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Implied volatility

Implied volatility is a mathematics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Implied volatility rather than just read about it. In short: In financial mathematics, the implied volatility (IV) of an option contract is that value of the volatility of the underlying instrument which, when input in an option pricing model (usually Black–Scholes), will return a theoretical value equal to the price of the option. A non-option financial instrument that has embedded optionality, such as an interest rate cap, can also have an implied volatility.

Key takeaways

  • Implied volatility belongs to mathematics; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Implied volatility to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Implied volatility from memory before moving on to harder problems.

Reference excerpt

In financial mathematics, the implied volatility (IV) of an option contract is that value of the volatility of the underlying instrument which, when input in an option pricing model (usually Black–Scholes), will return a theoretical value equal to the price of the option. A non-option financial instrument that has embedded optionality, such as an interest rate cap, can also have an implied volatility. Implied volatility, a forward-looking and subjective measure, differs from historical volatility because the latter is calculated from known past returns of a security. Implied volatility is also used as a predictor in models that forecast future realized volatility. To understand where implied volatility stands in terms of the underlying, implied volatility rank is used to understand its implied volatility from a one-year high and low IV.

Motivation An option pricing model, such as Black–Scholes, uses a variety of inputs to derive a theoretical value for an option. Inputs to pricing models vary depending on the type of option being priced and the pricing model used. However, in general, the value of an option depends on an estimate of the future realized price volatility, σ, of the underlying. Or, mathematically:

C = f ( σ , ⋅ ) {\displaystyle C=f(\sigma ,\cdot )\,}

where C is the theoretical value of an option, and f is a pricing model that depends on σ, along with other inputs. The function f is monotonically increasing in σ, meaning that a higher value for volatility results in a higher theoretical value of the option. Conversely, by the inverse function theorem, there can be at most one value for σ that, when applied as an input to f ( σ , ⋅ ) {\displaystyle f(\sigma ,\cdot )\,} , will result in a particular value for C. Put in other terms, assume that there is some inverse function g = f−1, such that

σ C ¯ = g ( C ¯ , ⋅ ) {\displaystyle \sigma _{\bar {C}}=g({\bar {C}},\cdot )\,}

where C ¯ {\displaystyle \scriptstyle {\bar {C}}\,} is the market price for an option. The value σ C ¯ {\displaystyle \sigma _{\bar {C}}\,} is the volatility implied by the market price C ¯ {\displaystyle \scriptstyle {\bar {C}}\,} , or the implied volatility. In general, it is not possible to give a closed form formula for implied volatility in terms of call price (for a review see ). However, in some cases (large strike, low strike, short expiry, large expiry) it is possible to give an asymptotic expansion of implied volatility in terms of call price. A different approach based on closed form approximations has been also investigated.

Example A European call option, C X Y Z {\displaystyle C_{XYZ}} , on one share of non-dividend-paying XYZ Corp with a strike price of $50 expires in 32 days. The risk-free interest rate is 5%. XYZ stock is currently trading at $51.25 and the current market price of C X Y Z {\displaystyle C_{XYZ}} is $2.00. Using a standard Black–Scholes pricing model, the volatility implied by the market price C X Y Z {\displaystyle C_{XYZ}} is 18.7%, or:

σ C ¯ = g ( C ¯ , ⋅ ) = 18.7 % {\displaystyle \sigma _{\bar {C}}=g({\bar {C}},\cdot )=18.7\%}

To verify, we apply implied volatility to the pricing model, f , and generate a theoretical value of $2.0004:

C t h e o = f ( σ C ¯ , ⋅ ) = $ 2.0004 {\displaystyle C_{theo}=f(\sigma _{\bar {C}},\cdot )=\$2.0004}

which confirms our computation of the market implied volatility.

Solving the inverse pricing model function In general, a pricing model function, f, does not have a closed-form solution for its inverse, g. Instead, a root finding technique is often used to solve the equation:

f ( σ C ¯ , ⋅ ) − C ¯ = 0 {\displaystyle f(\sigma _{\bar {C}},\cdot )-{\bar {C}}=0\,}

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Implied volatility

Start with the simplest possible case. Write down what Implied volatility claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Implied volatility before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Implied volatility ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Implied volatility

In research
Implied volatility appears in mathematics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Implied volatility in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Implied volatility is common in secondary-school and first-year university syllabi. It links to neighbouring topics Derivatives (finance), Mathematical finance, so understanding it makes those chapters shorter.
In everyday life
Look for Implied volatility outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Implied volatility in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Implied volatility means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Implied volatility out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Implied volatility in simple terms?

In financial mathematics, the implied volatility (IV) of an option contract is that value of the volatility of the underlying instrument which, when input in an option pricing model (usually Black–Scholes), will return a theoretical value equal to the price of the option. A non-option financial ins…

Why does Implied volatility matter?

Because it connects several mathematics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Implied volatility?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Implied volatility.

Tags

  • Derivatives (finance)
  • Mathematical finance

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