商品期货投资组合与市场收益的尾部相依研究

doi:10.16381/j.cnki.issn1003-207x.2021.1646

Abstract

Abstract:

The diversified allocation of asset portfolios interested many scholars and investors. Additionally， diversifying existing risk factor portfolios is a common strategy for portfolio allocation. However， diversified allocation of factor strategies may not be as advantageous when there is a high correlation between factors. Meanwhile， the Chinese commodity futures market is highly active， and its unique market environment causes different manifestations of risk factors. It is interesting to investigate the correlation between different factors in the Chinese commodity market. It focuses on whether commodity futures factors are applicable in the Chinese futures market and whether the allocation of risk factors can help investors optimize assets.The qpr quantile association model is employed to effectively capture tail dependence. Building upon the work of Li Ruosha et al.， the application of covariates is further expanded upon by integrating the mixed data model MIDAS （Mixed-Data Sampling Model） . Given the relatively short historical data available in China's financial market， the utilization of the MIDAS method becomes crucial. By amalgamating high-frequency daily data， this approach not only enriches the available data information but also enhances the model's fitting capabilities.In this paper， daily high-frequency commodity index and stock index data are used to describe the weekly tail dependence between risk factor returns and market return. Regarding the model： 1） Comparing the basic qpr model， the qpr-MIDAS model can better fit the tail dependence results； 2） the model can realize the diversification return with the allocation of investment portfolios in economic applications. The empirical results show that： 1） there is no obvious spillover relationship between risk factor combinations and market returns except during crises； 2） among all the factors， the momentum factor performs best， with high portfolio return and with low correlation with the market factor； 3） the risk spillover effect negative link with the returns of commodity indexes and stock indexes； 4） Based on our comparative analysis of commodity futures portfolios before and after the change in financial institutions’ access policies， it is determined that the relaxation of policies can help control the intensity of risk spillover. The above research results provide a theoretical reference for Chinese commodity futures investment.The contribution of this paper is that a new tail dependence measurement model qpr-MIDAS is constructed， which can more accurately capture the tail spillover effect under multi-frequency conditions， and explains the advantages of this model from both statistical and economic levels. In addition， based on the perspective of China's commodity futures market， the literature about the law of tail dependence between investment portfolios is extended.This extension serves to offer valuable insights and guidance tailored specifically for Chinese commodity futures investors， thereby enriching their understanding of market dynamics and facilitating more informed investment decisions.

Key words: commodity futures portfolio, tail dependence, MIDAS, quantile association regression model

CLC Number:

F830

Ranran Guo,Wuyi Ye,Xiaoquan Liu, et al. The Tail Dependence Between Commodity Futures Portfolios:Based on qpr-MIDAS Model[J]. Chinese Journal of Management Science, 2024, 32(10): 11-19.

Figures/Tables 8

指数类型	变量	VOL	MOM	FX	LIQ	SK	TS	HP	SPECU	OI
商品指数	$β 0$	0.0711	-0.0282	0.8762^*	-0.0051	0.2426	0.1571	-0.1398	0.1929	0.5733^*
	$β 0$	(0.2113)	(-0.0768)	(3.8811)	(-0.0137)	(0.6915)	(0.5623)	(-0.3749)	(0.602)	(2.3942)
	$β m i d a s$	-5.62^*	3.2196	-4.6552^*	2.7646	3.1705	-9.402^*	-9.2577^*	2.9747^*	0.7137
	$β m i d a s$	(-1.2957)	(0.9811)	(-1.3214)	(0.7102)	(0.8675)	(-4.5554)	(-4.236)	(1.8711)	(0.2909)
	BIC	-5.4538	-5.5203	-5.0843	-5.52	-5.3961	-5.279	-5.4446	-5.3962	-5.2526
股票指数	$β 0$	0.1158	-0.0016	0.8507^*	0.0137	0.1839	0.2677	-0.0549	0.2862	0.5671^*
	$β 0$	(0.3426)	(-0.0043)	(3.616)	(0.0378)	(0.5413)	(0.9265)	(-0.1411)	(0.9424)	(2.3873)
	$β m i d a s$	-2.838	1.5145	-3.2224^*	1.4006	-3.7065^*	-5.4037^*	-5.0227^*	-0.332	-1.13
	$β m i d a s$	(-1.0081)	(0.8021)	(-1.5318)	(0.4306)	(-1.7513)	(-2.4917)	(-2.5664)	(-0.0975)	(-0.5208)
	BIC	-5.4527	-5.5198	-5.0844	-5.5198	-5.397	-5.3024	-5.456	-5.3954	-5.2524

	商品指数为协变量的参数结果
变量	VOL	MOM	FX	LIQ	SK	TS	HP	SPECU	OI
$β 0$	-0.0658	-0.0166	0.8807^*	-0.0487	0.0335	0.3241	0.1521	-0.0107	0.3029
$β 0$	(-0.1616)	(-0.0436)	(3.9700)	(-0.1146)	(0.0829)	(0.9327)	(0.4232)	(-0.0287)	(0.9383)
$β 1$	-0.0701	0.0553	-0.018	-0.0573	0.0502	0.1917	0.0119	-0.0194	0.0089
$β 1$	(-0.5167)	(0.2122)	(-0.1602)	(-0.3251)	(0.2521)	(0.906)	(0.0583)	(-0.1983)	(0.0503)
$β 2$	-0.2893^*	0.0431	-0.1166	0.0509	0.0025	-0.1385^*	-0.0686	-0.3252^*	-0.0025
$β 2$	(-1.8982)	(0.3135)	(-1.0053)	(0.3732)	(0.0216)	(-1.3534)	(-0.4777)	(-3.867)	(-0.0273)
$β 3$	0.0685	0.1035	-0.0333	-0.1416	-0.3130^*	-0.1325	-0.0435	0.0221	0.2330^*
$β 3$	(0.3937)	(0.629)	(-0.3252)	(-0.6517)	(-2.4609)	(-0.7392)	(-0.2338)	(0.2278)	(1.8705)
$β 4$	-0.0047	-0.0009	-0.0636	0.1044	0.0193	-0.0192	0.0479	0.1228^*	0.2158^*
$β 4$	(-0.0241)	(-0.0026)	(-0.6242)	(0.4166)	(0.1504)	(-0.1058)	(0.2691)	(1.3225)	(1.5952)
	BIC值
商品	-5.4362	-5.5054	-5.064	-5.5077	-5.3692	-5.2794	-5.4367	-5.3765	-5.2406
股指	-5.5006	-5.4533	-5.2490	-5.2846	-5.0671	-5.5018	-5.4347	-5.4043	-5.3998

变量	VOL	MOM	FX	LIQ	SK	TS	HP	SPECU	OI
	商品期货指数为协变量的结果
$β 0$	0.8027^*	-0.0042	1.6644^*	0.0358	0.8142^*	1.0929^*	0.7574^*	0.8068^*	0.8176^*
$β 0$	(1.9881)	(-0.0051)	(7.1639)	(0.0588)	(2.0151)	(3.812)	(1.818)	(2.0373)	(2.2544)
$β 2012$	-1.5295^*	-0.0926	-1.9772^*	-0.5435	-1.1294^*	-1.4132^*	-2.2638^*	-1.1283^*	-0.4377
$β 2012$	(-1.5386)	(-0.098)	(-3.0687)	(-0.6782)	(-1.6996)	(-2.0939)	(-2.1447)	(-1.509)	(-0.7795)
$β m i d a s$	0.8959	3.1237	0.1061	4.9533^*	-0.1993	0.9043	2.4582^*	0.9803	-0.1475
$β m i d a s$	(0.7071)	(1.2596)	(0.0897)	(2.1373)	(-0.2258)	(0.3486)	(1.9085)	(0.3168)	(-0.1209)
	股票指数为协变量的结果
$β 0$	0.7479^*	0.1177	1.6666^*	0.3505	0.8096^*	1.0003^*	0.9595^*	0.7589^*	0.7887^*
$β 0$	(2.0213)	(0.1716)	(7.1946)	(0.704)	(1.916)	(3.2672)	(2.6017)	(1.749)	(2.1532)
$β 2012$	-1.4880^*	-0.1602	-1.9824^*	-0.715	-1.1378^*	-1.3461^*	-2.3709^*	-1.1446^*	-0.4379
$β 2012$	(-1.5492)	(-0.1827)	(-3.0727)	(-0.9269)	(-1.7255)	(-2.0271)	(-2.2175)	(-1.5233)	(-0.7853)
$β m i d a s$	-2.3172	1.2391	0.5733	4.0969	1.2819	-2.1520^*	-0.4044	2.3039^*	2.6685
$β m i d a s$	(-0.9766)	(0.551)	(0.3761)	(0.6046)	(0.5473)	(-1.4854)	(-0.3974)	(1.7357)	(0.6742)

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描述性统计量	VOL	MOM	FX	LIQ	SK	TS	HP	SPECU	OI	市场收益
均值	0.029	0.276^*	0.100	-0.047	0.128	0.147^*	-0.080	0.081	0.273^*	0.007
方差	2.189	2.831	2.517	2.579	2.141	2.307	2.743	2.604	2.069	1.793
夏普比例	0.013	0.097	0.040	-0.018	0.059	0.063	-0.030	0.031	0.132	0.004

商品	均值	1.1852	1.0355	2.5185	1.0355	1.3314	1.6272	1.1834	1.3314	1.7778
商品	标准差	0.7601	0.3875	0.9931	0.3026	0.3973	3.4385	2.3145	0.6561	0.2371
股指	均值	1.1845	1.0369	2.517	1.0364	1.3301	1.6256	1.1822	1.3312	1.7773
股指	标准差	0.4631	0.2796	1.1673	0.2024	0.7605	1.5653	1.1499	0.0526	0.2644

参数	qpr商品	qpr股指	高斯Copula	反转Gumble	协方差	等权重
	A．样本内
均值	0.2428^*	0.1286^*	0.1003^*	0.1287^*	0.1037^*	0.1009^*
t值	(3.3191)	(5.1089)	(2.7199)	(2.1354)	(2.4469)	(2.1311)
方差	0.7882	1.2319	1.226	1.2181	1.0983	1.5652
夏普比例	0.1154	0.189	0.0968	0.0742	0.0853	0.0758
	B．样本外
均值	0.1298	0.2230^*	0.0548	0.1645	0.1	0.0692
t值	(1.1208)	(1.9443)	(0.5783)	(1.0819)	(0.9028)	(0.8687)
方差	1.258	1.2459	1.0293	1.6517	1.2032	0.8653
夏普比例	0.0952	0.171	0.0435	0.0935	0.0748	0.0684

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The Tail Dependence Between Commodity Futures Portfolios:Based on qpr-MIDAS Model

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