【摘要】：波動(dòng)擇時(shí)策略是一種根據(jù)資產(chǎn)波動(dòng)以及相關(guān)性構(gòu)建投資組合的方法,具有較為廣泛的應(yīng)用。鑒于此,提出以波動(dòng)擇時(shí)績(jī)效的經(jīng)濟(jì)意義指標(biāo)比較已實(shí)現(xiàn)協(xié)方差矩陣的預(yù)測(cè)模型。用高頻數(shù)據(jù)構(gòu)建股指期貨、國(guó)債期貨和黃金期貨的已實(shí)現(xiàn)協(xié)方差矩陣,利用簡(jiǎn)單移動(dòng)平均模型、指數(shù)加權(quán)移動(dòng)平均模型和混合數(shù)據(jù)抽樣回歸模型對(duì)協(xié)方差矩陣進(jìn)行一步向前滾動(dòng)窗預(yù)測(cè),然后在均值-方差框架下根據(jù)預(yù)測(cè)協(xié)方差構(gòu)建動(dòng)態(tài)投資組合,并通過(guò)經(jīng)濟(jì)效益指標(biāo)對(duì)不同模型的預(yù)測(cè)進(jìn)行比較評(píng)價(jià)。實(shí)證結(jié)果表明,在股市上升階段,用簡(jiǎn)單長(zhǎng)期移動(dòng)平均模型預(yù)測(cè)已實(shí)現(xiàn)協(xié)方差矩陣時(shí)波動(dòng)擇時(shí)策略表現(xiàn)最好;在股市下跌階段,用簡(jiǎn)單短期移動(dòng)平均模型則更優(yōu);而用指數(shù)加權(quán)移動(dòng)平均和混合數(shù)據(jù)抽樣回歸模型時(shí)波動(dòng)擇時(shí)策略表現(xiàn)則始終居中。
[Abstract]:Volatility timing strategy is a method to construct portfolio based on asset volatility and correlation, which is widely used. In view of this, a prediction model of covariance matrix is proposed to compare the economic significance index of volatility timing performance with that of realized covariance matrix. The realized covariance matrix of stock index futures, treasury bonds futures and gold futures is constructed with high frequency data, and a simple moving average model is used. The exponential weighted moving average model and the mixed data sampling regression model are used to predict the covariance matrix in one step forward rolling window, and then the dynamic portfolio is constructed according to the prediction covariance under the framework of mean-variance. The prediction of different models is compared and evaluated by economic benefit index. The empirical results show that in the rising stage of the stock market, it is better to use the simple long-term moving average model to predict the realized covariance matrix, and to use the simple short-term moving average model in the stage of stock market decline, and to use the simple short-term moving average model to predict the realized covariance matrix. However, the performance of volatility timing strategy with exponential weighted moving average and mixed data sampling regression model is always in the middle.
【作者單位】： 南京大學(xué)工程管理學(xué)院;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(71201075,71671084) 高等學(xué)校博士學(xué)科點(diǎn)專項(xiàng)科研基金資助項(xiàng)目(20120091120003)
【分類號(hào)】：C934
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本文編號(hào)：2264867