Numbers are useful (I think we can all agree on that..). If you own a smart phone, you can install this runmeter app. When you run, you can take the smartphone with you and activate this app to collect interesting … Continue reading →
Blog Archives
R and Dropbox
When you woRk, you probably have a set of useful functions/packages you constantly use. For example, I often use the excellent quantmod package, and the nice multi.sapply function. You want your tools loaded when R session fires. In order to … Continue reading →
Moving Average Representation of VAR
A vector autoregression (VAR) process can be represented in a couple of ways. The usual form is as follows: The above (AR process) is what we often see and use in practice. However, I recently see more and … Continue reading →
Quantile Autoregression in R
In the past, I wrote about robust regression. This is an important tool which handles outliers in the data. Roger Koenker is a substantial contributor in this area. His website is full of useful information and code so visit when … Continue reading →
On p-value
Albert Schweitzer said: “Example is not the main thing in influencing others. It is the only thing.”, so I start with it. Generate two random samples from Normal distribution. Test the hypothesis number one: , do not reject, then test … Continue reading →
Live Correlation plot, shiny improvement.
Open CPU is a great project. Few months back, I wrote a function for plotting a moving window of the market average correlation. Jeroen C.L. Ooms was nice enough to upload it to their server. Something is now changed. Quotes … Continue reading →
A Simple Model for Realized Volatility
The post has two goals: (1) Explain how to forecast volatility using a simple Heterogeneous Auto-Regressive (HAR) model. (Corsi, 2002) (2) Check if higher moments like Skewness and Kurtosis add forecast value to this model. It will be a high … Continue reading →
On Volatility Proxy
Volatility is unobserved. Hence we need to use observed quantity as a proxy. Every once in a while I still see people using squared daily return as a proxy. However, there is ample evidence that it is a bad one. … Continue reading →
Forecasting the Misery Index, follow-up
Five months ago I generated forecasts for the Eurozone Misery index. I used the built-in “FitAR” package in R. Using different models differing in their memory length (how many lags were considered for each model) 24 months ahead forecasts were … Continue reading →
Volatility forecast evaluation in R
In portfolio management, risk management and derivative pricing, volatility plays an important role. So important in fact that you can find more volatility models than you can handle (Wikipedia link). What follows is to check how well each model performs, … Continue reading →
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
