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 looks offered below 1.1900
EUR/USD keeps its bearish tone unchanged ahead of the opening bell in Asia, returning to the sub-1.1900 region following a firmer tone in the US Dollar. Indeed, the pair reverses two consecutive daily gains amid steady caution ahead of Wednesday’s key US Nonfarm Payrolls release.
GBP/USD slips back to daily lows near 1.3640
GBP/USD drops to daily lows near 1.3640 as sellers push harder and the Greenback extends its rebound in the latter part of Tuesday’s session. Looking ahead, the combination of key US releases, including NFP and CPI, alongside important UK data, should keep the pound firmly in focus over the coming days.
USD/JPY extends backslide as election fallout bolsters Yen
USD/JPY is trading in a choppy, range-bound structure on the daily chart, oscillating between the January high near 159.450 and the late-January swing low at 152.100. Price closed Monday at 154.410, dropping sharply by 1.47 yen (0.94%) after an initial gap higher following Prime Minister Takaichi's landslide election victory was met with verbal intervention from Finance Minister Katayama and Japan's top currency official Mimura, both signaling readiness to act on yen volatility.
Editors’ Picks
When are the China’s CPI, PPI and how could they affect AUD/USD?
The National Bureau of Statistics of China will publish its data for January at 01.30 GMT. The Consumer Price Index is expected to show a rise of 0.4% YoY in January, compared to 0.8% in December. The Producer Price Index is projected to show a decline of 1.5% in January versus a fall of 1.9% prior.
USD/JPY extends backslide as election fallout bolsters Yen
USD/JPY is trading in a choppy, range-bound structure on the daily chart, oscillating between the January high near 159.450 and the late-January swing low at 152.100. Price closed Monday at 154.410, dropping sharply by 1.47 yen (0.94%) after an initial gap higher following Prime Minister Takaichi's landslide election victory was met with verbal intervention from Finance Minister Katayama and Japan's top currency official Mimura, both signaling readiness to act on yen volatility.
Gold declines to near $5,050, focus shifts to US jobs data
Gold price falls to near $5,045 during the early Asian session on Wednesday. Traders assess whether prices have found a floor following a historic sell-off. The delayed US employment report for January, which was pushed back due to the recently ended four-day government shutdown, will take center stage later on Wednesday.
Ethereum: Whales buy the dip amid rising short bets
Following one of Ethereum's largest weekly drawdowns, whales are slowly returning to action alongside a drop in retail selling pressure. After slightly selling into the decline at the start of the month, whales or wallets with a balance of 10K-100K ETH began buying the dip last Wednesday as prices crashed further.
Dollar drops and stocks rally: The week of reckoning for US economic data
Following a sizeable move lower in US technology Stocks last week, we have witnessed a meaningful recovery unfold. The USD Index is in a concerning position; the monthly price continues to hold the south channel support.
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