Confusion around Ho-Lee Model

[afterworkguinness]

I'm having trouble with the Ho-Lee model for short rates and differentiating between how to find the values for the free parameter λ versus using the model to predict future rates.

The Ho-Lee model for each step in a binomial tree:

λtdt+σ sqrt(dt)

I've read that to set the free parameter at each step in a recombining binomial tree, you set the rate at state 0 to the current spot rate (ie: 1 month spot rate) and find a value for lambda that when plugged into the model will result in the current spot rate for the next time step (eg: starting with 1 month spot rate at state 0 and using a 1 month time step, the correct value for lambda when plugged into the model will produce the current 2 month spot rate etc).

This confuses me. Once I've determined the value of lambda for each step in my tree, what inputs do I change to use the model with my binomial tree to predict futures rates .. ie: one month rate in one month, in two months etc?

In case my description isn't clear, here is an except from Bruce Tuckman chapter 9.

... find λ1 such that the model produces a two-month spot rate equal to that in the market. Then find λ2 such that the model produces a three-month spot rate equal to that in the market. Continue in this fashion until the tree ends.

 

[zhukky]

Your question is indeed not clear. I assume lambdas are the only unknowns, i.e. volatility is given, dt = 1/ 12, and pu = pd = 0.5.

This gives you enough to calibrate lambda1, lambda2, ... , lambdaN so that the tree produces the start node = current one period rate (i.e. 1 month rate).

Having computed lambdas, you can then construct the tree i.e. calculate rate at each node for all N time periods.