How to Exploit Volatility with CFDs in 2020

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How Volatility Weighted ETFs Work

VictoryShares has 12 ETFs on the market today, with all but one using a volatility-weighting scheme that attempts to offer better diversification and risk management than competing strategies. Together, they command about $1.4 billion in assets.

The firm, still relatively new to the ETF space, last month launched its first nonvol-weighted ETF using an index co-developed with Nasdaq to capture dividend growers.

Mannik Dhillon, president of VictoryShares, walks us through the philosophy and methodologies behind these ETFs, and the firm’s commitment to pushing boundaries in the smart-beta ETF segment.

ETF.com: Almost all of VictoryShares’ ETFs are volatility-weighted, the largest being the VictoryShares US EQ Income Enhanced Volatility Wtd Index ETF (CDC), with $435 million in assets. Why this approach?

Mannik Dhillon: Volatility weighting, very specifically, is not targeting low volatility as a selection mechanism. It’s a weighting mechanism to better diversify an index—to address the concentration that occurs in cap-weighted indices where a few stocks dominate the performance and the risk profile of the index.

If you think back to index design, the first answer the industry came up with to tackle that issue was equal weighting. That was a pretty good solution in diversification from cap weighting, but it introduces some other biases. For example, you weight more to the smaller companies in the index, which could be riskier.

The idea here is that if you use the volatility of a company’s stock price over the last 180 trading days, and basically inversely weight securities based on that metric—that the least volatile weights more—the most volatile stocks will still be in the portfolio. That’s what makes it different from some of the very popular low-vol strategies.

ETF.com: Do you compare these vol-weighted ETFs to other low-vol and minimum-vol strategies in the market, like the PowerShares S&P 500 Low Volatility Portfolio (SPLV) and the iShares Edge MSCI Min Vol USA ETF (USMV)?

Dhillon: No. I consider SPLV and USMV to be in a similar bucket, trying to drive a similar outcome. The question here is, what’s the outcome you’re trying to drive? In both those cases, you’re trying to get a less volatile portfolio.

Our strategies don’t have a target or end objective of adjusting volatility at all. We’re simply saying that if you use volatility of a company as your weighting mechanism, you get better diversification across that list of securities.

Sitefinity CMS – ‘ASP.NET’ Arbitrary File Upload

EDB-ID:

15563

Author:

Net.Edit0r
webapps

Platform:

2020-11-17
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About The Exploit Database

The Exploit Database is maintained by Offensive Security, an information security training company that provides various Information Security Certifications as well as high end penetration testing services. The Exploit Database is a non-profit project that is provided as a public service by Offensive Security.

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The Exploit Database is a CVE compliant archive of public exploits and corresponding vulnerable software, developed for use by penetration testers and vulnerability researchers. Our aim is to serve the most comprehensive collection of exploits gathered through direct submissions, mailing lists, as well as other public sources, and present them in a freely-available and easy-to-navigate database. The Exploit Database is a repository for exploits and proof-of-concepts rather than advisories, making it a valuable resource for those who need actionable data right away.

The Google Hacking Database (GHDB) is a categorized index of Internet search engine queries designed to uncover interesting, and usually sensitive, information made publicly available on the Internet. In most cases, this information was never meant to be made public but due to any number of factors this information was linked in a web document that was crawled by a search engine that subsequently followed that link and indexed the sensitive information.

The process known as “Google Hacking” was popularized in 2000 by Johnny Long, a professional hacker, who began cataloging these queries in a database known as the Google Hacking Database. His initial efforts were amplified by countless hours of community member effort, documented in the book Google Hacking For Penetration Testers and popularised by a barrage of media attention and Johnny’s talks on the subject such as this early talk recorded at DEFCON 13. Johnny coined the term “Googledork” to refer to “a foolish or inept person as revealed by Google“. This was meant to draw attention to the fact that this was not a “Google problem” but rather the result of an often unintentional misconfiguration on the part of a user or a program installed by the user. Over time, the term “dork” became shorthand for a search query that located sensitive information and “dorks” were included with may web application vulnerability releases to show examples of vulnerable web sites.

After nearly a decade of hard work by the community, Johnny turned the GHDB over to Offensive Security in November 2020, and it is now maintained as an extension of the Exploit Database. Today, the GHDB includes searches for other online search engines such as Bing, and other online repositories like GitHub, producing different, yet equally valuable results.

Volatility

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Volatility refers to the amount of uncertainty, risk and fluctuations that occur on the market and, most often, to the amount of price changes over a given period on the financial markets.

It’s a statistical measure of the dispersion of returns for any given instrument.

There are two types of volatility: historical and expected.

Traders tend to follow the Volatility Index ( VIX), which can be considered to be a gauge of fear or pessimism in the market place.

During high market volatility, prices tend to be very dynamic, and change rapidly over a short time period. When markets are not volatile or ‘fixed’ market movements and trends are created much more steadily. Volatility may appear in the market after a significant reading of macroeconomic data or after unexpected events, such as a natural disaster or a significant political event.

More experienced traders tend to invest during greater volatility, trying to open and close positions in a very short time.

Volatility in action

The chart above is a great example of both low and high volatility. This usually occurs before a significant macroeconomic reading, where traders are awaiting the outcome of the reading. After the reading is published, strong or significant movement can develop rapidly – as seen on the EURUSD chart above. This particular example occurred after the publication of the US non-farm payrolls, which came in vastly different to market expectations. It’s also interesting to note that once the initial reaction from the reading subsided, the market lost its volatility and returned to range-bound trading.

The example above shows the result of a UK referendum, where the UK100 chart fell by more than 600 pips in less than an hour. We can see a significant increase in risk aversion in the short term.

Volatility Index

The Volatility Index, known traditionally as the VIX, is a weighted index of implied volatility in options contracts on the US S&P 500 index. In effect, it’s a gauge of fear or pessimism in the market of 500 large capitalisation US stocks listed in the US, and as such a barometer of general market confidence and risk appetite.

Historically, the VIX Index is inversely correlated with US stock markets. The lower the VIX has been, the more stable market confidence is and stocks have rallied. Equally, the higher the VIX has been, the more volatile the stock markets have been and suffered price falls.

The xStation chart below tracks the VIX index against the US30 (underlying Dow Jones Index) and it’s here you can see the inverse correlation between the two indices strongly.

Hacking Articles

Raj Chandel’s Blog

5 ways to Exploit LFi Vulnerability

The main aim of writing this article is to share the idea of making an attack on a web server using various techniques when the server is suffering from file inclusion vulnerability. As we all are aware of LFI vulnerability which allows the user to include a file through URL in the browser. In this article, I have used two different platform bWAPP and DVWA which contains file inclusion vulnerability and through which I have performed LFI attack in FOUR different ways.

Basic Local file inclusion

Open target IP in the browser and login inside BWAPP as a bee: bug now chooses the bug remote & local file Inclusion then click on the hack.

Here the requested web page which suffering from RFI & LFI Vulnerability gets open. Where you will find a comment to select a language from the given drop-down list, and when you click on go button the selected language file gets included in URL. To perform basic attacks manipulate

In basic LFI attack we can directly read the content of a file from its directories using (../) or simply (/), now if you will notice the given below screenshot you will find that I have access the password file when the above URL is executed in the browser.

Null byte

In some scenario, the above basic local file inclusion attack may not work due to the high-security level. From the below image you can observe now that I got to fail to read the password file when executing the same path in URL. So when we face such kind of problem then go for NULL BYTE attack.

Now turn on burp suite to capture the browser request then select the proxy tab and start intercept. Do not forget to set browser proxy while making use of burp suite

Now inside burp suite send the intercepted data into the repeater.

Inside repeater, you can do an analysis of sent request and response generated by it. From the screenshot, it will be clear that /etc/passwd is not working and I am not able to read the password file.

From the following screenshot, you can see I had forward the request by adding null character (%00) at the end of directory /etc/passwd%00 and click on go tab. Then on the right sight of the window, the password file gets open as a response.

Base64 encoded

Now there is another way to exploit LFI when the security level is high and you are unable to view the PHP file content, and then use the following PHP function.

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