高維條件協(xié)方差矩陣的非線性壓縮估計(jì)及其在構(gòu)建最優(yōu)投資組合中的應(yīng)用
發(fā)布時(shí)間:2018-04-25 19:43
本文選題:非線性壓縮 + 線性壓縮; 參考:《中國管理科學(xué)》2017年08期
【摘要】:本文將非線性壓縮方法運(yùn)用到DCC和BEKK模型中,用非線性的壓縮估計(jì)量代替MMLE估計(jì)中初始的樣本協(xié)方差矩陣,大大提高了高維DCC和BEKK模型的估計(jì)效率,并突破性地使得橫截面維度大于時(shí)間維度時(shí),DCC和BEKK模型的有效估計(jì)成為可能。蒙特卡洛模擬發(fā)現(xiàn):非線性壓縮方法對于DCC和BEKK模型估計(jì)的優(yōu)化作用顯著,且優(yōu)化程度隨著橫截面維度和時(shí)間維度的比值增大而增加。實(shí)證分析進(jìn)一步說明了非線性壓縮方法對于準(zhǔn)確估計(jì)高維條件協(xié)方差矩陣、從而提高組合選擇效率的重要作用。
[Abstract]:In this paper, the nonlinear compression method is applied to the DCC and BEKK models. The initial sample covariance matrix in the MMLE estimation is replaced by the nonlinear compression estimator, which greatly improves the estimation efficiency of the high-dimensional DCC and BEKK models. It also makes it possible to estimate the BEKK and DCC models when the cross section dimension is larger than the time dimension. Monte Carlo simulation shows that the nonlinear compression method plays an important role in the estimation of DCC and BEKK models, and the degree of optimization increases with the ratio of cross section dimension to time dimension. The empirical analysis further illustrates the importance of nonlinear compression method in estimating the high dimensional conditional covariance matrix accurately and improving the efficiency of combination selection.
【作者單位】: 華中科技大學(xué)經(jīng)濟(jì)學(xué)院;
【基金】:國家自然科學(xué)基金面上資助項(xiàng)目(71671070)
【分類號】:F830.59;O212.1
【相似文獻(xiàn)】
相關(guān)期刊論文 前10條
1 周兆經(jīng);程捷;陳千;;采用協(xié)方差矩陣評定測量不確定度的方法[J];中國計(jì)量學(xué)院學(xué)報(bào);1991年01期
2 周兆經(jīng);估算測量不確定度的最大熵法和協(xié)方差矩陣法[J];遙測遙控;1995年04期
3 呂維;王志杰;李建辰;王明洲;胡橋;;混響空時(shí)協(xié)方差矩陣的兩種計(jì)算方法比較與分析[J];魚雷技術(shù);2012年04期
4 灻昭a(bǔ)v;;關(guān)于N≡3(mod 4)的最優(yōu)秤重,
本文編號:1802685
本文鏈接:http://www.sikaile.net/jingjilunwen/touziyanjiulunwen/1802685.html
最近更新
教材專著