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Betty · 2024年04月08日

为什么risk-factor based optimization most robust?

NO.PZ2019122802000036

问题如下:

Which of the following statement about Monte Carlo Simulation in alternative asset allocation is not correct?

选项:

A.

Monte Carlo Simulation relaxes the assumption of normally distributed return.

B.

Monte Carlo Simulation combines the high and low volatility normal distributions to lead a skewed and fat-tailed distribution for alternative investments.

C.

Monte Carlo Simulation is the most robust asset allocation approach.

解释:

C is correct.

Risk-factor based optimization is the most robust asset allocation approach.


蒙特卡洛模拟来做另类的资产配置的时候,核心的目标是去模拟一个skewed and fat-tailed 收益与风险的分布;具体方法是这三步:

确定low-volatility 以及 high-volatility两种情况下的风险因子,确定好之后,用蒙特卡洛模拟建议不同情况下的收益模型,然后再把这个不同情况下的收益模型叠加起来,就能构建一个skewed and fat-tailed模型,这样就符合另类投资的收益风险特征了。

这是讲义当中写的具体步骤哈:

Estimate the behavior of factors and/or assets in low-volatility regimes and high-volatility regimes

then generating scenarios using the different means and covariances estimated under the different regimes.

This mixture of high-and low-volatility normal distributions would lead to an altogether skewed and fat-tailed distribution of asset class return or risk factor changes.

为什么risk-factor based optimization most robust?

1 个答案
已采纳答案

pzqa35 · 2024年04月09日

嗨,努力学习的PZer你好:


因为risk factor是从风险的本质来源对投资进行分类,而不是单单根据表面上这些资产长得像不像来分类,就比如REITs虽然是房地产的投资,但是其实它跟股票的相关性还是较高的,所以如果仅从表面上看这两者并不像,如果用这两个去做分散化的话,其实效果是不好的,因为它们本身的相关性较高。但是如果是从risk factor角度去分类,那就避免了这种情况的出现,它是从本质上发掘风险的来源,因此不同的分类之间是有更好的分散化效果,所以如果利用它来构建组合,往往更为稳健。

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