An algorithm for moment-matching scenario generation with application to financial portfolio optimisation

A Heuristic for Moment-Matching Scenario Generation

Financial Portfolio Optimisation

HMM based scenario generation for an investment optimisation problem

The Geometric Brownian motion (GBM) is a standard method for modeling financial time series. An important criticism of this method is that the parameters of the GBM are assumed to be constants; due to this fact, GBM has been considered unable to properly capture important features, like extreme behaviour or volatility clustering. We propose an approach by which, the parameters of the GBM follow...

Multi-stage scenario generation by the combined moment matching and scenario reduction method

We describe an opportunity to speed up multi-stage scenario generation and reduction using a combination of two well known methods: the moment matching method (Høyland and Wallace 2001) and the method for scenario reduction to approximately minimize a metric (Heitsch and Römish 2009). Our suggestions is to combine them rather than using them in serial by making use of a stage-wise approximation...

Portfolio Optimization: Scenario Generation, Models and Algorithms

In single-period portfolio optimization several facets of the problem may influence the goodness of the portfolios selected. Despite that, some of these facets are frequently ignored when the optimization problem is solved. In this thesis, we aim at investigating the impact of these facets on the optimization problem and on the performances of the portfolios selected. Firstly, we consider the p...