Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Portfolio Optimization: A DCC-GARCH forecast with implied volatility
Linnaeus University, School of Business and Economics, Department of Management Accounting and Logistics.
Linnaeus University, School of Business and Economics, Department of Management Accounting and Logistics.
2019 (English)Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This thesis performs portfolio optimization using three allocation methods, Certainty Equivalence Tangency (CET), Global Minimum Variance (GMV) and Minimum Conditional Value-at-Risk (MinCVaR). We estimate expected returns and covariance matrices based on 7 stock market indices with a DCC-GARCH model including an ARMA (1.1) process and an external regressor of an implied volatility index (VIX). We then simulate returns using a rolling window of 500 daily observations and construct portfolios based on the allocation methods. The results suggest that the model can sufficiently estimate expected returns and covariance matrices and we can outperform benchmarks in form of equally weighted and historical portfolios in terms of higher returns and lower risk. Over the whole out-of-sample period the CET portfolio yields the highest mean returns and GMV and MinCVaR can significantly lower the variance. The inclusion of VIX has marginal effects on the forecasting accuracy and it seems to impair the estimation of risk.

Place, publisher, year, edition, pages
2019. , p. 80
Keywords [en]
DCC-GARCH, Portfolio Optimization, Certainty Equivalence Tangency, CET, Global Minimum Variance, GMV, Minimum Conditional Value-at-Risk, MinCVaR, Implied volatility index, VIX
National Category
Business Administration
Identifiers
URN: urn:nbn:se:lnu:diva-85992OAI: oai:DiVA.org:lnu-85992DiVA, id: diva2:1331920
Subject / course
Business Administration - Management Accounting
Educational program
Business Administration and Economics Programme, 240 credits
Supervisors
Examiners
Available from: 2019-08-06 Created: 2019-06-27 Last updated: 2019-08-06Bibliographically approved

Open Access in DiVA

fulltext(1325 kB)31 downloads
File information
File name FULLTEXT01.pdfFile size 1325 kBChecksum SHA-512
b96e9d3be6ad6e06ada7fba300f53ae9714126cc6caa108c5170ea759f20ff0f5044ba6add2dbcbe50a78d28ffcfdf648950b61380d367dfba2395ec1ada194f
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Bigdeli, SamBengtsson, Filip
By organisation
Department of Management Accounting and Logistics
Business Administration

Search outside of DiVA

GoogleGoogle Scholar
Total: 31 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 49 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf