# Don’t get left behind in your learning marketing forecasting using classic quant methods like GARCH

HI there
I have started demonstrating to my QuantLabs.net Premium Members different ways to predict the stock markets using  popular quant analysis techniques. I have already developed about 15-20 new scripts just from the Matlab Econometric toolbox alone.

This includes topics on:

1. When to use which model for certain market conditions
2. Model estimation in volatility conditions using maximum likleihood estimation and regression
3. Using Autoregressive models and Stochastic Differential Equations
This is just getting started so I would encourage anyone who would be interested in join this QuantLabs.Net Premium Membership right away to optimize the learning
Dozens of other membership benefits listed here.
Thanks Bryan

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# Difference between Black Scholes vs GARCH?

Difference between Black Scholes vs GARCH?

Question from someone via my newsletter:

I have developed and implemented a method for computing volatility using  black&scholes with (strike, maturity, call prices, interest rate and constant return on asset), i have simulated data, my question concerning data : in general strike and maturity have a vector with 8 rows, so how can i use historical data to apply them for my method? for example concerning garch model we can compute the volatility using return of historical data then we estimate the parameter of model by maximum likelihood function.
this is the formula of volatility : sigma = sqrt( (dc/dt -(r-q)(c-k*dc/dk) /  k^2/2 d^2c/dk^2)
what is the difference between volatility of black scholes and volatility of Garch Model? is it the same result?
I am no math expert nor do I ever claim to be, I use Black Scholes to calculate an instrument’s implied volatility. I have not played a whole with GARCH at this point but appears to be something very different. Even in Matlab, they are both very different so check out their help for each.
Black Schole for IV:
http://www.mathworks.com/help/finance/blsprice.html
GARCH for forecasting as explained here:
http://www.mathworks.com/help/econ/cvm.forecast.html
Hope this helps
From Dr Paul Cottrell
BSM is an option pricing model utilizing a constant volatility methodology in its purest form. Practitioners use a moving window of volatility to update the BSM. A certain method to forecast out volatility is using a varying array of GARCH types. My books on forecasting and risk management cover this topic. In short, GARCH types are to forecast out volatility based on previous volatility. When solving for volatility for BSM, one gets the implied volatility to match to the current option price. IF you do not know the current option price one would have to use the “current” volatility or some average thereof.
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# Demos of GARCH of market foreacting with volatility variance and more with Matlab 2013b

Demos of GARCH of market foreacting with volatility variance and more with Matlab 2013b

All source code in the M script is available to my Elite Quants

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# Demo of Garch Prediction of Forex Data with older Matlab functions

Demo of Garch Prediction of Forex Data with older Matlab functions

Tutorial:

http://www.mathworks.com/help/econ/garchfit.html

http://www.mathworks.com/help/econ/garchpred.html

Asymptotic meaning:

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# Garch with volatility clustering and Matlab new Garch example

Garch with volatility clustering and Matlab new Garch example

GARCH definition:

http://www.investopedia.com/terms/g/garch.asp

Source script here:

http://www.mathworks.com/help/econ/conduct-a-likelihood-ratio-test.html

Detail on each function or class:

http://www.mathworks.com/help/econ/garch-class.html?searchHighlight=garch

http://www.mathworks.com/help/econ/garch.estimate.html?searchHighlight=estimate

http://www.mathworks.com/help/econ/lratiotest.html

http://www.mathworks.com/help/econ/price2ret.html

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# Combo of volatility clustering,GARCH,fat tail analysis be EXTREME highest potential if profit in quant algo automated trading?

Combo of volatility clustering,GARCH,fat tail analysis be EXTREME highest potential if profit in quant algo automated trading?

Let me know your thought by responding to this forum posting:

Or just commenting below. If you got ideas, let me know privately even because I think this is something not ever talked about or publicly commented on.  As said, this may be worth pursuing.

You guys and gals are the shining light that can help me drill down to the best of best in terms of trading out there!! BIG THANKS for that!!

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# Can Matlab Coder to C++ work with GARCH or Black Scholes application with Econometrics toolbox?

`Can Matlab Coder to C++ work with GARCH or Black Scholes application with Econometrics toolbox?`
```Question:
Do you know if we can compile an application using functions in the econometric toolbox? For example a GARCH or Black Scholes application?

```Are you referring to Matlab Coder? If so, only a subset of Matlab functions are supported but typically
not including GARCH or Black Scholes. If you are deploying with Builder NE, all Matlab functions are
supposedly supported.

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# Survey says! Next chosen quant trading strategy model forecasting types to be focused on will be Pairs Trading and GARCH

Survey says! Next chosen  quant trading strategy model forecasting types to be focused on will be Pairs Trading and GARCH

We have played with a moving average indicator so now we focus on Pair Trading and GARCH!

My prediction will be that the pairs trading will most likely use a popular webinar technique of Mathwork’s MATLAB.

As for GARCH, there is a wide variety ways of doing it so I may visit Matlab’s techniques as explained in my Matlab courses

But I most likely integrate the R ones as they are more advanced. Oddly enough huh?

All the code will be implemented in my custom trading platform but the source code will be available to Elite members.

Check what I got on Youtube

Did I mention you do get all SOURCE CODE in all of the above examples???

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# Which are my fave GARCH R packages for financial forecasting and future a trading models for lucractive strategy

Which RDMS or NOSQL database do you use for R? MySQL, Cassandra,  HBase, MongoDB, Oracle, PostgreSQL, CouchDB, SQLite?

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This R survey is kind of important. It will show a few things:

1. Which R most users use regardless if they are commercial vs open source vs NOSQL .
2. This will help us figure out which database is best for R using the scalability and speed depending on the requirements. This includes multiple writes for market tick data from C++ or a Java application and access by various R algorithms for analytics purposes.

Go here for the poll.

Here are some reasonable options with reasons:

MYSQL

I would assume this to be the number one choice since it is open source (or at least they say). It also contains sharding and other scalability needs with clustering. Is this something that people are using for their trading platform requirements? This includes using MYSQL as a tick data repository.

PostgreSQL

Is anyone actually using this open source database for their R needs?

Oracle

This is easily the most popular commercial RDMS for both Linux/Unix and Windows. As Oracle has open R into with a connector into their ecosystem, I wondered if people are actually using this.

SQL Server/DB2/Sybase

I am unsure if there are any R package connectors to any of these databases. I was just curious as I am really not interested in these as a real option.

Cassandra

There seems to be no R package support for this. I once posted something on R-Bloggers.com and it lit up the site, it made me wonder if this is actually more popular than people think. It seems to meet the needs of both quick write and read access.

Redis

Now the doRedis R package looked really hot. It even showcased how to use with a potential financial analytics system. I even saw Java sharding examples which left me excited on the capabilities of this database.

MongoDB

This seems to be strangely the most popular of all. I also found various R packages which seems to support it as well.

HBase which is part of Hadoop

Eh. No support even according to Revolutionary Analytics which their lacking install R package guides. I gave up pretty quickly on these R packages.

All others database options seem fine but the ones listed above seem the most viable for any R user as repository for scaling and clustering.

Go here for the poll.

http://quantlabs.net/surveys/2012/06/19/what-rdms-or-nosql-database-should-a-r-user-focus-on/

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# Latest batch of optimitals ways to predict the markets using GARCH, ARMA, etc

HI there

I have never posted as many videos in one day as I have done yesterday. I am covering the various topics in market prediction, forecasting, simulation, and analysis with the Matlab Econometrics toolbox.

Do realize each posting does get a treatment of a source code walkthrough video with the corresponding Matlab M script. Interested?

Remember all of this is EXCLUSIVE only to my QuantLabs.net Premium Members.

Here is a video I posted:

Youtube video on complete GARCH market forecasting overview with Matlab preview video and source code sample

Here are some topics I have posted.

How to import forex pair into Matlab workspace using Excel and IQFeed

Comparing GARCH models within Matlab Econometrics toolbox

Demo of complete GARCH workflow in estimation, forecasting, simulation, and analysis

Demo of Unit Root testing for stationary time series in Matlab

Demo of random walk in Matlab

Comparing GARCH fits in Matlab

Model construction with GARCH in Matlab

Model section using GARCH / ARMAX in Matlab

There are many more topics coming over the next few days to weeks. I am also holding live Q&A webinars for my members on these. These are the exact techniques and tools that real professional traders in enterprise institutions like banks or hedge funds are using.