If there is one area that is regularly ignored by CFD traders it is that of volatility, which is often confused with risk. Certainly in terms of grading different types of asset classes, the two are connected, and both the risk and volatility of a government stock for instance will usually be much lower than say a dot.com or emerging market smaller company.
But the bottom line is that risk is related to reward, and it simply measures the amount that it is possible to lose within each investment or trade. Volatility however measures how much prices rise or fall over a set time for each investment issue, sector or share, and this is very useful when constructing portfolios, assessing margin requirements and position sizing.
Standard Deviation – the basic measure of volatility
Standard Deviation is the basic statistical measure of the dispersion of a population of data observations around a mean (average), and is widely used in stockmarket trading, forex and commodity analysis. It is simply the square root of the variance, and is calculated as follows:
1. Establish the mean value over the chosen time period.
2. Measure the deviation of each data point from that mean.
3. Square each deviation (this ensures all the deviations are positive).
4. Total up the squared deviations.
5. Divide that figure by the number of data points less one.
6. The Standard deviation is the square root of that figure.
There are some variations on the way the STD can be constructed, but the above is the usual formula supplied with most trading software systems.
Problems with standard deviation
1. If using short term action, the validity of the STD becomes less certain due to the usual short term randomness in the market.
2. It is a retrospective measurement, and is of little use if there is a major change in volatility due to outside news. Having said that, there are certain technical buy and sell indicators which search for changes in volatility to establish potential new trading opportunities, and here it is very useful.
Implied Volatility
Many traders in the options markets will be aware of the use of implied volatility in terms of option pricing, and here the trader can use both the underlying price of the security and the prices of puts (rights to sell) and calls (rights to buy) to establish an expectation of future or implied volatility.
This creates arbitrage possibilities if the stock, or market, is incorrectly priced compared to underlying options available in it, and these disparities often occur after big price moves or panicky action. The formula for implied volatility is much more complex, but it is an interesting area for more sophisticated players to analyse, as it also includes dividend payments and interest rates.
What is beta?
Beta is another measure of volatility, and whilst totally different from standard deviation, it nevertheless provides another angle in portfolio or trade construction.
Standard deviation determines the volatility of a fund, market, sector or stock according to the disparity of its returns over a period of time, whereas beta determines the volatility in comparison to an index or other benchmark.
If an investor has a portfolio of shares with a beta of 1, this means that the list should generally match the underlying movement in that benchmark over time. It doesn’t mean that it will naturally perform better or worse on an individual stock basis, but if the FTSE 100 index was to rally by say 10% over one year, the portfolio with a beta of 1 would in total expect to improve by a similar amount.
On a trading level, each stock has its own beta which is important for CFD traders, and a beta of more than 1 suggests greater volatility than the benchmark, with a beta of less than 1 suggesting lower volatility.
A stock with a beta of 2 for instance would be expected to move 2 times more than the benchmark, or double the underlying index move. Clearly if a CFD trader has a balanced list of positions in terms of longs and shorts, the average beta on each side needs to be assessed in terms of the overall risk of big market moves in one direction.
Normally, but not always, the highest beta stocks are those with the greatest volatility as measured by the standard deviation, but also how much they are affected by the business cycle and interest rates. Fund managers, housebuilders and insurance companies for instance have much higher betas than supermarkets, pharmaceuticals and utility stocks.
In portfolio analysis, the beta coefficient, or financial elasticity (sensitivity of the asset returns to market returns and relative volatility), is a key parameter in the capital asset pricing model and is a way of separating an investor’s profits related to market action as opposed to the willingness to take risk. In essence this means how much added value there has been as opposed to just the luck from being in rising markets.
If one is highly bullish about the underlying market, it makes it easier to beat the market over the term in question by choosing high beta stocks. Equally, if a big fall is expected imminently, a CFD trader might prefer to take low beta long positions and high beta shorts if a balanced trading list was required.
The average true range indicator
This is an important indicator that can be used for setting stops and is also another way of measuring volatility, and is included in most software systems.
The ATR determines a share’s volatility over a set period that can be defaulted as desired. The daily ATR indicator is very simple to calculate and is the highest of:
The difference between the current high and the current low
The difference between the current high and the previous close
The difference between the current low and the previous close
Basically this is the maximum range in which the share has traded from the previous close to the current high and low. The average is then taken over a set number of days (ten is often used), and the stop is then calculated as a multiple of the ATR.
The reason traders like the ATR is that it captures more intra-day information, while the standard deviation only measures the volatility of closing prices (although it can be refined to include highs, lows, etc).
Reasons for volatility and what to look for
On a short term view, shares that have quotes in more than one market or currency may exhibit high volatility, but not necessarily a high beta. This is simply because of arbitrage possibilities, where traders buy the stock on one market and sell in another to take advantage of price discrepancies.
Changes in technology naturally affect the volatility of individual stocks because it takes a while for this information to become available to the wider investment community, so a period of volatility often ensues. Once the stock becomes more mainstream or loses its super-growth tag, volatility can often die down.
News-led events often lead to big changes in volatility, again as traders and investors begin to adjust expectations for future prices. This can include profit upgrades or warnings, unexpected changes in economic policy, natural disasters or geopolitical events.
If the volatility increases for the same investment amount, so does the potential risk and reward and trade sizes/stop losses should be adjusted accordingly for CFD traders.
Mike Estrey is the Head of Research for Blue Index, specialists in Online CFD Trading and Contracts for Difference and Online Forex Trading
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http://EzineArticles.com/?Volatility-and-Risk-in-Stockmarket-Trading&id=702018
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