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.
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 Processesâ€.
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!
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 Processesâ€.
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!
Editors’ Picks
EUR/USD holds around 1.1750 after weak German and EU PMI data
EUR/USD maintains its range trade at around 1.1750 in European trading on Tuesday. Weaker-than-expected December PMI data from Germany and the Eurozone make it difficult for the Euro to find demand, while investors refrain from taking large USD positions ahead of key employment data.
GBP/USD climbs above 1.3400 after upbeat UK PMI data
GBP/USD gains traction and trades in positive territory above 1.3400 on Tuesday as the British Pound benefits from upbeat PMI data. Later in the day, crucial data releases from the US, including Nonfarm Payrolls, Retail Sales and PMI, could trigger the next big action in the pair.
Japanese Yen seems poised to appreciate further; awaits BoJ decision on Friday
The Japanese Yen maintains its bid tone through the first half of the European session on Tuesday which, along with a bearish US Dollar, keeps the USD/JPY pair depressed below the 155.00 psychological mark. The growing acceptance that the Bank of Japan will raise interest rates this week turns out to be a key factor behind the safe-haven JPY's outperformance.
Editors’ Picks
How will US Dollar react to October and November NFP? – LIVE
The US Bureau of Labor Statistics (BLS) will publish the official employment report for the first time since the government reopened. Nonfarm Payrolls figures for October and November could provide key insights into the labor market conditions and influence the market pricing of the Fed's rate outlook.
EUR/USD holds around 1.1750 after weak German and EU PMI data
EUR/USD maintains its range trade at around 1.1750 in European trading on Tuesday. Weaker-than-expected December PMI data from Germany and the Eurozone make it difficult for the Euro to find demand, while investors refrain from taking large USD positions ahead of key employment data.
GBP/USD climbs above 1.3400 after upbeat UK PMI data
GBP/USD gains traction and trades in positive territory above 1.3400 on Tuesday as the British Pound benefits from upbeat PMI data. Later in the day, crucial data releases from the US, including Nonfarm Payrolls, Retail Sales and PMI, could trigger the next big action in the pair.
Gold retreats from seven week highs on profit-taking; all eyes on US NFP release
Gold price loses momentum below $4,300 during the early European trading hours on Tuesday, pressured by some profit-taking and weak long liquidation from the shorter-term futures traders. Furthermore, optimism around Ukraine peace talks could weigh on the safe-haven asset like Gold.
Ukraine-Russia in the spotlight once again
Since the start of the week, gold’s price has moved lower, but has yet to erase the gains made last week. In today’s report we intend to focus on the newest round of peace talks between Russia and Ukraine, whilst noting the release of the US Employment data later on day and end our report with an update in regards to the tensions brewing in Venezuela.
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