All posts by Alan

The ERP apps

I am finishing up a book:  “The Equity Risk Premium: An Options-based Approach“.  I expect to release it sometime this year.

As part of that book, I estimate the term structure of the ERP for the US broad equity market, as proxied by the S&P 500 Index. That is, at trade date t we can estimate the ERP for horizons T, using the listed options market. This is abbreviated \textnormal{ERP}_{t,T}. Just as we do with interest rates, regardless of the horizon, the values are displayed as annualized and in percent.

I have done this for every market day over the 11+ year period from Jan 3, 2012 through Apr 28, 2023. This produces a lot of term structures — in fact, 2823 of them. This is way too much data to review in detail in the book, so I limit myself there to interesting periods like the COVID-19 Pandemic era (esp. Mar 2020), the “Volpocalypse” (Feb 5, 2018) and so on.

However, you can view the ERP term structure on any of the 11-year study dates by visiting the ERP app (historical)  (Once the app is “woken up”, I believe it’s pretty responsive).

Here is a partial screenshot from the app after loading: (Click to fully view). It shows the US market ERP term structure close to the Covid pandemic peak inversion in Mar 2020. With the date selector you can choose any date in an 11+ year study period.

ERP Info App

I estimate the ERP by inferring a forward-looking risk-neutral density for SPX returns from the options market. Then, this density is converted to a  real-world density by an exponential tilt with a parameter. The parameter is an estimate of the Coefficient of Relative Risk Aversion for a representative investor, often written as the Greek letter \gamma, so we have \textnormal{ERP}_{t,T}(\gamma).

Based on a historical estimator, I use  \gamma = 3 for some midpoint estimates and \gamma \in (2.5,3.5) for a ballpark uncertainty band. These values are charted at the App and also visible in a displayed data table. For example, the midpoint estimate is shown in the table with column header ERP3.0 and so on. The table has other elements that are discussed in the book.

I am working on some additional ERP apps. When they are deployed, I will update this post or make some new ones.

Update (Aug 17, 2026). The book is now released. See the landing page for this site for selling site links. A second supplementary app is available. It demonstrates how well Gaussian mixture models fit option smiles. Here is the link:  ERP App (GMM Fit Demo).

Known issues. Apps make take 30 seconds to “wake up”. If you then see a blank page with “Loading…” in the upper left corner, try refreshing your browser window. (This may be peculiar to MS Edge; the Chrome browser did not exhibit this glitch in my tests. However, refreshing the browser window is a good catch-all fix for many app issues).

The Equity Risk Premium: An Options-based Approach — new talk slides posted

Yesterday I gave a talk for the Financial engineering workshops at the Bayes Business School (London). My thanks to Laura Ballotta and Gianluca Fusai for the speaking invitation.

I presented results from my forthcoming book: “The Equity Risk Premium: An Options-based Approach”. (If you’ve seen slides from an earlier talk I gave at Stevens Institute of Technology, note that there is a lot of new material in this one).

If you’re interested, here are the slides for download:


Update: The book is now released and available at amazons worldwide. Here is the link for amazon.com (US): Amazon
For other amazons, see the list here: Welcome to Finance Press

OptionCity Calculator added

Many years ago, for an old web site, I developed a nice option value calculator that handles various standard models. It calculates option values, so-called “Greeks”, and “smile curves” for the Black-Scholes model, Merton’s jump-diffusion, and various models with stochastic volatility or stochastic volatility with jumps. You can also use it to extract implied volatilities. Here’s a screenshot:

OptionCity Calculator (Screenshot)

If you’re interested, here is the calculator available for download.
What you get is a zipped file that contains a Windows executable and a tutorial (help) file in pdf format. The software is free to use but is not maintained. After extracting the two files, put both of them in any convenient folder.

Advanced Smile Asymptotics: Geometry, Geodesics, and All That

This is the title of Chapter 12 of  “Option Valuation under Stochastic Volatility II”. Because of some interest in this chapter, I decided to offer it as a stand-alone Kindle ebook at the various Amazons around the world. The image below links to amazon.com

This was an interesting exercise for me as it was my first attempt at a Kindle book and I learned some things.

First, I had been somewhat reluctant to embrace that format as I thought that fairly complex book material (equations, graphics, etc.) just didn’t work. However, using the “Print replica” format, which I did, you just get the original pdf displayed. Viewing the results on the Kindle reader on my iPad, I think it’s quite readable. (You can ask Amazon to “Send you a free sample” to view it on your own Kindle app prior to any actual purchase). But, if anyone does experience readability problems on any device, I’d appreciate hearing about it.

Second, the process was quite fast. Within about three hours of starting, I had a book.

What’s this chapter about? It discusses the general principles for the construction of the limiting time-0 option smile in stochastic volatility models. It constructs in detail the limiting smile for the case where the volatility process is of CEV-type with an arbitrary volatility power p. Solutions are found using ideas from Riemannian geometry and related theory. It’s fascinating to see how basic Riemannian geometry of curved spaces — in particular, the geodesic distance from a point to a line — leads very naturally to the small-time behavior of option smiles.

From the cover background above, you can see that surfaces of positive, negative, and zero Gaussian curvature occur in the smile solution.

The ebook length is 76 pages and it’s also available in a traditional paperback version.

The Equity Risk Premium (forthcoming book) — slides posted

Yesterday I gave a Zoom talk for a Business school seminar at the Stevens Institute of Technology in New Jersey. My thanks go out to Dan Pirjol, Zack Feinstein, and the other folks from the Business school at Stevens, both for the invitation and attending.

I summarized results from my forthcoming book: “The Equity Risk Premium: An Options-based Approach”.
If you’re interested in a preview, here are the slides:


Update: The book is now released and available at amazons worldwide. Here is the link for amazon.com (US): Amazon
For other amazons, see the list here: Welcome to Finance Press

Proof of non-convergence of the short maturity expansion for the SABR model

The SABR model has been around for a relatively long time and it’s often studied via a small-maturity power series. In fact, all stochastic volatility models have a double power series expansion, for the Black-Scholes implied volatility, in the time-to-maturity T and the log-moneyness. With my coauthor Dan Pirjol, we prove that this series (taking the lognormal SABR model at the at-the-money point) is strictly asymptotic: non-convergent for any T>0. This new research preprint is posted to the arXiv here

Update: The final published version (in the journal Quantitative Finance) has limited (1st 50 clicks) free availability: here  

Update 2: Here is a zipped file containing the Mathematica code for most of the figures in the published article: FiguresPaper

For some background, the non-analyticity of the diffusion kernel factor, e^{-d^2/T} at T=0 leads many to believe strict asymptotics in T is the only possibility for option values and related solutions for diffusion models. As we explain in the paper, that’s not correct. For example, a simple case is the at-the-money Black-Scholes option value

V_{BS}(T) = {\text{erf}}(2^{-3/2} \sqrt{T}),

using the error function. After division by a \sqrt{T} , this is an entire function in the complex T-plane.

In principle, similar “nice” behavior could occur in the SABR model. That is, the value function or some simple normalized version of it, could have had a power series that converged for
|T| \lt R in the complex T-plane. Here \{ R: 0 \lt R \le \infty\} denotes a convergence radius. In fact, we prove this doesn’t happen.