Volatility history with treasury yield is range bound

# Tag Archives: Volatility

# Forex volatility spikes with recent VIX spike

Forex volatility spikes with recent VIX spike

# How to model Bitcoin volatility and Which countries grow their wealth

How to model Bitcoin volatility and Which countries grow their wealth

# 3 strategies of ABC Pattern Murrey Math and Yin Yang Volatility

MATLAB code: 3 strategies of ABC Pattern Murrey Math and Yin Yang Volatility

# Old High Frequency Tick Data R Package exists with spreads, trade direction, statistics, volatility for forex and equity

Old High Frequency Tick Data R Package exists with spreads, trade direction, statistics, volatility for forex and equity

This high frequency R package looks fantastic. It includes a lot of analysis on high frequency data where the number of observations could easily be 100K or way more. It contains so many juicy benefits including:

1. A very decent PDF sample is included. This can be quite rare as there are some real world examples including a few equity analysis and even foreign exchange trading pair.

2. A good section is described on duration which is part of high frequency data.

3. Many traders will always find spread results but again to see an R function do this is rare. On the provided equity example like Microsoft, this shows the spread between the bid and ask quotes. It is quite convenient. There is also an example provided with a foreign exchange pair as well between bid and ask quotes in multiples of ticks with a specified tick size. I never saw anything like this in Matlab.

4. There is a handy function which triggers a trading direction. Here you can analyze the dataset to know when to buy or sell based on this function. You can also specify the time lag as well.

5. There is a set of handy functions for standard statistic measurements like mean, standard deviation, etc. As well, you get associated plots like histograms as well as functions for calendar patterns.

6. There is a realized volatility function which is based on Anderson versus other volatility models like GARCH models, stochastic volatility models, or the volatility implied by options or other

Derivative prices. Also, there is another set of benefits quoted from the supplied PDF:

They prove that as the sampling frequency of returns approaches infinity,

realized volatility measures are asymptotically free of measurement error. For daily

volatility, they use 5-minute returns to construct daily realized volatilities. The 5-

minute horizon is short enough to have the underlying asymptotic work well, but long

enough to mitigate the autocorrelation distortion caused by market microstructure

frictions.

**The Challenge**

All this sounds fine but you will find that this package is pretty old from 2003. This R package looks like it has been abandoned and needs a refresh so let me know what you think of this package’s potential. I want to note the conversion using the TAQ Load seems broken so capturing and converting data is not so easy.

Get your hands on it over at:

Compatible R in S+:

http://faculty.washington.edu/ezivot/research/HFAnalysis.ssc

PDF: http://faculty.washington.edu/ezivot/research/hfanalysis.pdf

Get the data here: http://faculty.washington.edu/ezivot/ezresearch.htm

# How to measure Liquidity measure Aggregation and volatility, Inferred trade direction from market data in R

How to measure Liquidity measure Aggregation and volatility, Inferred trade direction from market data in R

From http://www.econ.kuleuven.be/public/n09022/RTAQ_vignette.pdf

This appears to work ok but this RTAQ package is impressing me each time

# R source code example on how to trade using a GARCH Volatility Forecast

R source code example on how to trade using a GARCH Volatility Forecast

I found this link:

http://www.r-bloggers.com/trading-using-garch-volatility-forecast/

I am using RStudio so I can confirm sthat the code seems to be ok but the plots fail with a message;

*> plotbt.custom.report.part1(regime.switching.garch, regime.switching, buy.hold)*

*Error in plot.window(…) : Logarithmic axis must have positive limits*

*In addition: Warning message:*

*In xy.coords(x, y, xlabel, ylabel, log) :*

* 3120 y values <= 0 omitted from logarithmic plo*

Could it be the data used? I am not sure but I only need the data anyhow. Looks good but still need to validate it as I get more comfortable with R.