ETF Prophet

Here is the final part of the series of posts on the volatility modelling where I will briefly talk about one of the many variant of the GARCH model: the exponential GARCH (abbreviated EGARCH). I chose this variant because it improves the GARCH model and better model some market mechanics. In the GARCH post, I [...]

As promised in last post, we will look at a popular implementation of the GARCH(1,1) model: the value-at-risk. I chose this implementation because it is used quite often in academic literature and for its educational purpose. The value-at-risk, also abbreviated VaR, is a measure of the risk for a portfolio. To recap, the 1 percent [...]

As request by several readers in light of the previous series of post on using GARCH(1,1) to forecast volatility, here is a very basic introduction post on two models widely used in finance: the GARCH and EGARCH. One of the great tools of statistics used in finance is the least square model (not exclusively the [...]

With the goal to improve reader experience on the blog, I will be introducing a new performance statistics report for backtest results. In deciding what I would be using, I used reader feedback and I went on other blogs I follow to see what they were using. This way I think readers will be able [...]

In the same line of thoughts as last post, today we will look at a way to incorporate the GARCH volatility model we introduced yesterday to create a regime switching strategy. It is often discussed on the blogosphere that high volatility is good for daily MR, see previous editions of the state of short-term mean-reversion [...]

Continuing on the current series of post, I was at the point of forecasting volatility. There is several ways to just that; this very topic is the subject of a lot of research in finance. Different models to model volatility are available and they range from both ends of the complexity spectrum. I am going [...]

I was not happy with last post’s graph, it did not give a good perspective on the recent distribution of volatility. This quick post is to show the distribution of the last three years so it is easier to compare. Stay tuned this week for deeper analysis of volatility of volatility and applications to trading. [...]

As you can see, I changed the theme of the blog to answer the numerous complaints I received on the previous one. Size of the letters, font and dark color scheme and all. However, rest assured that the content and the blog itself remains the same ! Please let me know in the comment section [...]

It’s been a long time since the Quantum Financier blog saw any action but today I am back (not home yet but back on the blogosphere nonetheless!). Today’s post will focus on the distribution of volatility and of the volatility of volatility in an attempt to examine which one is more stable. The implication of [...]

The Quantum Financier blog will take a pause since I will be away for work, I am leaving Sunday and will return on August 13th and I will most likely be unable to post during this period. Upon my return you can expect the finale for some unfinished series of post. I will still be [...]

Better late than never, as promised, the R code for the SVM system discussed in a previous post. For the record this code is based on the random forest system created by Max Dama. I thought that it would make it easier for common reader to compare and evaluate. I also want to state that [...]

Here is a quick idea I had the other day. I was reading about statistical arbitrage and pair trading and as some of you may know, the notion of spread is quite central in the domain. The idea behind pair trading is somewhat simple; find assets exhibiting similar behavior, then when the spread depart from [...]

First of all, I want to apologize for the lack of posting on the blog these days. I have been insanely busy with work (still am) and had trouble squeezing full posts in. Today, I thought I would follow up on the previous post on SVM learning. In this post I did exactly what I [...]

For an introduction to autocorrelation see this previous post: First Order Autocorrelation as a Moderator of Daily MR. In the previous post, I looked at how autocorrelation indicated MR performance, in this post, I observed the autocorrelation of another moderator of daily follow-through; volatility. Intuitively, it makes sense for volatility to be highly autocorrelated. VIX [...]

A lot of the daily mean reversion strategies discussed in the blogosphere are designed for equity index ETFs or equity index mutual funds. Indexes being a group of individual stocks, if we could determine which stocks will drive the index returns or in this case, the behavior, we could adapt a strategy to profit from [...]

This post is derived from a comment I received my last post on probability density function for an adaptive RSI strategy. pinner made the following observation: “Alternatively you could regress the returns against the set of 6mo & 1yr RSI points as a means to determine the best decision. While this approach probably requires more [...]

I will take a pause of the Time Machine series for now while I work on it some more and prepare future posts. Today I will follow up on my post on return distributions and show a simplistic way to include it in an adaptive strategy. It has been discussed quite a lot on the [...]

Results on a commodities basket. WTI Crude Natural Gas Gold Silver Results As you can see, the algorithm adapts to different classes of commodities and outperforms most of the buy and hold returns. But I want to emphasis that this concept alone is not something I would trade as is (not robust enough). The results [...]