NO.PZ2018113001000088
问题如下：
Emerging market currency trading is subject to relatively frequent extreme events and market
pressures. As a result, the probability distribution of returns for emerging
market investments shows a larger tail than the normal distribution，which usually used to assess the performance of investments in
developed markets. In addition, there is a significant negative skew in the
probability distribution of returns in emerging markets compared to developed
markets (normal distribution).
Analyze the reasons why VAR is used to
manage risk in emerging markets,and sharpe ratio is used to measure historical
performance is inappropriate.
选项：
解释：
Answer:
The probability distribution of returns for
emerging market investments shows a larger tail than the normal distribution, and
there is a significant negative skew in the probability distribution of returns
in emerging markets.
Given these differences, risk management
and control tools that rely on normal distribution (such as VAR) can be
misleading in extreme market conditions and significantly underestimate the
risks to a portfolio.
Similarly, many of the investment
performance indicators, such as the Sharpe ratio, used to assess performance
are based on normal distribution. So when market conditions are stable,
historical performance as measured by these indicators can look very attractive,
but this apparent outperformance can disappear into heavy losses faster than
most investors react.
Therefore,
the "model" of its emerging market portfolio should not be assumed to
be normally distributed. Instead, a larger, negatively skewed return
probability distribution should be assumed to better reflect exposure to
extreme events.
中文解析：
新兴市场外汇交易受到相对频繁的极端事件和市场压力的影响。因此，新兴市场投资的回报概率分布比通常用于评估发达市场投资表现的正态分布表现出更大的尾部。此外，与发达市场(正态分布)相比，新兴市场的回报概率分布也有明显的负偏态。
鉴于这些差异，在极端市场条件下，依赖于正态分布的风险管理和控制工具(如VAR)可能会产生误导，并大大低估了投资组合所面临的风险。同样，许多用于评估业绩的投资业绩指标也是基于正态分布的。因此，当市场状况稳定时，用夏普比率(Sharpe ratio)等指标评估的历史业绩可能看起来非常有吸引力，但这种明显的出色表现可能会以比大多数投资者反应得更快的速度消失在严重亏损中。
新兴市场的短期稳定可能会给投资者一种过度自信的错觉，从而鼓励基于正态分布回报错觉的过度配置。因此，不应假定其新兴市场投资组合的“模型”为正态分布，而应该假设一个更大的、负偏态的回报概率分布，更好地反映极端事件的风险暴露。
简要地说就是数据不一定都是正态分布的。所以VaR和夏普比率都不一定准确
