1994issue C021-8
Constructing hourly index futures lattices from live volatility
An index-futures book can be built as a live lattice. The Black-Scholes Model recovers the futures backbone, the binomial probability model places each hourly node, and the volatility forecast is the input redrawn as new prints arrive.
- Constructions that treat an index as a continuously dividend-paying stock, including the Black-Scholes Model family, first recover a futures price and only then value the option or the reachable price set.
- The binomial probability model lays each futures step with exponential multipliers set by volatility and the square root of the step length, then an up-move probability of (a-d)/(u-d).
- For a given time slice, annualized volatility has the largest effect on the width of the estimated range, the risk-free rate has little effect, and a longer slice widens the range only with the square root of time.
- A volatility forecast can be taken from history or back-fitted until two successive prices sit on the tree, then used to rebuild a lattice when the latest interval implies a new volatility.
A construction site for the book
This TradersWeek editorial treats an index-futures book as a construction site. The Black-Scholes Model supplies the continuous-dividend futures backbone. The binomial probability model lays each hourly node. The volatility forecast is the only input that should be redrawn as new prints arrive.
Recover a futures price first
Option-pricing constructions that treat an index as a continuously dividend-paying stock, including the Black-Scholes Model family, first recover a futures price. Only after that step do they value the option or the reachable price set. The recovered futures price is the starting point for the recomputed tree of reachable futures prices over a chosen time slice.
Place the hourly nodes
The binomial probability model for a futures step uses a equal to 1. The up factor is e raised to volatility times the square root of the time slice, with a matching down factor. Those exponential multipliers set by volatility and the square root of the step length define the next nodes. The up-move probability is (a-d)/(u-d).
What widens the estimated range
For a given time slice, annualized volatility has the largest effect on the width of the estimated range. The risk-free rate has little effect. Extending the slice widens the range only with the square root of time.
An hourly lattice from one session
A 16 September 1993 hourly lattice for December S&P 500 futures started from the prior 462.30 close. A 10 percent volatility forecast and a one-hour step of 0.000114 years produced first-hour nodes of 462.79 and 461.81, with multipliers near 1.001069 and 0.998932. Those same inputs gave an up-move probability of 0.50187 and a down-move probability of 0.49813, treated as nearly even for practical purposes.
Rebuilding the first two hours at volatility forecasts of 0.1, 0.2, 0.3 and 0.40 showed a one-hour band of 463.78 to 460.82 at 0.30. A fitted 0.284 matched the 10:30 print of 460.90.
After 11:30 printed 461.10, the back-fitted volatility forecast dropped to 0.04 and the next-hour band narrowed to 461.29 to 460.90. Later hours needed about 15 percent then 10 percent volatility to stay near the 460.35, 460.50 and 460.60 prints.
Redraw only the volatility forecast
A volatility forecast can be taken from history or back-fitted by iterating until two successive observed prices sit on the tree. That forecast is then used to rebuild a lattice when the latest interval implies a new volatility. The same construction can run on intervals from minutes to days. In this editorial reading, the latest print changes the volatility forecast, not the Black-Scholes Model backbone or the binomial probability model rules.
All readings on this track · 6 readings
- 1994Constructing hourly index futures lattices from live volatility
- 1995A short-to-long historical-volatility ratio as a regime-gate
- 2001Constructing a variable-interval average from a difference-oscillator or volatility forecast
- 2006Percent-scale average true range for comparable range
- 2007Historical compression and implied slope as a futures regime map
- 2013GARCH and a volatility rank as market-regime classifiers