**Types of Volatility**

Volatility is one of the factors that investors in the financial markets analyze when making trading decisions. There are two key approaches to volatility, each with its pros and cons:

**Implied Volatility:**

The term implied volatility describes the estimated or forecasted volatility of an asset and it is a common feature of options trading. Implied volatility reflects how the marketplace views where volatility should be in the future, but it does not forecast the direction that the asset’s price will move. Generally, an asset’s implied volatility rises in a bear market (Put buying increases) because most investors predict that its price will continue to drop over time. It decreases in a bull market since traders believe that the price is bound to rise over time. This is down to the common belief that bear markets are inherently riskier compared to bullish markets. Implied Volatility is one of the measures that traders use to estimate future fluctuations of an asset price on the basis of several predictive factors.**Realised / Historical Volatility:**

Realised volatility, also known as historical volatility (HV), is a way of statistically measuring how the returns from a particular asset or market index are dispersed when analyzed over a given timeframe. Normally, historical volatility is measured by establishing the average deviation of a financial instrument from its average price over a given period of time. Standard deviation tends to be the most common measure of realized volatility, though there are other methods used to calculate this metric. Risky security is one that has a high historical volatility value though, in certain types of trades, it is not necessarily a negative factor since both bullish and bearish conditions could be risky. In relation to these two metrics, historical volatility (backward-looking) serves as a baseline measure, with implied volatility (forward-looking) defining the relative values of asset prices.

If the two metrics show similar values, then an asset is considered to be fairly priced on the basis of historical norms. For this reason, traders look for deviations from this equilibrium to establish if assets are overvalued or undervalued.

**The Standard Deviation Model of Assessing Financial Volatility**

Standard deviation is a measure used to statistically determine the level of dispersion or variability around the average price of a financial asset, making it a suitable way to measure market volatility. In general terms, dispersion is the differential between an asset’s average value and its actual value. The higher the dispersion or variability, the higher the standard deviation is. The lower the variation is, the lower the standard deviation. Analysts often use standard deviation as a means of measuring expected risk and determining how significant a price movement is.

When calculating the standard deviation of volatility, the variance of a data set of prices of an underlying asset must be derived. The standard deviation is the square root of variance. For purposes of illustration, we will consider the price of an underlying asset that has rallied uniformly from $1 to $10 in 10 trading periods. Standard deviation will be derived in the following steps:

- Calculate the mean for the 10 trading days. This is done by adding the prices together ($1, $2….to $10) and then dividing it by 10 (in this case, the total number of prices). The sum of 55 divided by 10 will be $5.5.
- Determine the deviation from the mean at each period. This is the difference between the closing price and the mean. For instance, on the 7th day, the price of $7 deviates from the $5.5 mean by 2.5.
- Square the deviation of each period. All the periods with negative deviations will be eliminated by squaring them.
- Sum the squared deviations. As per our example, the sum is $82.5
- Divide the sum by the number of periods, in this case, 10. This will be $8.25.
- The standard deviation is the square root of this number. In this case, the standard deviation is $2.75 which reflects how values are spread out around the average price, giving traders insight as to how far the asset price may deviate from the average.

As the calculation above shows, standard deviation as **a measure of risk assumes that the data set follows a normal distribution,** or what is referred to as a bell curve. In such a scenario, as above, 68% of data will fall within one standard deviation; 95% will fall within two standard deviations, and 99.7% of data will fall within three standard deviations.

But there are a few limitations to using standard deviation as a measure of volatility. To start with, prices or returns are never uniform, and they are punctuated by periods of sharp spikes in either direction. This will mean that the standard deviation itself may experience fluctuations depending on the periods that are taken into consideration during the calculation. There is also the **beta (β) method** for measuring or calculating volatility. In this method, an underlying asset’s volatility is measured against other related assets. For instance, the volatility of Apple stock can be measured against the overall volatility of other technology sector stocks or even an entire benchmark stock index. Learn more about how this model of volatility assessment is calculated as well as its significance in risk management.

**Why Volatility Matters?**

**Volatility Affects Trader Sentiment**

Analysing market sentiment is an essential part of financial data analysis. Prices of assets traded on the financial markets will usually move up and down on a daily basis – a natural effect of the stochastic behavior of the financial market. In spite of these price movements, hundreds of millions of investors worldwide continue to risk their money in the financial market, hoping to make returns in the future.

The volatility of the financial markets is of interest to investors since **high levels of volatility often come with the chance of huge profits or significant losses** at the expense of higher uncertainty. If volatility is extremely high, investors may choose to stay away from the markets in fear of losing their funds. Others might engage in riskier trading in the hope of earning higher profits.

**Volatility Affects Trading Costs**

Volatility is a fact of investing life, and it guides or affects various decisions that investors have to make in the market. In general, high volatility implies high inherent risk, but it also means high reward opportunity. Money is made out of price changes in the markets, but high volatility carries additional risks as well.

In CFD markets, high volatility typically widens the spreads of underlying assets. This can directly impact overall profit potential or investing goals. The high volatility witnessed during the release of major economic news and events of underlying assets is a testament to this.

In **options trading**, high volatility has the effect of increasing premiums (which is essentially the cost of an option contract). This is because of the perceived higher likelihood that a highly volatile asset has of hitting any relevant strike price and thus, expire in the money. Additionally, volatility can influence decisions on capital allocation and portfolio rebalancing.

Typically, less volatile assets will be allocated a higher proportion of capital than more volatile ones. This can trickle down to position sizes with investors likely to trade more volatile assets with smaller lot sizes. Volatile assets can also skew the performance of an overall portfolio, and this may prompt investors to rebalance to achieve stability.