Laura's research interests are in the areas of Mathematical Finance, Risk Management, and Financial Engineering, with particular focus on problems of practical relevance in current financial markets conditions, such as Counterparty Credit Risk (CCR) valuation and collateral design, and development of realistic models for the dynamics of the relevant risk drivers which also recognize the interdependence in place between them.
Laura Ballotta obtained her PhD in Mathematical and Computational Methods for Economics and Finance from the Università degli Studi di Bergamo (Italy). She has previously held positions at Università Cattolica del Sacro Cuore, Piacenza (Italy), and Department of Actuarial Science and Statistics, City University London (UK). Laura graduated with a BSc in Economics from Università Cattolica del Sacro Cuore, Piacenza (Italy), and a MSc in Financial Mathematics from the University of Edinburgh - jointly awarded with Heriot-Watt University (UK).
- BSc in Economics, Universita' Cattolica Sacro Cuore, Italy
- MSc in Financial Mathematics, University of Edinburgh, United Kingdom
- PhD in Computational Methods in Economics and Finance, Universita'degli Studi di Bergamo, Italy
Memberships of Professional Organisations
- Member, Bachelier Finance Society, Nov 2016 – present
- Fellow, The Association for Mathematics Applied to Economics and Social Sciences (AMASES), Oct 2014 – present
- Fellow, Istituto Italiano degli Attuari, Jan 2005 – present
- Cass Business School, City, University of London (2011) Teaching and Learning Prize
for excellence in teaching at postgraduate level
- City University (2005) Excellence in Research
Commendation for Excellence in Research
French, German and Italian (can read, write, speak, understand spoken and peer review).
- Financial Engineering
- Mathematical Finance
- Quantitative Finance
- Risk Management
- Risk Modelling
- Simulation Methods
- Futures & Options
- Life Insurance
- Multivariate statistics
- Probability Theory
- Stochastic Processes
Over the last few years, my research has contributed on problems of practical relevance in current financial markets conditions following the move of the financial industry towards the analysis and implementation of sophisticated tools for risk management. Specifically the themes I have been working on are Counterparty Credit Risk valuation, and development of realistic models for the dynamics of the relevant risk drivers which also recognize the interdependence in place between them.
- Counterparty credit risk in a structural default model using multivariate Levy processes
- This is joint work with Gianluca Fusai (Cass Business School) and Daniele Marazzina (Politecnico, Milan). We aim at providing a mathematically and computationally tractable setting for the computation counterparty risk at single trade level. The proposed model allows for the analysis of the impact of skewness, kurtosis and correlation on counterparty risk and wrong way risk, as to assess current Supervisory Authority recommendations on the multipliers to be applied for the calculation of the banks’ capital requirements. We also consider the treatment of the first to default problem, mitigating clauses and gap risk in the setting of a multivariate structural model. Finally, extensions to multiple trades and portfolio level are being investigated.
- Multivariate Lévy Models by Linear Combination: Estimation
- This is joint work with Angela Loregian and Gianluca Fusai. In this paper we propose a simple and effective two-step procedure to estimate the multivariate Lévy model introduced by Ballotta and Bonfiglioli (2014). We assess our estimation approach via simulations, comparing the results with those obtained through a standard but more computationally intensive one-step maximum likelihood estimation. The proposed method is then applied to the computation of the intra-horizon Value at Risk for a portfolio of assets following the model under consideration.
- Multivariate Time Changed Lévy processes
- In this work, we aim at extending the framework proposed by Ballotta and Bonfiglioli (2014) to incorporate volatility and leverage effects originated by both diffusion and pure jump components. Several applications in pricing, hedging and risk management are considered.
- Smiles & Smirks
- We propose a general setting for modelling equity prices with stochastic volatility and leverage effects based on time changed Lévy processes in order to answer some long standing modelling design questions: which risk factor offers sufficient flexibility for a robust calibration performance; which feature of the log-return process, such as volatility of volatility, leverage expressed as either covariance or correlation, enables the model to fit the data better; relevance of the classic diffusion component built on the Brownian motion in presence of leverage generated via jumps. The latter point assumes relevance in the case of extensions to multivariate asset modelling in order to maintain the parsimony of the dimensional complexity of the parameter space. The proposed model also allows the analysis of existing stochastic volatility models in terms of their distributional features, and to propose alternative robust constructions. Preliminary results point to the attractiveness of jumps.
- 2005 - present, MSc Financial Mathematics, Admissions Tutor
- 2006 - present, MSc Quantitative Finance, Admissions Tutor
- 2003 - 2006, MSc Financial Mathematics, Director