Rolling Realized Volatility and FX Data Exploration
Summary
This tutorial uses minute-level foreign exchange prices to build return series and calculate rolling realized volatility. It defines realized volatility from squared returns over a chosen interval and applies a rolling standard deviation to represent recent price variability. The example uses a 30-observation window for a major and a thinly traded exotic pair, while noting that sparse trading makes the effective time span variable.
The analysis then explores return and volatility distributions, compares dispersion and outliers across the pairs, and checks the correlation between returns and realized volatility before proposing them as separate features for a support vector regression model. It reports that the exotic pair has fewer observations, broader returns, and more distant points, and gives small feature correlations for both instruments. These are exploratory observations from a limited sample, not evidence of predictive performance. The article is a preparation step for a later volatility forecasting model, and data quality, missing observations, window choice, and model validation remain relevant limitations.
Key ideas
- Realized volatility summarizes historical return variability over a selected window.
- A rolling standard deviation can track changing volatility, but its effective time span depends on data frequency and gaps.
- Distribution plots can reveal skew, heavy tails, and unusual returns that may affect modeling.
- Thinly traded FX pairs can have fewer observations and more extreme returns than liquid pairs.
- Low correlation between candidate features supports treating them separately, but does not establish forecasting value.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.