Determining Volatility through Use of Spot and Strip Costs
In the realm of financial markets, volatility is a cherished characteristic that breathes life into asset prices. It's the wild card, the unpredictable factor that keeps traders on their toes. This essay will delve into the concept of volatility, focusing on spot and strip prices, and demonstrate how to calculate volatility for each type.
When you hear the term "spot price," don't be surprised if it pertains to a commodity, stock index, currency, or interest rate. The suffix "spot" serves to set it apart from futures and strip prices. For instance, the market price of a stock like AT&T is the spot price; however, there isn't an active futures market for individual stocks. In contrast, the spot price of oil refers to the immediate cost of delivery, differing from the futures price, which is an agreed-upon price for delivery at a later date.
On the other hand, the strip price is a term particularly popular in energy markets. It represents the cost of a bundle of futures contracts with sequential delivery dates for the same underlying commodity, such as natural gas. These futures strips are actively traded as standalone products and have their own quoted price.
Now, how do we calculate volatility with both spot and strip prices? Volatility, according to finance theory, is simply the standard deviation of daily returns. Although you could also compute the monthly volatility using monthly prices, traders often refer to volatility in annualized terms, considering the approximate 252 trading days in a year.
So, why is calculating volatility on strip prices important? A large number of options traders actively bet on strip volatility because it is an essential input in option-pricing models. The underlying volatility of a futures strip, which is often unobservable, is estimated using historical data to allow for better estimation.
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Calculating volatility in futures strip prices involves several steps, primarily focusing on implied volatility methods. Here's a detailed approach:
- Implied Volatility Calculationa. Gather data: Collect the prices of futures contracts in the strip.b. Determine option prices: Obtain the prices of options on the underlying futures contracts.c. Calculate implied volatility: Utilize the Black-Scholes model or other option pricing models to derive the implied volatility from the option prices.
- Using Volatility Indicesa. Identify relevant indices: For a global benchmark, consider the CME Group Volatility Index (CVOL), or for U.S. equity market volatility, the CBOE Volatility Index (VIX).b. Apply the index: Use these benchmarks to calculate expected volatility based on the Implied Volatility Curve using improved simple variance estimation methods (for CVOL) or option prices of the S&P 500 index (for VIX).
- Historical Volatility Calculation (Optional)a. Collect historical price data: Gather historical closing prices of the futures contracts over a specified period.b. Compute the daily returns: Calculate the percentage change in price from one period to the next.c. Find the average return: Compute the average of all the daily returns over the selected period.d. Calculate the variance: Subtract the average return from each return, square the result, and find the average of those squared differences.e. Calculate the standard deviation: Take the square root of the variance. This provides the historical volatility.
In the realm of finance and investing, calculating volatility is crucial for both spot and strip prices. The volatility of a stock's spot price, such as AT&T, can indicate the level of risk associated with that investment, while volatility in strip prices, like natural gas, is vital for options traders due to its role in option-pricing models.
Financial theory defines volatility as the standard deviation of daily returns, and traders often express it in annualized terms. For calculating the volatility of futures strip prices, methods like implied volatility calculations, using volatility indices, and historical volatility calculation can be employed.
