Python 中 N 项资产的投资组合的投资组合方差
投资组合方差计算如下:
port_var = W'_p * S * W_p
对于具有 N 资产的投资组合,其中
W'_p = transpose of vector of weights of stocks in portfolios
S = sample covariance matrix
W_p = vector of weights of stocks in portfolios
我有以下 numpy 矩阵。
投资组合中股票权重的数组(向量)(有10只股票):
weights = np.array(
[[ 0.09],
[ 0.05],
[ 0.15],
[ 0.10],
[ 0.15],
[ 0.15],
[ 0.08],
[ 0.08],
[ 0.1 ],
[ 0.05]])
股票收益的协方差矩阵:
covar = np.array([[ 0.00154474 0.00079555 0.00099691 0.00052596 0.0005363 0.00062005
0.00064031 0.00037494 0.00018826 0.00132809],
[ 0.00079555 0.00287429 0.00058536 0.00091774 0.00046885 0.00110434
0.00137141 0.00046724 0.00030414 0.0016615 ],
[ 0.00099691 0.00058536 0.00155757 0.00056336 0.00052395 0.00060104
0.00057223 0.00021365 0.00017057 0.00130247],
[ 0.00052596 0.00091774 0.00056336 0.00126312 0.00031941 0.00088137
0.00024493 0.00025136 0.00011519 0.00135475],
[ 0.0005363 0.00046885 0.00052395 0.00031941 0.00054093 0.00045649
0.00042927 0.00021928 0.00016835 0.00093471],
[ 0.00062005 0.00110434 0.00060104 0.00088137 0.00045649 0.00133081
0.00060353 0.0003967 0.00024983 0.00168281],
[ 0.00064031 0.00137141 0.00057223 0.00024493 0.00042927 0.00060353
0.00468731 0.00059557 0.00020384 0.00078669],
[ 0.00037494 0.00046724 0.00021365 0.00025136 0.00021928 0.0003967
0.00059557 0.00082333 0.00017191 0.00066816],
[ 0.00018826 0.00030414 0.00017057 0.00011519 0.00016835 0.00024983
0.00020384 0.00017191 0.00036348 0.0004505 ],
[ 0.00132809 0.0016615 0.00130247 0.00135475 0.00093471 0.00168281
0.00078669 0.00066816 0.0004505 0.00530036]])
当我计算时,
weights.T * covar * weights
结果是一个与covar大小相同的数组。我是投资组合理论的新手,但我认为投资组合的方差应该是标量(单个值)。
有没有人有这方面的经验可能会有所帮助?
Portfolio variance is calculated as:
port_var = W'_p * S * W_p
for a portfolio with N assest where
W'_p = transpose of vector of weights of stocks in portfolios
S = sample covariance matrix
W_p = vector of weights of stocks in portfolios
I have the following numpy matrixes.
Array (vector) of weights of stocks in the portfolio (there are 10 stocks):
weights = np.array(
[[ 0.09],
[ 0.05],
[ 0.15],
[ 0.10],
[ 0.15],
[ 0.15],
[ 0.08],
[ 0.08],
[ 0.1 ],
[ 0.05]])
Covariance matrix of stock returns:
covar = np.array([[ 0.00154474 0.00079555 0.00099691 0.00052596 0.0005363 0.00062005
0.00064031 0.00037494 0.00018826 0.00132809],
[ 0.00079555 0.00287429 0.00058536 0.00091774 0.00046885 0.00110434
0.00137141 0.00046724 0.00030414 0.0016615 ],
[ 0.00099691 0.00058536 0.00155757 0.00056336 0.00052395 0.00060104
0.00057223 0.00021365 0.00017057 0.00130247],
[ 0.00052596 0.00091774 0.00056336 0.00126312 0.00031941 0.00088137
0.00024493 0.00025136 0.00011519 0.00135475],
[ 0.0005363 0.00046885 0.00052395 0.00031941 0.00054093 0.00045649
0.00042927 0.00021928 0.00016835 0.00093471],
[ 0.00062005 0.00110434 0.00060104 0.00088137 0.00045649 0.00133081
0.00060353 0.0003967 0.00024983 0.00168281],
[ 0.00064031 0.00137141 0.00057223 0.00024493 0.00042927 0.00060353
0.00468731 0.00059557 0.00020384 0.00078669],
[ 0.00037494 0.00046724 0.00021365 0.00025136 0.00021928 0.0003967
0.00059557 0.00082333 0.00017191 0.00066816],
[ 0.00018826 0.00030414 0.00017057 0.00011519 0.00016835 0.00024983
0.00020384 0.00017191 0.00036348 0.0004505 ],
[ 0.00132809 0.0016615 0.00130247 0.00135475 0.00093471 0.00168281
0.00078669 0.00066816 0.0004505 0.00530036]])
When I compute
weights.T * covar * weights
The result is an array the same size as covar. I am new to portfolio theory but I would imagine that the variance of the portfolio should be a scalar (single value).
Does anyone have experience with this that might help?
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对于 2D numpy 数组,np.dot 相当于矩阵乘法。
对于 2D 数组 np.dotted 和 1D 数组,
np.dot
相当于矩阵向量乘法。对于一维数组,
np.dot
相当于内积。对于 numpy 数组,
*
执行逐元素乘法(如有必要,使用 广播 )。或者,如果将
covar
转换为np.matrix
,则*
相当于矩阵乘法。For 2D numpy arrays,
np.dot
is equivalent to matrix multiplication.For a 2D array np.dotted with a 1D array,
np.dot
is equivalent to matrix-vector multiplication.For 1D arrays,
np.dot
is equivalent to the inner product.For numpy arrays, the
*
performs element-wise multiplication (with broadcasting if necessary).Alternatively, if you convert
covar
to anp.matrix
, then*
is equivalent to matrix multiplication.