Recovering First-Passage Probabilities from a Random-Walk Generating Function
Summary
The discussion explains why coefficients can be identified from an equality between two power-series expressions for the same function. In the example, the expected value of a discount factor raised to the first time a symmetric random walk reaches a specified level is represented as a series. Comparing that expression with the probability-weighted sum over possible hitting times gives a candidate formula for each hitting-time probability.
The key justification is uniqueness of power-series coefficients, which does not hold for arbitrary weighted sums. If two power series agree throughout an interval, their coefficients agree; one can see this by evaluating at zero, then differentiating and evaluating at zero repeatedly. This argument depends on the expressions genuinely being convergent power series on a suitable domain, so differentiation and coefficient recovery are valid. The note presents a mathematical justification rather than a trading strategy.
Key ideas
- Equality of arbitrary weighted sums does not by itself imply equality of the individual terms.
- Power-series representations of the same function have unique coefficients within their domain of convergence.
- Coefficients can be recovered by repeatedly differentiating the function and evaluating at zero.
- The random-walk hitting-time probabilities are identified by matching coefficients in the generating function.
- The argument requires valid power-series convergence and differentiation.
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# A problem involving random walks from Shreve
# A problem involving random walks from Shreve
Problem 5.4i in Shreve examines a symmetric random walk. Let $\tau_2 $ be the first time that the random walk reaches 2.
For $\alpha\in (0, 1) $, we are given that $$E [\alpha ^ {\tau_2}] =\sum_{k = 1} ^\infty (\alpha/2) ^ {2k}\frac{(2k)!}{(k+1)!k!}$$
It's clear that
$$E [\alpha ^ {\tau_2}] =\sum_{k = 1} ^\infty (\alpha) ^ {2k} P (\tau_2 = 2k) $$
It's therefore tempting to conclude that
$$P (\tau_2 = 2k)=\frac{(2k)!}{(k+1)!k!}2^{-2k}$$
And indeed that is the answer given. But in general $\sum_i f_i g_i =\sum_i f_i h_i$ does not imply that $g_i = h_i$ and so I'm not sure how we can reach this conclusion. (Asked about specific circumstances where this conclusion is true here.) What am I missing?
## Answer by Olaf (score 1, accepted)
https://quant.stackexchange.com/a/17521
It's true that in general if $\sum_i f_i g_i = \sum_i f_i h_i$, we do not automatically have $g_i = h_i$. But this sum is special, because all $f_i$ are monomials (i.e. of the form $\alpha^n$). This makes the sum a power series (of the form $\sum_{n=0}^\infty \alpha^n C_n$), and these series have a lot of nice properties such as continuity and differentiability (with respect to $\alpha$).
Now suppose we have two series representation of the same function:
$$F(\alpha) = \sum_{n=0}^\infty\alpha^n C_n$$ $$F(\alpha) = \sum_{n=0}^\infty\alpha^n D_n$$
Does this imply $C_n = D_n$? Yes. Yes it does. Precisely because we are dealing with a power series. One way to see this is to think of the series representation as a Taylor series in $\alpha$. The Taylor series is unique, so we automatically have $C_n = D_n = \frac{1}{n!}\frac{\partial^n}{\partial\alpha^n}F\Big|_{\alpha=0}$.
Where does this equality come from? A non-formal proof goes likes this: Take the limit of $\alpha\downarrow 0$. In that case we recover $F(0) = C_0 = D_0$. That proves $C_0 = D_0$. Next, differentiate the series with respect to $\alpha$, and again take the limit of $\alpha$ to zero. This can be done because power series are differentiable. You will then recover that $C_1=D_1$. You now get the trick: to prove that $C_n = D_n$ you differentiate $n$ times and take the limit of $\alpha$ to zero. This proves the equality.Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.