interesting site

Nov
9

Do not know whether you visited it before or not, I came across this site "Run My Code" today when I searched a paper. It is interesting and useful so I have no hesitation to share with you immediately.

Basically RunMyCode is a novel cloud-based platform that enables scientists to openly share the code and data that underlie their research publications. It has many files accompanying those published papers so you can easily replicate the results, which dramatically decreases your research efforts. You can choose to download the coding files directly, or upload your data and run it via the site's cloud platform. (I tried twice but failed for unknown reasons, so I recommend you to download the file and run on your own computer.)

The site is a newly established and is expanding, at the moment it includes 64 files under the following categories

A sample search in Finance returns you the codes.

It is free to use, quite nice, isn't it?

Basically RunMyCode is a novel cloud-based platform that enables scientists to openly share the code and data that underlie their research publications. It has many files accompanying those published papers so you can easily replicate the results, which dramatically decreases your research efforts. You can choose to download the coding files directly, or upload your data and run it via the site's cloud platform. (I tried twice but failed for unknown reasons, so I recommend you to download the file and run on your own computer.)

The site is a newly established and is expanding, at the moment it includes 64 files under the following categories

A sample search in Finance returns you the codes.

It is free to use, quite nice, isn't it?

Sep
22

Statistics and Finance: An Introduction is a useful book emphasizing the applications of statistics and probability to finance, such as regression, ARMA and GARCH models, the bootstrapping, and nonparametric regression using splines. For an easier learning and application, the author also public the

Possible interesting sections:

Fig 2.11 & R Code: Comparison on normal and heavy-tailed distributions.

Fig 2.12 & R Code: Survival function of a Pareto distribution with c=0.25 and a=1.1 and of normal and exp distribution on being greater than 0.25.

Fig4.1 & R Code: Autocorrelation functions of AR(1) processes with r equal to 0.95 , 0.75,0.2 and -0.9

Fig4.2 & R Code: Simulations of 200 Observations from AR(1) processes with various parameters. The white noise process is the same for all four AR(1) Processes.

Fig4.7 & R Code: Time series plot of the 3 month Treasury bill rates, plot of first differences, and ACFs. The data set contains monthly values of the 3 month rates from Jan 1950 until Mar 1996.

Model Fit Examples R Codes: Fit GE Daily log return using AR(1),AR(6), MA(2), ARMA(2,1) and log price using ARIMA(2,1,0) Model

Fig4.9 & R Code: Time series plot of the daily GE log Prices with forecasts from an ARIMA(1,1,0) Model.

Fig5.3 & R Code: Expected frontier and tangency portfolio with different r.

Fig5.4 : Efficient frontier (solid) plotted for N=3 assets.

Tangency portfolio with the constraints R Code:

R Code Volatility smiles and polynomial regressionpage 283-284

Fig 8.15: Ratio of Log Return on a call to log return on the underlying stock. Page 291.

Fig 9.4 : Polynomial and spline estimates of forward rates of U.S. Treasury bonds.

Fig 10.5 : Actual efficient frontier for the sample (optimal) and bootstrap efficient frontier (achieved) for each of six bootstrap resamples.

Fig 10.6: Results from 400 bootstrap resamples. For each resample, the efficient portfolio with a mean return of 0.012 is estimated. In the upper subplot, the actual mean return and standard deviation of the return are plotted as a small dot. The large dot is the point on the efficient frontier with mean return of 0.012.

Model Fit:

** R Code: GARCH Model Fit, Page 373.

**R codes and examples**at http://www.stat.tamu.edu/~ljin/Finance/stat689-R.htm.Possible interesting sections:

Fig 2.11 & R Code: Comparison on normal and heavy-tailed distributions.

Fig 2.12 & R Code: Survival function of a Pareto distribution with c=0.25 and a=1.1 and of normal and exp distribution on being greater than 0.25.

Fig4.1 & R Code: Autocorrelation functions of AR(1) processes with r equal to 0.95 , 0.75,0.2 and -0.9

Fig4.2 & R Code: Simulations of 200 Observations from AR(1) processes with various parameters. The white noise process is the same for all four AR(1) Processes.

Fig4.7 & R Code: Time series plot of the 3 month Treasury bill rates, plot of first differences, and ACFs. The data set contains monthly values of the 3 month rates from Jan 1950 until Mar 1996.

Model Fit Examples R Codes: Fit GE Daily log return using AR(1),AR(6), MA(2), ARMA(2,1) and log price using ARIMA(2,1,0) Model

Fig4.9 & R Code: Time series plot of the daily GE log Prices with forecasts from an ARIMA(1,1,0) Model.

Fig5.3 & R Code: Expected frontier and tangency portfolio with different r.

Fig5.4 : Efficient frontier (solid) plotted for N=3 assets.

Tangency portfolio with the constraints R Code:

R Code Volatility smiles and polynomial regressionpage 283-284

Fig 8.15: Ratio of Log Return on a call to log return on the underlying stock. Page 291.

Fig 9.4 : Polynomial and spline estimates of forward rates of U.S. Treasury bonds.

Fig 10.5 : Actual efficient frontier for the sample (optimal) and bootstrap efficient frontier (achieved) for each of six bootstrap resamples.

Fig 10.6: Results from 400 bootstrap resamples. For each resample, the efficient portfolio with a mean return of 0.012 is estimated. In the upper subplot, the actual mean return and standard deviation of the return are plotted as a small dot. The large dot is the point on the efficient frontier with mean return of 0.012.

Model Fit:

** R Code: GARCH Model Fit, Page 373.

Sep
8

Congratulations to myself that this blog's R category has been indexed by

Interested readers please check http://www.r-bloggers.com/ for more.

**R bloggers**, which is absolutely an encouragement to write more quality articles on R, thank you Tal for your permission.**What is R-Bloggers.com?**R-Bloggers.com is a central hub (e.g: A blog aggregator) of content collected from bloggers who write about R (in English). The site will help R bloggers and users to connect and follow the “R blogosphere”.

Interested readers please check http://www.r-bloggers.com/ for more.

Jan
29

Volopta is a site I came across yesterday, it contains free C++, Matlab, and VBA code for derivatives pricing. Derivatives categories include equity options, options on bonds, swaps, swaptions, options on futures, variance swaps, collateralized debt obligations, credit default swaps, volatility models, etc.

At the moment the files uploaded are only a few, which is understandable considering it is a newly launched website, take a look if interested, http://www.volopta.com/index.html.

Have a nice weekend.

At the moment the files uploaded are only a few, which is understandable considering it is a newly launched website, take a look if interested, http://www.volopta.com/index.html.

Have a nice weekend.

Jan
21

I guess most of Matlab users know Matlab central: an open exchange for the Matlab and simulink user community, where a major section is

Specifically, financial services, Mathematical modeling and Statistics and Probability are three categories I keep eyes on.

Besides Matlab central, Matlab M-files database built by Professor Wohlmuth's group is another site I often visit, it has a smaller size but grow quickly, focusing on using Matlab for numerical calculation.

Stay tuned.

**Matlab file exchange**, including a large list of Matlab files across wide application, for example, you can choose to browse files by categorySpecifically, financial services, Mathematical modeling and Statistics and Probability are three categories I keep eyes on.

Besides Matlab central, Matlab M-files database built by Professor Wohlmuth's group is another site I often visit, it has a smaller size but grow quickly, focusing on using Matlab for numerical calculation.

Stay tuned.