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Research keywords: Econometrics and Quantitative Methods, Financial Economics.
Download CV: Natalia Bailey [PDF 40KB]
Cross-sectional dependence: Factor and Spatial models with applications to Finance and Macroeconomics.
Natalia Bailey is a lecturer at Queen Mary, University of London and specializes in econometrics and quantitative methods.
One strand of her work focuses on analysing the nature of cross-sectional dependence in large panels, in particular measuring the strength of interdendencies typically found in financial and macroeconomic data sets. Most recently she studied methods of distinguishing between strong (pervasive) forms of dependence and weaker (localised) connections, and how this variation affects the manner in which dependence is modelled. A second area of her research looks at robust estimation of large matrices using computationally efficient approaches. She further explores the implications that this has when tackling important problems in empirical finance such as testing the CAPM hypothesis and optimising the asset allocation of a large risky portfolio. Her research also extends to testing for the strength of dependence in a time series setting by taking advantage of useful properties found in the frequency domain. She is currently working on using similar techniques to describe potential seasonality existing in key macroeconomic and financial variables.
She received her PhD in economics from Queen Mary, University of London and her BSc from Warwick University. She continued with post-doctoral research at the University of Cambridge. She has worked in the financial sector for 8 years.
- Bailey, N., Kapetanios, G. and Pesaran, M. H. (2016). Exponent of Cross-sectional Dependence: Estimation and Inference, Journal of Applied Econometrics, 31 (6), 929-960.
- Bailey, N., Holly, S. and Pesaran, M. H. (2016). A Two Stage Approach to Spatio-temporal Analysis with Strong and Weak Cross-sectional Dependence, Journal of Applied Econometrics, 31, 249-280
- Bailey, N. and Giraitis, L. (2016). Spectral Approach to Parameter Free Unit Root Testing, Computational Statistics and Data Analysis, 100, 4-16.
- Bailey, N. and Giraitis, L. (2013). Weak Convergence in the Near Unit Root Setting, Statistics & Probability Letters, 83 (5), 1411-1415.
- Bailey, N., Pesaran, M. H. and Smith, L. V. (2016). A Multiple Testing Approach to the Regularisation of Large Sample Correlation Matrices, QMUL Working Paper Series No. 764 – under revision.
- Aquaro, M., Bailey, N. and Pesaran, M. H. (2015). wp749.pdf [PDF 613KB], QMUL Working Paper Series No. 749.