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A HYBRID DERIVATIVE TRADING SYSTEM BASED ON VOLATILITY AND RETURN FORECASTING

机译:基于波动率和收益预测的混合衍生交易系统

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The following proposes a methodology that utilizes a generalized regression neural network to develop a hybrid option trading system that incorporates both volatility and return forecasting. This study focuses on the S&P 500 stock index as being representative of the market. Two different hybrid systems are discussed. The first hybrid system applies a signal from the volatility forecasting as a primary signal and then uses long and short straddle, strip, and strap strategies to take advantage of the volatility signal. The second hybrid system applies a signal from the return forecasting as a primary signal and then uses long calls and puts and bull and bear spread strategies to take advantage of the forecasting signal. The results show that the hybrid options trading model can improve the overall trading return and can outperform trading models using merely return forecasting or volatility forecasting in isolation. A sensitivity analysis for each trading system is investigated to observe the results for different values of critical option parameters.
机译:以下内容提出了一种利用广义回归神经网络开发结合了波动率和收益预测的混合期权交易系统的方法。这项研究着眼于代表市场的标准普尔500股指。讨论了两种不同的混合动力系统。第一个混合系统将来自波动率预测的信号用作主要信号,然后使用长短跨骑,剥离和带状策略来利用波动率信号。第二种混合系统将来自收益预测的信号用作主要信号,然后使用多头看跌期权和看跌期权以及牛市和熊市套利策略来利用预测信号。结果表明,混合期权交易模型可以提高整体交易收益,并且仅使用收益预测或波动率预测就可以胜过交易模型。研究每个交易系统的敏感性分析,以观察关键期权参数不同值的结果。

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