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Author: Dimitrios I. Vortelinos Publisher: ISBN: Category : Languages : en Pages :
Book Description
The intraday nonparametric estimation of the variance-covariance matrix adds to the literature in portfolio analysis of the Greek equity market. This paper examines the economic value of various realized volatility and covariance estimators under the strategy of volatility timing. I use three types of portfolios: Global Minimum Variance, Capital Market Line and Capital Market Line with only positive weights. The estimators of volatilities and covariances use 5-min high-frequency intraday data. The dataset concerns the FTSE/ATHEX Large Cap index, FTSE/ATHEX Mid Cap index, and the FTSE/ATHEX Small Cap index of the Greek equity market (Athens Stock Exchange). As far as I know, this is the first work of its kind for the Greek equity market. Results concern not only the comparison of various estimators but also the comparison of different types of portfolios, in the strategy of volatility timing. The economic value of the contemporary non-parametric realized volatility estimators is more significant than this when the covariance is estimated by the daily squared returns. Moreover, the economic value (in b.p.s) of each estimator changes with the volatility timing.
Author: Dimitrios I. Vortelinos Publisher: ISBN: Category : Languages : en Pages :
Book Description
The intraday nonparametric estimation of the variance-covariance matrix adds to the literature in portfolio analysis of the Greek equity market. This paper examines the economic value of various realized volatility and covariance estimators under the strategy of volatility timing. I use three types of portfolios: Global Minimum Variance, Capital Market Line and Capital Market Line with only positive weights. The estimators of volatilities and covariances use 5-min high-frequency intraday data. The dataset concerns the FTSE/ATHEX Large Cap index, FTSE/ATHEX Mid Cap index, and the FTSE/ATHEX Small Cap index of the Greek equity market (Athens Stock Exchange). As far as I know, this is the first work of its kind for the Greek equity market. Results concern not only the comparison of various estimators but also the comparison of different types of portfolios, in the strategy of volatility timing. The economic value of the contemporary non-parametric realized volatility estimators is more significant than this when the covariance is estimated by the daily squared returns. Moreover, the economic value (in b.p.s) of each estimator changes with the volatility timing.
Author: Samuel Köppel Publisher: GRIN Verlag ISBN: 3346719480 Category : Business & Economics Languages : de Pages : 29
Book Description
Akademische Arbeit aus dem Fachbereich BWL - Investition und Finanzierung, Universität Mannheim, Sprache: Deutsch, Abstract: In the first step of my analysis, I provide some descriptive statics of daily overnight and intraday returns in the German equity market. Followed by this, we will discuss evidence for differences in the return-/beta relation and make an answer to our first Hypothesis. Then we will go over to the momentum analysis and show their predictive power for momentum and reversal strategies based on overnight and intraday return signals and take a quick look on where it happens (intraday vs. overnight) like Baradehi et al. (2022) and Lou et al. (2015). For explaining the different behavior of stock returns, we first start with the basic idea of a multiple factor model where exists multiple priced risk factors whose covariance matrix varies between the day and night. In case of different behavior of intraday and overnight returns there are existing different types of risk factors which can predict the expected stock return. To explain the relation between returns and beta we determine that the risk-return relationship is positive only during specific times, for example in January (Tinic and West, 1984), during months of low inflation (Cohen et al., 2005) or days with news about economic trends like inflation or unemployment. By looking at returns of the CDAX decomposed into its intraday and overnight returns we see a distinctively different behavior.
Author: Yafei Zhang Publisher: ISBN: Category : Languages : en Pages : 70
Book Description
Abstract: Estimation of the covariance matrix of asset returns is a key component of portfolio optimization. Inherent in any estimation technique is the capacity to inaccurately reflect current market conditions. Typical of Markowitz portfolio optimization theory, which we use as the basis for our analysis, is to assume that asset returns are stationary. This assumption inevitably causes an optimized portfolio to fail during a market crash since estimates of covariance matrices of asset returns no longer re ect current conditions. We use the market crash of 2008 to exemplify this fact. A current industry standard benchmark for estimation is the Ledoit covariance matrix, which attempts to adjust a portfolio's aggressiveness during varying market conditions. We test this technique against the CALM (Covariance Adjustment for Liability Management Method), which incorporates forward-looking signals for market volatility to reduce portfolio variance, and assess under certain criteria how well each model performs during recent market crash. We show that CALM should be preferred against the sample convariance matrix and Ledoit covariance matrix under some reasonable weight constraints.
Author: Gregory D. McArthur Publisher: ISBN: Category : Languages : en Pages : 70
Book Description
Abstract: Estimation of the covariance matrix of asset returns is a key component of portfolio optimization. Inherent in any estimation technique is the capacity to inaccurately reflect current market conditions. Typical of Markowitz portfolio optimization theory, which we use as the basis for our analysis, is to assume that asset returns are stationary. This assumption inevitably causes an optimized portfolio to fail during a market crash since estimates of covariance matrices of asset returns no longer re ect current conditions. We use the market crash of 2008 to exemplify this fact. A current industry standard benchmark for estimation is the Ledoit covariance matrix, which attempts to adjust a portfolio's aggressiveness during varying market conditions. We test this technique against the CALM (Covariance Adjustment for Liability Management Method), which incorporates forward-looking signals for market volatility to reduce portfolio variance, and assess under certain criteria how well each model performs during recent market crash. We show that CALM should be preferred against the sample convariance matrix and Ledoit covariance matrix under some reasonable weight constraints.
Author: Dimitrios I. Vortelinos
Author: Dimitrios I. Vortelinos
Author: Samuel Köppel
Author: G. V. G. Stevens
Author: Guy V. G. Stevens
Author: 
Author: Cheng F. Lee
Author: Michiel David Pooter
Author: Yafei Zhang
Author: Gregory D. McArthur
Author: Luca Manera