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Efficient Risk Factor Allocation with Regime Based Models

Axelsson, Oscar (2017) FMS820 20171
Mathematical Statistics
Abstract
It is widely accepted that nancial mark behaviour is characterized by periodicity. However, in
academia and practice financial markets are often modeled as time consistent, resulting in static
investment strategies that are assumed to be ecient. This has great implications on investors'
allocation decisions and in valuation of risks and performances.
In this thesis we consider different risk factors and their behaviour over time. By using a hidden
market model, which allow the risk factors to jump between 2 dierent regimes, we are able to
identify distinctly different behaviour for all the risk factors in the different regimes. We are then
able to design different investment strategies and use an online implementation of the model... (More)
It is widely accepted that nancial mark behaviour is characterized by periodicity. However, in
academia and practice financial markets are often modeled as time consistent, resulting in static
investment strategies that are assumed to be ecient. This has great implications on investors'
allocation decisions and in valuation of risks and performances.
In this thesis we consider different risk factors and their behaviour over time. By using a hidden
market model, which allow the risk factors to jump between 2 dierent regimes, we are able to
identify distinctly different behaviour for all the risk factors in the different regimes. We are then
able to design different investment strategies and use an online implementation of the model that
field significant better returns than equivalent static investment strategies.
Our results give further proof of the benefits of using regime based models when working with
portfolio decisions. We also suggest a shrinkage approach to the covariance matrix estimations
that gives increased stability to the model and makes it highly applicable in practice. (Less)
Please use this url to cite or link to this publication:
author
Axelsson, Oscar
supervisor
organization
course
FMS820 20171
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Risk Factors, Hidden Markov Models, Regime Models, Asset Allocation, Ecient Frontier, Shrinkage Factor, Baum-Welch Algorithm, EM algorithm.
language
English
id
8926049
date added to LUP
2017-09-20 13:14:13
date last changed
2017-09-21 07:47:32
@misc{8926049,
  abstract     = {It is widely accepted that nancial mark behaviour is characterized by periodicity. However, in
academia and practice financial markets are often modeled as time consistent, resulting in static
investment strategies that are assumed to be ecient. This has great implications on investors'
allocation decisions and in valuation of risks and performances.
In this thesis we consider different risk factors and their behaviour over time. By using a hidden
market model, which allow the risk factors to jump between 2 dierent regimes, we are able to
identify distinctly different behaviour for all the risk factors in the different regimes. We are then
able to design different investment strategies and use an online implementation of the model that
field significant better returns than equivalent static investment strategies.
Our results give further proof of the benefits of using regime based models when working with
portfolio decisions. We also suggest a shrinkage approach to the covariance matrix estimations
that gives increased stability to the model and makes it highly applicable in practice.},
  author       = {Axelsson, Oscar},
  keyword      = {Risk Factors,Hidden Markov Models,Regime Models,Asset Allocation,Ecient Frontier,Shrinkage Factor,Baum-Welch Algorithm,EM algorithm.},
  language     = {eng},
  note         = {Student Paper},
  title        = {Efficient Risk Factor Allocation with Regime Based Models},
  year         = {2017},
}