I don't think there is an ELI5 explanation for these terms. To really understand these terms you need at least some relevant educational background, particularly you need some familiarity with continuous-time finance. In the simplest terms, an RND is the terminal (at expiry) distribution of possible prices of the underlying implied by options prices of all traded strikes at a given expiry. Or, in other words, it is the density function that allows you to recover all the prices on an options curve to a close approximation. To recover an option price from a given density function we integrate, from zero to +infinity, that function times the payoff function of the option from zero to +infinity. Try this with a log-normal density and the black-scholes vol for an option, you'll recover the option price. Here below is some R code illustrating this: Code: directLNormOptionPrice <- function(cpFlag,S,K,T=1,r=0,v=.1) { v <- v * sqrt(T) K <- K * exp(-r*T) mu <- -v^2 / 2 mn <- K/S if(cpFlag == 'P' || cpFlag == 'p') { p <- integrate(function(x){(mn-x)*dlnorm(x,mu,v)},lower=0,upper=mn,subdivisions=2000)$value * abs(S) } else { p <- integrate(function(x){(x-mn)*dlnorm(x,mu,v)},lower=mn,upper=Inf,subdivisions=2000)$value * abs(S) } return(p) } > bsPrice('C',100,110.5,T=0.5,r=0.01,q=0.0,v=.16) [1] 1.330945 > bsPrice('P',100,110.5,T=0.5,r=0.01,q=0.0,v=.16) [1] 11.27982 > directLNormOptionPrice('C',100,110.5,T=.5,r=0.01,v=.16) [1] 1.330945 > directLNormOptionPrice('P',100,110.5,T=.5,r=0.01,v=.16) [1] 11.27982 Risk Neutral Measure Q is just the probability measure that allows us to recover the option prices according to [an implication of] the first fundamental law of asset pricing (assets are priced at discounted expectation). A probability measure P assigns a probability P[A] to every posible state A. In the case of options the states A are all the possible prices that the underlying might close at on expiry. That is a very broad and not very enlightening explanation of measure Q. It also seems more than a bit circular. Wikipedia does a better job: "In mathematical finance, a risk-neutral measure (also called an equilibrium measure, or equivalent martingale measure) is a probability measure such that each share price is exactly equal to the discounted expectation of the share price under this measure. This is heavily used in the pricing of financial derivatives due to the fundamental theorem of asset pricing, which implies that in a complete market, a derivative's price is the discounted expected value of the future payoff under the unique risk-neutral measure. Such a measure exists if and only if the market is arbitrage-free." But after the first paragraph, Wiki strays into the weeds. A better layperson's explanation is at this stackexchange link: https://quant.stackexchange.com/questions/55239/explaining-the-risk-neutral-measure Once you've derived a good RND (risk-neutral density) for the SPX for a given expiry you can compare it against implied or empirical P-measure densities at the same tenor, RND's for other related instruments (inverse-leveraged ETFs especially) at the same expiry, SPX RND's for nearby expiries, or a combination of these comparisons, to identify relative value trades (sometimes these RV trades are near arbs). The method of pricing in the code above can be used to price spreads that isolate a portion of the curve. Price the spread in the candidate instrument using the SPX density curve appropriately shifted and scaled. Compare the fitted price to the actual market price of the spread in the candidate instrument. If there is a large difference you have a trade. Do the most difficult to execute first (usually not the SPX) in the appropriate COB. Bid/offer near mid, and when that side is filled, fill the other [usually] SPX side in the CBOE COB. Fitting a very good RND is difficult, but fitting an OK RND is easy. Papers on it and even blog tutorials abound. The above RV trading method really only requires mediocre RNDs. There are plenty of opportunities for retail traders who have mastered COB execution. If you haven't, on the other hand, you'll end up waiting all day for an RND discrepancy large enough to cover transaction costs. In the attached png file, green is an RND in log strike (x axis) and magenta is the implied P-measure (actual) density, assuming an Arrow/Debreau risk coefficient of 3 (a reasonable guess) and power utility. You should be able to see where the edges are in this plot.
Thanks for the detailed explanation. I'll go through this as many times as it takes to understand the concept