Christian has been working in industry for the past 7 years; 4 years in commercial property finance in the London, UK, and 3 years as a bond trader and portfolio manager for the United Nations in Rome, Italy. He brings with him a wealth of industry working knowledge, which will be of great use for solving practical issues in his thesis.
Estimation, Inference and Forecasting for Value-at-Risk and Conditional Value-at-Risk using Bayesian GARCH Methods and High Frequency Intra-Day Data
Financial institutions are constantly looking for better ways to both manage and quantify risk following the regulatory disclosure of Value-at-Risk (VaR). An incorrect estimation could jeopardize the returns or risk profile of an institution, leading to loss of confidence from stakeholders. The proposed model introduces high frequency intra-day data into a traditionally daily GARCH specification via mixed data sampling, MIDAS. The goal is to increase accuracy, especially for short term forecasts of both VaR and Conditional VaR. Monte Carlo Markov Chain (MCMC) methods will be employed within a Bayesian framework for inference and parameter estimation. The resulting models should provide application in risk management, derivative pricing and portfolio optimization.
Supervisor: Professor Richard Gerlach.