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Pairs trading is a popular speculation strategy. Several implementation methods are proposed in the literature: they can be based on a distance criterion or on co-integration. This article extends previous research in another direction: the combination of forecasting techniques (Neural Networks) and multi-criteria decision making methods (Electre III). The key contribution of this paper is the introduction of multi-step-ahead forecasts. It leads to major changes in the trading system and raises new empirical and methodological questions. The results of an application based on S&P 100 Index stocks are promising: this methodology could be a powerful tool for pairs selection in a highly non-linear environment.  相似文献
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Pairs trading is a popular quantitative speculation strategy. This article proposes a general and flexible framework for pairs selection. The method uses multiple return forecasts based on bivariate information sets and multi-criteria decision techniques. Our approach can be seen as a sort of forecast combination but the output of the method is a ranking. It helps to detect potentially under- and overvalued stocks. A first application with S&P 100 index stocks provides promising results in terms of excess return and directional forecasting.  相似文献
3.
This paper investigates the robust optimal pairs trading using the concept of equivalent probability measures and a penalty function associated with the confidence in parameter estimates when the parameters in the drift term of the continuous-time cointegration model are estimated with errors. A closed-form solution is derived for the robust pairs trading rule. We compare the robust pairs trading rule against its non-robust counterpart using simulations and real data. The robust strategy is empirically more stable and less volatile.  相似文献
4.
对基于协整理论的配对交易策略进行了改进。改进后的模型利用计算机能够快速循环运算的特点,循环查找最优配对组合与建仓阈值,使模型能够快速运用到各类资产及多种数据频率的配对交易中,具有根据数据变化进行自我动态修正的功能。  相似文献
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