{"id":6370,"date":"2021-11-14T20:21:39","date_gmt":"2021-11-14T12:21:39","guid":{"rendered":"https:\/\/slashtraders.com\/?p=6370"},"modified":"2025-09-29T17:34:09","modified_gmt":"2025-09-29T09:34:09","slug":"market-volatility-hv-iv","status":"publish","type":"post","link":"https:\/\/slashtraders.com\/en\/blog\/market-volatility-hv-iv\/","title":{"rendered":"Know Your Market Volatility, a Complete Guide to HV and IV"},"content":{"rendered":"

When trading options for profit, a rule of thumb is to sell options when the Implied Volatility is high, and buy options when the IV is lower.<\/p>\n\n\n\n

But do you know what market volatility represents in general and how differences in volatility can affect options prices?<\/p>\n\n\n\n

In this article, we'll try to provide an in-depth explanation of what market volatility is, because a solid understanding of volatility is important to trading options for consistent income<\/a>. We also share the differences between Historical Volatility (HV) and Implied Volatility (IV).<\/p>\n\n\n\n

What Is Volatility?<\/h2>\n\n\n\n

Volatility is the fluctuations and dispersion of data points, and we can use it to indicate the fluctuation of stock prices in the market. Mathematically speaking, volatility is the deviation of the data dispersion.<\/p>\n\n\n\n

Thinkorswim<\/a> shares various market volatility data in the Options Statistics. The most important of them all are Historical Volatility (HV) and Implied Volatility (IV).<\/p>\n\n\n\n

What Are the Differences Between HV and IV?<\/h2>\n\n\n\n

HV is the backward-looking<\/a> volatility of the underlying, while IV is forward-looking for the theoretically expected future moves.<\/p>\n\n\n\n

How to Calculate Historical Volatility?<\/h2>\n\n\n\n

In case you have left your high school years long behind you, we\u2019ll take it step by step through the process of calculating volatility.<\/p>\n\n\n\n

We calculated the volatility of the stock price with reference to a series of recent BABA closing prices. Volatility calculated using historical data is commonly referred to as historical volatility (HV).<\/p>\n\n\n\n

We first calculate the daily changes of 10 recent BABA closing prices.<\/p>\n\n\n\n\n\n\n\t\n\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t
Trading days<\/th>Last<\/th>Daily change<\/th>\n<\/tr>\n<\/thead>\n
10\/4<\/td>139.63<\/td><\/td>\n<\/tr>\n
10\/5<\/td>143.14<\/td>2.5%<\/td>\n<\/tr>\n
10\/6<\/td>144.10<\/td>0.7%<\/td>\n<\/tr>\n
10\/7<\/td>156.00<\/td>8.3%<\/td>\n<\/tr>\n
10\/8<\/td>161.52<\/td>3.5%<\/td>\n<\/tr>\n
10\/11<\/td>163.95<\/td>1.5%<\/td>\n<\/tr>\n
10\/12<\/td>163.00<\/td>-0.6%<\/td>\n<\/tr>\n
10\/13<\/td>167.40<\/td>2.7%<\/td>\n<\/tr>\n
10\/14<\/td>166.78<\/td>-0.4%<\/td>\n<\/tr>\n
10\/15<\/td>168.00<\/td>0.7%<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n\n\n

The standard deviation of these 10 daily changes is 2.54%<\/p>\n\n\n\n

Since there are around 252 trading days each year, we can transform the 2.54% standard deviation into an annualised number of 40.3%, which is HV.<\/p>\n\n\n\n

\"annual<\/figure>\n\n\n\n

Then we can compare our HV number with the Thinkorswim HV data.<\/p>\n\n\n\n

\"baba
Thinkorswim outlines several volatility values for each underlying.<\/figcaption><\/figure>\n\n\n\n

We deduce the Thinkorswim HV from the HV Percentile formula to get a similar number to our calculated HV.<\/p>\n\n\n\n

\"hv<\/figure>\n\n\n\n

The famous Gaussian normal distribution describes the random outcomes of a data set in our real world where 50% of all outcomes fall on the left side of the mean and the other 50% on the right side of the mean.<\/p>\n\n\n\n

\"normal
The standard deviation outlines the probability of data points in a normal distribution.<\/figcaption><\/figure>\n\n\n\n

1 standard deviation (\u03c3) on either side of the mean mark the range in which 68% of all fluctuations of the given process would fall. While +\/-2\u03c3 mark the probability of 95%.<\/p>\n\n\n\n

The current closing price for BABA is $158.73, while HV is 40%. So we have a 68% confidence that BABA closing prices will stay within $95.24 and $222.22 in the following year.<\/p>\n\n\n\n

If BABA\u2019s closing prices fluctuated more with a bigger standard deviation, its HV would be high.<\/p>\n\n\n\n

If the closing prices fluctuated less with a smaller standard deviation, the HV would be low.<\/p>\n\n\n\n

How Does IV Affect Options Prices?<\/h2>\n\n\n\n

Option prices are a function of IV, which is the expected move of the underlying. IV is also calculated from the options pricing model.<\/p>\n\n\n\n

Options provide us with rights to sell or buy an asset at a certain price<\/a>. Such right is inherently associated with a certain cash inflow or outflow and has to be discounted by interest and dividends.<\/p>\n\n\n\n

The famous Black Scholes option pricing model<\/a> was rewarded with a Nobel Prize and provides us with a set of elaborate formulas that allows us to calculate Call and Put option prices. It uses a log-normal distribution model because the price can't go lower than 0, and has a long tail as prices approach infinity.<\/p>\n\n\n\n

\"log<\/a>
The log-normal distribution model can't go lower than 0, and has a long tail as prices approach infinity.<\/figcaption><\/figure>\n\n\n\n

Call and Put option prices<\/a> are calculated as follows:<\/p>\n\n\n\n

\"black-scholes<\/figure>\n\n\n\n