Author: Oratile Ame KgosietsilePublisher:
ISBN:
Category : Stock price forecasting
Languages : en
Pages : 90
Book Description
Modelling and Forecasting the Volatility of JSE Returns
Author: Oratile Ame KgosietsilePublisher:
ISBN:
Category : Stock price forecasting
Languages : en
Pages : 90
Book Description
Modeling and Forecasting Stock Return Volatility in the JSE Securities Exchange
Author: Zamani Calvin MasingaPublisher:
ISBN:
Category : Stock price forecasting
Languages : en
Pages : 134
Book Description
Modelling and forecasting stock return volatility and the term structure of interest rates
Author: Michiel de PooterPublisher: Rozenberg Publishers
ISBN: 9051709153
Category :
Languages : en
Pages : 286
Book Description
This dissertation consists of a collection of studies on two areas in quantitative finance: asset return volatility and the term structure of interest rates. The first part of this dissertation offers contributions to the literature on how to test for sudden changes in unconditional volatility, on modelling realized volatility and on the choice of optimal sampling frequencies for intraday returns. The emphasis in the second part of this dissertation is on the term structure of interest rates.
Modelling and Forecasting Volatility of JSE Sectoral Indices
Author: Matthew SongPublisher:
ISBN:
Category : Options (Finance)
Languages : en
Pages : 80
Book Description
Modeling and Forecasting Daily Stock Return Volatility with Intra-day Price Fluctuation Information
Author: Yansong LuModeling and Forecasting Stock Return Volatility
Author: Pia GrammigThe Ability of GARCH Models in Forecasting Stock Volatility on the JSE Limited
Author: Tholoana MokoenaPublisher:
ISBN:
Category : Forecasting
Languages : en
Pages :
Book Description
Latent Common Return Volatility Factors
Author: Mingmian ChengPublisher:
ISBN:
Category :
Languages : en
Pages : 44
Book Description
In this paper, we use factor-augmented HAR-type models to predict the daily integrated volatility of asset returns. Our approach is based on a proposed two-step dimension reduction procedure designed to extract latent common volatility factors from a large dimensional and high-frequency returns dataset with 267 constituents of the S&P 500 index. In the first step, we apply either LASSO or elastic net shrinkage on estimates of integrated volatility of all constituents in the dataset, in order to select a subset of asset return series for further processing. In the second step, we utilize (sparse) principal component analysis to estimate latent common asset return factors, from which latent integrated volatility factors are extracted. Although we find limited in-sample fit improvement, relative to a benchmark HAR model, all of our proposed factor-augmented models result in substantial out-of-sample predictive accuracy improvement. In particular, forecasting gains are observed at market, sector, and individual-stock levels, with the exception of the financial sector. Further investigation of the factor structures for non-financial assets shows that industrial and technology stocks are characterized by minimal exposure to financial assets, inasmuch as forecasting gains associated with factor-augmented models for these types of assets are largely attributable to the inclusion of non-financial stock price return volatility in our latent factors.
Modeling Return Volatility on the JSE Sectors
Author: Publisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages : 149
Book Description
Modelling -- Volatility -- Johannesburg Stock Exchange -- ARCH -- GARCH -- EGARCH -- TGARCH -- JSE sectors -- Models.
Author: Eugenie Hol