Quantitative Risk Solutions Lab

RESEARCH Expertise​

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Actuarial Mathematics & Statistics:

• Demographic Statistics: population dynamics, environment and population spatial-temporal models, climate change and population, pollution and health, deprivation indexes​

• General insurance: claims reserving methods, claims processes and loss process modelling, risk measures​

• Investment Decision Making: dynamic portfolio decision-making, price process modelling, wealth management and pension, Environmental Social Governance (ESG) and Divestment Practices.​

• Life insurance and Longevity: stochastic mortality, morbidity & population modelling.

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Econometrics and Computational Finance:

• Time series methods: time series regressions, panel regressions, isotonic regressions, functional time series regressions, causality testing, VARMAX models, dynamic copula models, state-space models, spatial-temporal models, cointegration methods, univariate and multivariate stochastic volatility models, Hawkes process, counting processes, Natural Language Processing time series for sentiment

• Application Domains: commodities, equity portfolios, Exchange Traded Funds, fixed income
  and bonds, environmental finance, digital finance - blockchain and crypto 

 

Risk Management:

• Operational Risk: loss modelling, capital calculations and approximation, Loss Distributional Approach, Key Risk Indicators, dependence and copula modelling, cyber risk, natural disaster risk analysis

• Portfolio Risk Analysis: optimal risk-adjusted investment decision-making, allocation principles, risk measure approximations, dynamic risk models.

• Stress Testing Models: analysis of portfolio stress testing, fixed income interest rate stress testing.

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Statistics:

• Time Series: methodology and application, forecasting models, Bayesian time series methods, panel, term structure, state-space, non-linear, non-stationary methods, decomposition methods, machine learning methods in time series

• Multivariate Analysis: Copula methods, Feature Extraction Kernel Methods, multiple output Gaussian Process Models, extreme value theory and heavy-tailed processes

• Monte Carlo Methods and Sampling: SMC Samplers, MCMC, RJ-MCMC, Cross Entropy, Variational approximation

• Spatial-Temporal Modelling: Gaussian process models, warped Gaussian processes, extreme value processes, quantile processes, graph regressions, tensor processes in space and time

• Statistical Signal Processing:  multi-modal spatial process field reconstruction, sensor networks, participatory sensing and privacy, location verification methods, optimal network routing, communications engineering and channel modelling, detection and receiver design.