Stochastic Calculus, FAQ Flashcards
Define a convex function
A function f is convex if for every x and y in the interval, f(lambdax + (1-lambda)y) <= lambdaf(x) + (1-lambda)f(y), for any lambda in [0, 1]
State Jensen’s Inequality
If x is a convex function and x is a r.v. then E[f(x)] >= f(E[x])
State two insights from Jensen’s Inequality
- Explains why non-linear instruments have inherent value
2. If a contract has convexity in a r.v., an allowance must be made in pricing
State four complications with delta hedging
- Need to estimate delta from the model, but the model may be incorrect
- Continuous rebalancing
- Transaction costs
- Underlying must move consistently with the assumptions of the model (e.g. stock GBM)
State three explanations of risk-neutral pricing
- Hedging correctly in a Black-Scholes framework eliminates all risk
- Change measure from the real world to risk neutral framework (arbitrage invariant under measures)
- Price non-vanilla options using vanilla options via a replicating portfolio
Define equivalent measures
Two measures are equivalent if they have the same sample space and possibilities (but same probabilities not required)
State four implications of Girsanov’s Theorem
- Every equivalent measure is given by a drift change
- There is only one equivalent risk-neutral measure
- Not needed for classic Black-Scholes but useful for more complicated analysis
- Important for fixed-income with different maturities
State two definitions of complete markets
- Derivative products can be artificially reconstructed from more basic instruments
- There exist the same number of linearly independent securities as there are states of the world in the future
State three reasons why markets are incomplete in reality
- Lognormal woth random volatility has too many states in the world
- Jump diffusion implies more states than underlying securities
- Some variables cannot be hedged
State two methods of pricing in incomplete markets
- Actuarial Pricing - consider long run average with the CLT
- Consistent Pricing - replocate securities and assume no arbitrage for pricing
State three ways to price with real world probabilities
- Modern portfolio theory and utility functions
- Certainty equivalent under a real random walk
- Price = Real Expected Value - Multiple of Standard Deviation
State two real world pricing concerns
- Need to be able to measure real world probabilities and parameters
- Need to measure risk aversion
List requirements needed to apply risk-neutrality
- Complete Market
- Enough traded quantities to hedge risk
- Continuous hedging
- No transaction costs
- Accurate parameters
- No jumps
State four common confusions in risk-neutral pricing
- Forward price is the expected future value (it is not)
- Forward curve is the market expected value of the spot rate (also has premium for risk aversion)
- Using risk neutral pricing it is possible to replace mu with r (only under certain assumptions)
- Delta is the probability that an option ends up ITM (that probability depends on RW not RN)