• English
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Latviešu
  • Magyar
  • Nederlands
  • Português
  • Português do Brasil
  • Suomi
  • Svenska
  • Türkçe
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Log In
  • Communities & Collections
  • Browse OpenUCT
  • English
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Latviešu
  • Magyar
  • Nederlands
  • Português
  • Português do Brasil
  • Suomi
  • Svenska
  • Türkçe
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Log In
  1. Home
  2. Browse by Author

Browsing by Author "Sebastian, Ponni"

Now showing 1 - 1 of 1
Results Per Page
Sort Options
  • Loading...
    Thumbnail Image
    Item
    Open Access
    Systematic asset allocation using flexible views for South African markets
    (2021) Sebastian, Ponni; Gebbie, Timothy
    We implement a systematic asset allocation model using the Historical Simulation with Flexible Probabilities (HS-FP) framework developed by Meucci [142, 144, 145]. The HS-FP framework is a flexible non-parametric estimation approach that considers future asset class behavior to be conditional on time and market environments, and derives a forward-looking distribution that is consistent with this view while remaining as close as possible to the prior distribution. The framework derives the forward-looking distribution by applying unequal time and state conditioned probabilities to historical observations of asset class returns. This is achieved using relative entropy to find estimates with the least distortion to the prior distribution. Here, we use the HS-FP framework on South African financial market data for asset allocation purposes; by estimating expected returns, correlations and volatilities that are better represented through the measured market cycle. We demonstrate a range of state variables that can be useful towards understanding market environments. Concretely, we compare the out-of-sample performance for a specific configuration of the HS-FP model relative to classic Mean Variance Optimization(MVO) and Equally Weighted (EW) benchmark models. The framework displays low probability of backtest overfitting and the out-of-sample net returns and Sharpe ratio point estimates of the HS-FP model outperforms the benchmark models. However, the results are inconsistent when training windows are varied, the Sharpe ratio is seen to be inflated, and the method does not demonstrate statistically significant outperformance on a gross and net basis.
UCT Libraries logo

Contact us

Jill Claassen

Manager: Scholarly Communication & Publishing

Email: openuct@uct.ac.za

+27 (0)21 650 1263

  • Open Access @ UCT

    • OpenUCT LibGuide
    • Open Access Policy
    • Open Scholarship at UCT
    • OpenUCT FAQs
  • UCT Publishing Platforms

    • UCT Open Access Journals
    • UCT Open Access Monographs
    • UCT Press Open Access Books
    • Zivahub - Open Data UCT
  • Site Usage

    • Cookie settings
    • Privacy policy
    • End User Agreement
    • Send Feedback

DSpace software copyright © 2002-2026 LYRASIS