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Dr Emese Lazar

Undergraduate Programme Area Director

Programme Director: MSc Financial Engineering
Programme Director: Financial Investment Banking
Associate Professor of Quantitative Finance

Emese Lazar

Specialisms

  • Financial Econometrics, 
  • Market Risk, 
  • Volatility Modelling

Location

ICMA Centre, Whiteknights Campus

Emese received a PhD in Finance from the ICMA Centre, The University of Reading in 2006. Previously she obtained an MSc in Financial Engineering and Quantitative Analysis with a distinction from the ICMA Centre.

She graduated from the Academy of Economic Studies in Bucharest, with a BSc in Finance and Banking. Also, she holds a BSc in Computer Science obtained from the University of Bucharest, Faculty of Mathematics. Her research interests include: risk measurement and management, model risk, volatility and correlation models and their applications in pricing structured products. Emese presently teaches Market Risk and Derivatives Modelling.

Reference: Avino, D. and Lazar, E. (2020) Rethinking capital structure arbitrage: a price discovery perspective. The Journal of Alternative Investments, 22 (4). pp. 75-91. ISSN 1520-3255 doi: https://doi.org/10.3905/jai.2020.1.093
Reference: Lazar, E. and Zhang, N. (2020) Market risk measurement: preliminary lessons from the COVID-19 crisis. In: Billio, M. and Varotto, S. (eds.) A New World Post COVID-19 Lessons for Business, the Finance Industry and Policy Makers. Innovation in Business, Economics & Finance 1. Edizioni Ca'Foscari, pp. 97-107. ISBN 9788869694424 doi: https://doi.org/10.30687/978-88-6969-442-4/007
Reference: Alexander, C. and Lazar, E. (2020) The continuous limit of weak GARCH. Econometric Reviews. ISSN 1532-4168 doi: https://doi.org/10.1080/07474938.2020.1799592
Reference: Alexander, C., Lazar, E. and Stanescu, S. (2020) Analytic moments for GJR-GARCH (1,1) processes. International Journal of Forecasting. ISSN 0169-2070 doi: https://doi.org/10.1016/j.ijforecast.2020.03.005 (In Press)
Reference: Jiang, Y. and Lazar, E. (2020) Forecasting VIX using filtered historical simulation. Journal of Financial Econometrics. ISSN 1479-8417 doi: https://doi.org/10.1093/jjfinec/nbaa041 (In Press)
Reference: Lazar, E. and Zhang, N. (2019) Model risk of expected shortfall. Journal of Banking and Finance, 105. pp. 74-93. ISSN 0378-4266 doi: https://doi.org/10.1016/j.jbankfin.2019.05.017
Reference: Lazar, E. and Xue, X. (2019) Forecasting risk measures using intraday data in a generalized autoregressive score (GAS) framework. International Journal of Forecasting. ISSN 0169-2070 doi: https://doi.org/10.1016/j.ijforecast.2019.10.007 (In Press)
Reference: Pele, D. T., Lazar, E. and Mazurencu-Marinescu-Pele, M. (2019) Modelling expected shortfall using tail entropy. Entropy, 21 (12). 1204. ISSN 1099-4300 doi: https://doi.org/10.3390/e21121204
Reference: Pele, D. T., Lazar, E. and Dufour, A. (2017) Information entropy and measures of market risk. Entropy, 19 (5). 226. ISSN 1099-4300 doi: https://doi.org/10.3390/e19050226
Reference: Avino, D. , Lazar, E. and Varotto, S. (2015) Time varying price discovery. Economics Letters, 126. pp. 18-21. ISSN 0165-1765 doi: https://doi.org/10.1016/j.econlet.2014.09.030
Reference: Avino, D. , Lazar, E. and Varotto, S. (2013) Price discovery of credit spreads in tranquil and crisis periods. International Review of Financial Analysis, 30. pp. 242-253. ISSN 1057-5219 doi: https://doi.org/10.1016/j.irfa.2013.08.002
Reference: Alexander, C. , Lazar, E. and Stanescu, S. (2013) Forecasting VaR using analytic higher moments for GARCH processes. International Review of Financial Analysis, 30. pp. 36-45. ISSN 1057-5219 doi: https://doi.org/10.1016/j.irfa.2013.05.006
Reference: Symeonidis, L., Prokopczuk, M. , Brooks, C. and Lazar, E. (2012) Futures basis, inventory and commodity price volatility: an empirical analysis. Economic Modelling, 29 (6). pp. 2651-2663. ISSN 0264-9993 doi: https://doi.org/10.1016/j.econmod.2012.07.016 (http://www.sciencedirect.com/science/journal/02649993)
Reference: Alexander, C. and Lazar, E. (2009) Modelling regime-specific stock price volatility. Oxford Bulletin of Economics and Statistics, 71 (6). pp. 761-797. ISSN 1468-0084 doi: https://doi.org/10.1111/j.1468-0084.2009.00563.x
Reference: Badescu, A., Kulperger, R. and Lazar, E. (2008) Option valuation with normal mixture GARCH models. Studies in nonlinear dynamics & econometrics, 12 (2). 5. ISSN 1558-3708 doi: https://doi.org/10.2202/1558-3708.1580
Reference: Alexander, C. and Lazar, E. (2006) Normal mixture GARCH(1,1): applications to exchange rate modelling. Journal of Applied Econometrics, 21 (3). pp. 307-336. ISSN 1099-1255 doi: https://doi.org/10.1002/jae.849
Reference: Alexander, C. and Lazar, E. (2004) Time aggregation of normal mixture GARCH models. In: Second international IASTED conference on financial engineering and applications, 8-10 November, 2004, Massachusetts Institute of Technology, Cambridge, USA.

Derivatives Modelling

The module is designed to provide an introduction to the models and pricing of interest rates and credit derivatives. It conveys the basic concepts and analytical methodology for the valuation...

Module code: ICM292

Market Risk

The purpose of the module is to provide an understanding of the latest developments in banking regulations that are the main driving force behind changes in our approaches to risk...

Module code: ICM207

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