Davide Benedetti

Research

• Climate Change Investment Risk: Optimal Portfolio Construction Ahead of the Transition to a Lower-carbon Economy
  with Enrico Biffis, Davide Benedetti, Fotis Chatzimichalakis, Luciano Fedele and Ian Simm
  Available at SSRN
  (Revise and Resubmit to Annals of Operations Research)
• Security Posture Assessment in Conflict Areas: Evidence from Iraq
  with Enrico Biffis and Mehmet Shoukru
  
  We consider the problem of assessing the security posture of organizations operating in conflict areas. We show how spatio-temporal risk models can be used to understand the dynamics of attack occurrences and severities, providing insights into the design of the Close Protection security layer (i.e., the range of security measures under direct control of an organization). The research design is as follows: first, (i) we estimate spatio-temporal risk models on a granular dataset of attacks carried out in Iraq during the period 2007-15, to better understand the dynamics of attack occurrences and severities; then, (ii) we implement survey data from the top five security providers in Iraq to create Close Protection security benchmarks based on ‘unconditional’ (broad) information, as well as ‘conditional’ on the predictions from the risk models of point (i); finally, (iii) we quantify the economic gains of using predictive models by computing cost deviations of implementing the ‘unconditional’ security posture vs. the ‘conditional’ one vs. the observed one. As an application, we consider in detail four different areas presenting different socio-economic characteristics and patterns of attack occurrence. We then look at the oil and gas industry, and discuss a case study based on a medium sized oil field in the Basra province. We find that on average, firms design their Close Protection based on unconditional information, and sometimes overreacting to spikes in conflict activity that have limited bearing for the exposure at stake. Furthermore, in areas with extensive investments in counterinsurgency programs (e.g., Basra province), an appropriate use of spatio-temporal information can deliver average security cost savings of around 30% relative to the ‘unconditional’ benchmark, and of around 50% with respect to security postures driven by overreaction. Instead, areas featuring significant spatio-temporal clustering of events (e.g., Al-Anbar and Baghdad [Red Zone]) may require up to 50% more investment in mobile security.
• Financial Constraints and Risk Management: Evidence from the US P&C Insurance Market
  with Enrico Biffis
  
  Using a large and granular panel dataset of reinsurance transactions by US Property & Casualty (P&C) insurers, we examine the determinants for both risk management demand (as measured by reinsurance coverage) and quality of hedging instruments (as proxied by reinsurer’s counterparty risk). The results show that more financially constrained insurers purchase less reinsurance. However, financial constraints are positively correlated with the demand for more credit worthy and hence expensive reinsurance. In terms of insurer’s credit rating, higher rated insurers tend to prefer higher rated reinsurance. Conversely, lower quality insurers seek (or can afford) lower rated reinsurance. The findings are in line with empirical predictions of corporate risk management models showing that the costs of risk management may outweigh its benefits in the presence of financial constraints.
• Insurance contract design and endogenous frailty
  with Enrico Biffis
  
  We study how the design of options and guarantees can shape the exposure to mortality/longevity risk in life insurance contracts. We introduce a model of selective withdrawals, driven by exogenous and endogenous factors, offering insights into traditional approaches to the analysis of surrender guarantees and dynamic adverse selection. Via this model, the mortality risk profile of policyholders can be represented in terms of an endogenous frailty process shaped by the relative attractiveness of different contract benefits in different states of the world. This method does not require the knowledge of individual insureds’ health conditions, but only their exit times and reasons. This framework can be used, by both practitioners and researchers, for pricing, reserving and statistical testing of dynamic adverse selection in life insurance contracts.
• Large Commercial Exposures and Tail Risk: Evidence from the Asia-Pacific P&C Insurance Market
  with Enrico Biffis and Andreas Milidonis
  Download link
  

• Benedetti D., and Molnar R., (2020), Stress Testing Corporate Earnings of US Companies, In Data-Centric Business and Applications, Publisher: Springer, Cham, Pages: 347-370.
  Link
• Eastwood J., Hapgood M. A., Biffis E., Benedetti D., Bisi M. M., Green L., Bentley R. D., and Burnett C. (2018). Quantifying the economic value of space weather forecasting for power grids: an exploratory study, Space Weather: The International Journal of Research and Applications, , 16(12), pp.2052-2067.
  Link
• Benedetti D., Biffis E., and Milidonis A. (2015). Large Commercial Risks (LCR) in Insurance: Focus on Asia-Pacific, Insurance Risk and Finance Research Centre Technical report, Retrieved from www.irfrc.com.
  Download link

Teaching

MSc Risk Management and Financial Engineering - Imperial College Business School

• Data Management (Cleaning and Pre-Processing) in R
• Portfolio Performance Metrics in R
• Factor Investing with Machine and Deep Learning
• Lasso, Ridge and Elastic-Net
• Trees, Random Forests and Gradient Boosting
• Artificial Neural Networks
• Extensions: Ensemble Learning, Interpretability and Backtesting
• Validation and Tuning

MSc Finance & Accounting - Imperial College Business School

• Applications of R for Econometrics and Finance
• Momentum Strategies and Portfolio Performance Metrics in R
• Linear Models in R
• Instrumental Variables and SEM in R
• Time Series Analysis and Dynamic Linear Models in R

MSc Climate Change Management & Finance - Imperial College Business School

• Introduction to R
• Linear Models in R
• Instrumental Variables Regression in R
• Fixed-Effects Panel Model in R
• Time Series Analysis in R
• Geographic Information System in R

MRes/PhD Programme - Imperial College Business School

• Introduction to Econometrics
• Fundamentals of regression analysis I: Multivariate Regression Models
• Fundamentals of regression analysis II: Non-linear Regression Models
• Regression with binary dependent variable: Probit and Logit models
• Endogeneity: Instrumental Variables Regression
• Panel Data I: Fixed Effects, Clustered Covariance Matrix, Fixed Effects Vector Decomposition and its critique
• Panel Data II: Anderson-Hsiao's, Arellano-Bond's, Hausman-Taylor's estimators and Hausman's specification test
• Panel Data III: Incidental Parameters Problem, Concentrated estimators, Conditional Log-Likelihood, Mundlack's Approach to Probit model and Simulated Maximum Likelihood (SML)
• Time Series I: Stationarity, Ergodicity, Wold Decomposition Theoreom, Autoregressive Processes and ADL models
• Time Series II: Newey-West Robust SD, Non-Stationarity, Structural Breaks

MRes/PhD Programme - Imperial College Business School

• Cross section analysis and introduction to panel: OLS, GLS, IV and GMM estimators
• Fixed Effects model (FE), Fixed Effects Vector Decomposition (FEVD) and its critique
• Robust covariance matrix for the FE model
• Random Effects model (RE) and mixed RE-FE models: Mundlak's and Chamberlain's approach
• Parameter Heterogeneity: Swamy's Random Coefficients model
• Endogeneity in panel data: Anderson-Hsiao's, Arellano-Bond's and Hausman-Taylor's estimators
• Specification tests in econometrics: Hausman's specification test
• Non-linear panel data models: Incidental Parameters Problem, Concentrated estimators, Conditional Log-Likelihood, Random Effects Probit model and Simulated Maximum Likelihood (SML)
• Truncation and sample selection bias: Heckman correction and extensions

MSc Risk Management and Financial Engineering - Imperial College Business School

• Mortality Protection Gap: definition and estimation
• Valuation of Life Insurance Contracts: profit testing
• Longevity Risk and Stochastic Mortality: Lee-Carter and extensions, continuous time models
• Risk Quantification and Management: application of copulae and extreme value theory in Life Insurance
• Simulation Methods for Life Insurance: Monte Carlo applications
• Surrender Risk and Policyholder's Behaviour: generalized linear models for surrender risk, optimal policyholder's behaviour
• Advanced Simulation Methods: Nested Monte Carlo and Least Squares Monte Carlo
• Variable Annuity: pricing, management and product design
• Solvency II

Projects

PI: Dr. Erik Chavez (Imperial), CI: Dr. Enrico Biffis (Imperial)

Climate KIC, European Institute of Innovation and Technology

May 2016 - actual

PI: Dr. Jonathan Eastwood (Imperial), CI: Dr. Enrico Biffis (Imperial)

Met Office, Airbus, UCL-MSSL and RAL

April 2016 - November 2016

PI: Dr. Enrico Biffis (Imperial)

Grantham Insitute and Commonwealth Scientific and Industrial Research Organisation

November 2015 - April 2016

PI: Dr. Enrico Biffis (Imperial), CI: Davide Benedetti

FINMAR, Willis Group

September 2015 - June 2016

PI: Dr. Andreas Milidonis (University of Cyprus), CI: Dr. Enrico Biffis (Imperial)

Insurance Intellectual Capital Initiative Consortium, SCOR, Hiscox, Liberty

June 2015 - August 2015

PI: Prof. Ralf Toumi (Imperial)

Climate-KIC, European Institute of Innovation and Technology

July 2012 - June 2015

Visiting