia, V. & Ge, J. & Heratha, S. & Rabhi, F. House Price Forecasting from Investment Perspectives
(15) Ouysse, R. & Chang, C. Consistent Estimation and Valid Inference for Dynamic Panel Data Models: Theory and Application to Latent Carbon Emissions Prices.
Note Abstract Submitted to Special Issue of Journal of Econometrics (A*).
(16) Ouysse, R. A Study of the distribution of Model Space under the Ridge and G-prior: Simulation and Application to Growth Data.
(17) Ouysse, R., Bayesian Model Selection in Hybrid Factor model: Application to Arbitrage Pricing Theory Model.
(18) Ouysse, R., Consistency of the g-prior in multivariate regression models.
(19) Ouysse, R.& Qian, M., New Test for Endogeneity in Exactly Identified Instru- mental Variable Model.
Book Chapter. Ouysse, R., 2019 Estimation of Common Factors by Principal Com- ponents, Partial Least Squares, and related methods, in M acroeconomic Forecasting in the Era of Big Data. Editors Koop, G., Matyas, L. & Fuleky, P., to be published in the “Ad- vanced Studies in Theoretical and Applied Econometrics” series by Springer.
Ouysse, R., 2006. Book review of “Introduction to the Theory of Econometrics”, by Jan R. Magnus, VU University Press.
Ouysse, R., 2006. Book review on “Introduction to the Mathematical and Statistical Foundations of Econometrics”, Herman J. Bierens, Cambridge University Press, Economic Record, 82, pages:230-231.
Ouysse, R. 2013. Forecasting using a large number of predictors: Bayesian model averag- ing versus principal components regression Ouysse, R. 2009. “Fast Iterated Bootstrap for Mean Bias Correction,” Proceedings of 2009 NZESG Workshop, University of Canterbury, Christchurch, New Zealand.
Ouysse, R., Nicholas, C. 2008. “Time Varying Determinants of Cross-Country Growth,” UNSW School of Economics Discussion paper 2008/03.
Ouysse, R. 2007. “ Finite Sample Properties of the Dependent Bootstrap for Conditional Moment Models,” Proceedings of 36th Australian Conference of Economists, Hobart, Tas- mania.
Ouysse, R. 2005. “Sampling Properties of Block Bootstrap in Non-linear Rational Expec- tations Models: Case of Consumption Asset Pricing Model,” Proceedings of 34th Australian Conference of Economists, Melbourne, Australia.
Learning and Teaching Qualifications
(1) UNSW Graduate Certificate in University Learning and Teaching, January 2015- December 2016.
(2) UNSW Foundations of University Learning and Teaching (FULT), 2006
Term 1 2021