传统的投资风险被认为是资产的市场价值与真实价值之间的波动或标准偏差。标准偏差表示的是投资价值与平均收益之间的波动幅度。投资的产品越不稳定，它的价格就越有可能围绕历史平均值波动（利弊共存），也就说明该资产或投资类别的风险性越大。

比如，下图1显示了全球股市与全球证券的正常回报对比。用摩根士丹利世界指数表示（MSCI）代表全球股市，全球证券由花旗集团(Citigroup)投资级债券指数表示。

如果在过去十年中你同时投资全球股市与证券，那么现在你的投资组合价值应该几乎相等，分别为168美元以及165美元（股市的年风险收入比是非常令人沮丧的，这一点要另当别论）。然而，全球股市波动性比证券大得多，因此从定义上来讲，它的风险也更大。按照日回报率的时间序列来计算它们的标准偏差的话，那么全球股市为2.53% 而全球证券则为0.50%。

图 1: 全球股市& 全球证券10年的正常回报率

 
有许多衡量风险的传统方法比如风险价值分析法或条件风险价值（CVaR）。这些方法现在已经被大量的投资者沿用至今。
然而，与我密切相关同时也对我来说最重要的是爆仓的风险。这些年来，我的灵感以及经验来源于许多市场专家和基金经理。他们成功地塑造了我的交易思维并且看到风险的观念，其中“交易成本亏损的风险”是最重要的一点。
那么，我们要如何量化亏损风险？我所知道的两种方法，它们来自D.R. Cox与H.D Miller所著的《随机过程理论》。

1、固定交易规模、仓位不变（比如，无论交易本金如何变化，持仓手数不变）。

R= 失去交易资本z的风险百分比（概率）。
e = 自然对数的指数， 2.71828。
z = 如果我们想计算失去账户一半本金的风险，那么z值输入0.5.
a = 交易的平均回报，需要与d同一个周期。比如，如果a使用日平均回报，那么d就使用日回报的标准偏差。如果a使用周平均回报，那么d就使用周回报平均偏差。
d=回报的标准偏差，需要与前面提到的平均回报处于一个时间框架。

2、固定交易百分比（比如，本金的2%）。

R=失去交易资本z的风险百分比（概率）。
e =自然对数的指数， 2.71828。
ln(1-z) = (1-z)的自然对数
z =如果我们想计算失去账户一半本金的风险，那么z值输入0.5.
a = 交易的平均回报，需要与d同一个周期。比如，如果a使用日平均回报，那么d就使用日回报的标准偏差。如果a使用周平均回报，那么d就使用周回报平均偏差。
d=回报的标准偏差，需要与前面提到的平均回报处于一个时间框架。
你或许希望将这些算法应用于计算资金管理工具中，并计算出交易中最重要的交易风险。保持交易的长久性，是一场在外汇交易中生存下来的游戏。

Live To Trade Another Day
In traditional investing, risk is viewed as volatility or standard deviation of the asset’s marked to market value. Standard deviation tells you how much an investment’s value will fluctuate from the average return. The more volatile the investment is likely to swing (both positively and negatively) around it’s own historical average, the more risky an investment or asset class is.

For example, Chart 1 below shows the normalised returns of Global Equities, as represented by MSCI World Index, versus Global Bonds, as represented by Citigroup Broad Investment Grade Bonds Index.

If you had invested $100 in both Global Equities and Global Bonds for the past 10 years, your portfolio value of either would be fairly similar, which is $168 for Global Equities and $165 for Global Bonds. (pretty dismal annualised returns per unit risk for Global Equities but that’s another story altogether) However, Global Equities is more volatile than Global Bonds and hence more risky by definition. The standard deviation of a time series of daily returns for Global Equities is 2.53% versus 0.50% for Global Bonds. 

Chart 1: Normalised Returns For Past 10 Years For Global Equities & Global Bonds

There are many more traditional measures of risk such as Value-at-Risk or Conditional-Value-at-Risk, which is an extension of VAR. These measures are widely used by the vast majority of investors for many years now.

However the most important and relevant risk to me when I trade is the Risk of Ruin. There were many sources of inspiration and influences (the “Market Wizards” type of traders and successful fund managers) through the years in shaping my thoughts on trading and this concept of looking at risk as the “risk of losses of trading capital” has been one of the most important.

So how do we quantify the risk of ruin? I came across these 2 methods as described below. They were referenced from D.R. Cox and H.D Miller in “The Theory of Stochastic Processes”.

For fixed trade size without dynamic position sizing (i.e. fixed trade size regardless of trading capital changes)
 
R= Risk of losing z fraction of the trading capital in percentage terms (probability)
e = Base of natural logarithm, 2.71828
z = If we want to calculate the risk of losing half the account, input 0.5
a = mean return of the trades, must be same time frame as d. For example if daily mean returns are used, then use standard deviation of daily returns. If weekly mean returns are used, then use standard deviation of weekly returns.
d = standard deviation of returns, must be same time frame as mean returns mentioned earlier.

For fixed trade percentage (e.g. 2% of capital per trade)
 
R= Risk of losing z fraction of the trading capital in percentage terms (probability)
e = Base of natural logarithm, 2.71828
ln(1-z) = natural logarithm of (1-z)
z = If we want to calculate the risk of losing half the account, input 0.5
a = mean return of the trades, must be same time frame as d. For example if daily mean returns are used, then use standard deviation of daily returns. If weekly mean returns are used, then use standard deviation of weekly returns.
d = standard deviation of returns, must be same time frame as mean returns mentioned earlier.

You may wish to incorporate these calculations in your money management tools to give you an idea of the risk of ruin which is so important in trading. Live to trade another day. It is all about survival in this game!

本文翻译由兄弟财经提供

文章来源：http://www.fxstreet.com/education/technical/live-to-trade-another-day/2014/07/23/