Publications by Ilya Kipnis
(Don’t Get) Contangled Up In Noise
This post will be about investigating the efficacy of contango as a volatility trading signal. For those that trade volatility (like me), a term you may see that’s somewhat ubiquitous is the term “contango”. What does this term mean? Well, simple: it just means the ratio of the second month of VIX futures over the first. The idea being is t...
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Replicating Volatiltiy ETN Returns From CBOE Futures
This post will demonstrate how to replicate the volatility ETNs (XIV, VXX, ZIV, VXZ) from CBOE futures, thereby allowing any individual to create synthetic ETF returns from before their inception, free of cost. So, before I get to the actual algorithm, it depends on an update to the term structure algorithm I shared some months back. In that alg...
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Which Implied Volatility Ratio Is Best?
This post will be about comparing a volatility signal using three different variations of implied volatility indices to predict when to enter a short volatility position. In volatility trading, there are three separate implied volatility indices that have a somewhat long history for trading–the VIX (everyone knows this one), the VXV (more recen...
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Creating a Table of Monthly Returns With R and a Volatility Trading Interview
This post will cover two aspects: the first will be a function to convert daily returns into a table of monthly returns, complete with drawdowns and annual returns. The second will be an interview I had with David Lincoln (now on youtube) to talk about the events of Feb. 5, 2018, and my philosophy on volatility trading. So, to start off with, a f...
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A Different Way To Think About Drawdown — Geometric Calmar Ratio
This post will discuss the idea of the geometric Calmar ratio — a way to modify the Calmar ratio to account for compounding returns. So, one thing that recently had me sort of annoyed in terms of my interpretation of the Calmar ratio is this: essentially, the way I interpret it is that it’s a back of the envelope measure of how many years i...
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A Review of James Picerno’s Quantitative Investment Portfolio Analytics in R
This is a review of James Picerno’s Quantitative Investment Portfolio Analytics in R. Overall, it’s about as fantastic a book as you can get on portfolio optimization until you start getting into corner cases stemming from large amounts of assets. Here’s a quick summary of what the book covers: 1) How to install R. 2) How to create some rud...
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Principal Component Momentum?
This post will investigate using Principal Components as part of a momentum strategy. Recently, I ran across a post from David Varadi that I thought I’d further investigate and translate into code I can explicitly display (as David Varadi doesn’t). Of course, as David Varadi is a quantitative research director with whom I’ve done good work ...
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GARCH and a rudimentary application to Vol Trading
This post will review Kris Boudt’s datacamp course, along with introducing some concepts from it, discuss GARCH, present an application of it to volatility trading strategies, and a somewhat more general review of datacamp. So, recently, Kris Boudt, one of the highest-ranking individuals pn the open-source R/Finance totem pole (contrary to popu...
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Right Now It’s KDA…Asset Allocation.
This post will introduce KDA Asset Allocation. KDA — I.E. Kipnis Defensive Adaptive Asset Allocation is a combination of Wouter Keller’s and TrendXplorer’s Defensive Asset Allocation, along with ReSolve Asset Management’s Adaptive Asset Allocation. This is an asset allocation strategy with a profile unlike most tactical asset allocation s...
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KDA–Robustness Results
This post will display some robustness results for KDA asset allocation. Ultimately, the two canary instruments fare much better using the original filter weights in Defensive Asset Allocation than in other variants of the weights for the filter. While this isn’t as worrying (the filter most likely was created that way and paired with those in...
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