Mastering R for Quantitative Finance - Kindle edition by Edina Berlinger, Ferenc Illés, Milán Badics, Ádám Banai, Gergely Daróczi, Barbara Dömötör, Gergely Gabler, Dániel Havran, Péter Juhász, István Margitai, Balázs Márkus, Péter Medvegyev, Julia Molnár, Szűcs Balázs Árpád, Ágnes Tuza, Tamás Vadász, Kata Váradi, Ágnes Vidovics-Dancs. Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Mastering R for Quantitative Finance.
The Silk Road case was compromised again and again as Force and Bridges took every opportunity to embezzle and steal money. With so much bitcoin on their hands, the two had to coax bitcoin and payments companies to help convert their ill-gotten gains. When companies resisted, investigations were launched, subpoenas were issued, and civil forfeitures were sought in retaliation.
A critical Git security vulnerability has been announced today, affecting all versions of the official Git client and all related software that interacts with Git repositories, including GitHub for Windows and GitHub for Mac. Because this is a client-side only vulnerability, github.com and GitHub Enterprise are not directly affected.
The vulnerability concerns Git and Git-compatible clients that access Git repositories in a case-insensitive or case-normalizing filesystem. An attacker can craft a malicious Git tree that will cause Git to overwrite its own .git/config file when cloning or checking out a repository, leading to arbitrary command execution in the client machine. Git clients running on OS X (HFS+) or any version of Microsoft Windows (NTFS, FAT) are exploitable through this vulnerability. Linux clients are not affected if they run in a case-sensitive filesystem.
We strongly encourage all users of GitHub and GitHub Enterprise to update their Git clients as soon as possible, and to be particularly careful when cloning or accessing Git repositories hosted on unsafe or untrusted hosts.
Repositories hosted on github.com cannot contain any of the malicious trees that trigger the vulnerability because we now verify and block these trees on push. We have also completed an automated scan of all existing content on github.com to look for malicious content that might have been pushed to our site before this vulnerability was discovered. This work is an extension of the data-quality checks we have always performed on repositories pushed to our servers to protect our users against malformed or malicious Git data.
Updated versions of GitHub for Windows and GitHub for Mac are available for immediate download, and both contain the security fix on the Desktop application itself and on the bundled version of the Git command-line client.
In addition, the following updated versions of Git address this vulnerability:
The Git core team has announced maintenance releases for all current versions of Git (v220.127.116.11, v1.9.5, v2.0.5, v2.1.4, and v2.2.1). Git for Windows (also known as MSysGit) has released maintenance version 1.9.5. The two major Git libraries, libgit2 and JGit, have released maintenance versions with the fix. Third party software using these libraries is strongly encouraged to update.
More details on the vulnerability can be found in the official Git mailing list announcement and on the git-blame blog.
Get an in-depth insight into the mathematical and statistical tools and techniques used in quantitative and computational finance!
In this course, you'll make use of R to analyze financial data, estimate statistical models, and construct optimized portfolios. You will learn how to build probability models for assets returns, the way you should apply statistical techniques to evaluate if asset returns are normally distributed, how to use Monte Carlo simulation and bootstrapping techniques to evaluate statistical models, and the usage of optimization methods to construct efficient portfolios.
The material in this course was originally developed as a complement to Prof. Eric Zivot's Coursera lectures. Therefore, for those that are new to the subject, and for those that want a full course experience, it is recommended to make use of these Coursera resources as well. However, if you have past experiences in the subject of financial econometrics and computational finance, it is possible - but challenging!- to take the DataCamp course stand-alone.
This course is for everyone interested in finance. There are no hard requirements, but having a good mathematical basis, and an interest in financial markets is recommended.
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