Replication and Risk-Neutral Valuation
Why the hedge cost equals a discounted expectation, and why the pricing measure makes discounted prices martingales.
Learning Objectives
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- Show that the replication cost equals the discounted risk-neutral expectation and explain why they must coincide.
- State the martingale property of the discounted stock price under the risk-neutral measure \(\mathbb{Q}\).
- Extend one-period replication to a multi-period binomial tree by backward induction.
- Distinguish the real-world measure \(\Prob\) from the pricing measure \(\mathbb{Q}\) and articulate the role of each.
- Price a multi-period claim by both backward induction and the risk-neutral expectation and confirm they agree.
Key Vocabulary
- Risk-neutral measure \(\mathbb{Q}\)
- The probability under which every traded asset’s discounted price is a martingale; the pricing measure, distinct from real-world \(\Prob\).
- Discounted price
- The price divided by the money-market account, \(\tilde S_t=S_t/R^t\); martingale-ness of \(\tilde S\) under \(\mathbb{Q}\) encodes no-arbitrage.
- Martingale
- A process with \(\E_{\mathbb{Q}}[\tilde S_{t+1}\mid\mathcal F_t]=\tilde S_t\): no predictable drift after discounting.
- Self-financing strategy
- A dynamic portfolio rebalanced with no external cash injection or withdrawal after inception.
- Backward induction
- Pricing a tree by computing terminal payoffs and rolling one-period risk-neutral values back to the root.
- Complete market
- Every claim can be replicated; then the price is unique and equals its discounted \(\mathbb{Q}\)-expectation.
Intuition & Motivation
Replication cost = discounted expectation
Recall the one-period hedge \(\Delta=(C_u-C_d)/(S_0(u-d))\) and \(B=\tfrac1R\tfrac{uC_d-dC_u}{u-d})\). Its cost simplifies:
The algebra is exact: the replication cost is the discounted risk-neutral expectation. Neither is an approximation; no-arbitrage forces their equality.
The martingale property
Check the stock: \(\E_{\mathbb{Q}}[S_1]=qS_0u+(1-q)S_0d=S_0\big(qu+(1-q)d\big)=S_0R\), so \(\E_{\mathbb{Q}}[S_1]/R=S_0\). Discounting removes exactly the risk-free growth, leaving a driftless (fair-game) process. This martingale identity is the modern statement of no-arbitrage - made precise in 14.3.
Multi-period: backward induction
With \(n\) periods the tree has nodes \(S_0u^kd^{\,j-k})\) at time \(j\). Price by rolling back: at each node the value is the one-period risk-neutral value of its two children,
starting from the terminal payoffs. The hedge \(\Delta\) is recomputed at every node from its children - a dynamic, self-financing strategy. Equivalently, the root value is the discounted expectation over all terminal paths, \(V_0=R^{-n}\E_{\mathbb{Q}}[V_n]=R^{-n}\sum_k\binom nk q^k(1-q)^{n-k}V_n^{(k)}\).
Interactive: multi-period binomial pricing
- Taking expectations under the real measure \(\Prob\) and discounting at \(R\); only \(\E_{\mathbb{Q}}\) discounted at \(R\) gives an arbitrage-free price.
- Treating the hedge as static; the replicating strategy must be rebalanced at every node to stay self-financing.
- Confusing ‘risk-neutral’ with a claim about investor psychology; it is a change of measure, not a statement that anyone is indifferent to risk.
- Forgetting to discount by \(R^n\) (not \(R\)) over \(n\) periods.
- Check any tree price two ways: backward induction and the binomial risk-neutral sum - they must be equal.
- The martingale test \(\E_{\mathbb{Q}}[\tilde S_{t+1}\mid\mathcal F_t]=\tilde S_t\) is the quickest sanity check that your \(q\) is right.
- In the continuous limit this backward induction becomes the Black–Scholes PDE and the sum becomes the BS integral (14.4).
- Keep \(\Prob\) and \(\mathbb{Q}\) mentally separate: \(\Prob\) for risk, forecasting, and P&L; \(\mathbb{Q}\) for pricing and hedging.
Knowledge Check
Practical Exercise
For the two-period tree \(S_0=100,u=1.2,d=0.9,R=1.05\) price a put with strike \(K=100\). (a) Find terminal payoffs. (b) Roll back to get the price. (c) Verify put–call parity \(C-P=S_0-K/R^2\) using the call value \(10.204\) from the worked example (recompute the call for \(K=100\) is not needed; use \(K=100\) for both).
(a) Terminal prices \(144,108,81\); put payoffs \((100-S)^+\) are \(0,\,0,\,19\).
(b) \(V_u=\tfrac1{1.05}(0.5\cdot0+0.5\cdot0)=0\) (both children in-the-money for the stock), \(V_d=\tfrac1{1.05}(0.5\cdot0+0.5\cdot19)=9.0476\). Root: \(V_0=\tfrac1{1.05}(0.5\cdot0+0.5\cdot9.0476)=4.3084\).
(c) The \(K=100\) call rolls back to \(C_0=13.6054\) (payoffs \(44,8,0\)). Parity: \(C-P=13.6054-4.3084=9.297\) and \(S_0-K/R^2=100-100/1.1025=100-90.703=9.297\). They match, confirming no-arbitrage consistency.
Lesson Summary
Retrieval Practice
Close the lesson and answer from memory before checking. This is deliberate, effortful recall - the single highest-yield study action.
A: Under \(\mathbb{Q}\), discounted prices are martingales: \(\E_{\mathbb{Q}}[\tilde S_{t+1}\mid\mathcal F_t]=\tilde S_t\) with \(\tilde S_t=S_t/R^t\); hence \(V_0=R^{-T}\E_{\mathbb{Q}}[V_T]\).
A: Backward induction with \(V=\tfrac1R(qV_{\text{up}}+(1-q)V_{\text{down}})\), equal to the discounted \(\mathbb{Q}\)-expectation. \(\Prob\) (real) and \(\mathbb{Q}\) (pricing) generally differ.
Completion Checklist
- I can explain the core ideas in my own words
- I worked the derivations/examples by hand
- I completed the interactive workbench(es)
- I passed the knowledge check