Patent Issued for Methods and systems to quantify and index correlation risk in financial markets and risk management contracts thereon (USPTO 11080789)

2021 AUG 19 (NewsRx) -- By a News Reporter-Staff News Editor at Information Technology Business Daily -- According to news reporting originating from Alexandria, Virginia, by NewsRx journalists, a patent by the inventors Guzman, Giselle Claudette (New York, NY, US), Klein, Lawrence (Gladwyne, PA, US), filed on June 18, 2020, was published online on August 3, 2021.

The assignee for this patent, patent number 11080789, is Economic Alchemy LLC (New York, New York, United States).

Reporters obtained the following quote from the background information supplied by the inventors: “Markets are incomplete, in that, it is not possible to hedge against all potential risks. Recent financial crises have highlighted the need for more effective risk management. Portfolio managers are increasingly faced with the need to better understand and efficiently manage multiple sources of risk that can affect the value of their holdings. This can be particularly challenging for holders of multiple asset classes across multiple geographies. Some customized hedging solutions are available to professional money managers, such as, for example the use of swaps. But these over-the-counter instruments are unregulated, expensive, illiquid, and carry significant counter-party risk. The over-the-counter swaps market operates in the shadows of the financial markets, with an estimated size of $650 billion. (See, e.g., http://www.huffingtonpost.com/2012/07/08/us-derivatives-reform-rules_n_1656980.html, which is hereby incorporated by reference herein in its entirety.) Indeed, the lack of transparency in the swaps market is largely blamed in the collapse of financial firms such as Lehman Brothers and insurer American International Group during the financial crisis of 2007-2009, which led to billions of dollars in government bailouts, a burden ultimately shouldered by taxpayers.

“The specter of regulation looms over the derivatives market. The 2010 Dodd Frank financial reform law is meant to increase transparency in order to mitigate systemic risk, but compliance with such regulation will be expensive, and many small traders will be likely shut out of the market. Additionally, customized and complex hedging solutions through the use of swaps and other derivatives have long been out of reach for individual investors, and costly regulation will further prohibit individual investors from being able to hedge their portfolios from serious risks that can devastate the value of their portfolios. Recent decades have brought technological advances that democratized equity trading for individual investors by making online trading accessible and affordable, but effective risk management remains out of reach.

“Risk management must be simplified and democratized in order to build and preserve wealth, both for institutions as well as for individuals. Risk metrics and risk management contracts must be accessible, affordable, and transparent. Improved risk management techniques will assist in mitigating the boom-bubble-bust cycles that have roiled financial markets in recent decades.

“One example of improvement in risk management techniques was the introduction of the Chicago Board Options Exchange Market Volatility Index, also known by its ticker symbol, “VIX”. The VIX is a popular measure of the implied volatility of S&P 500 index options. It is often referred to as the fear index or the fear gauge, because it represents one measure of the market’s expectation of stock market volatility over the subsequent 30-day period. The concept of a volatility index, and financial instruments based on such an index, was first proposed by Menachem Brenner and Dan Galai in 1986, and was published in “New Financial Instruments for Hedging Changes in Volatility,” appearing in the July/August 1989 issue of Financial Analysts Journal. (See, e.g., http://people.stern.nyu.edu/mbrenner/research/FAJ_articleon_Volatility_Der.pdf, which is hereby incorporated by reference herein in its entirety.)

“While stock index options and futures give investors the ability to hedge against market and interest rate volatility, the VIX allows investors to hedge against the risk of changes in volatility. Changes in market volatility can be brought about by macroeconomic factors such as inflation or economic policy, or by firm-specific factors such as changes in capital structure or news about performance. The ability to hedge against changes in volatility has helped to complete the market by providing insurance against a very real and potentially devastating portfolio risk.

“But markets remain significantly incomplete. Investors today are faced with a multitude of serious risks that remain uninsurable. These risks are frequently discussed by market practitioners and in the financial media, but they are discussed as broad concepts, often in nebulous terms. As of yet, there has not been a concerted effort to quantify and index many of these risks so that efficient and accessible hedging methods can be introduced.

“There are three risks in particular that are of vital importance to investors participating in modern financial markets: 1) correlation risk; 2) liquidity risk; and 3) sentiment risk. We propose systems and methods to quantify and index these risks, and risk management contracts in order to insure against these risks. These indices would serve as underlying assets for futures and options and other financial instruments that investors would use to hedge against the risk of changes in correlation, liquidity, and sentiment in financial markets.”

In addition to obtaining background information on this patent, NewsRx editors also obtained the inventors’ summary information for this patent: “Systems and methods for creating indicators to quantify and index correlation risk that is market-wide among a broad set of asset classes, a collection of assets or securities, securities in an index, or portfolio specific relative to an investor’s portfolio holdings. The present disclosure relates to risk management in financial markets, and in particular to systems and methods for quantifying and indexing correlation risk such that these indices can serve as underlying assets for futures and options and other financial instruments that investors would use to hedge against the risks.

“In accordance with some embodiments, a method for providing a risk index is provided, the method comprising: selecting a plurality of assets; retrieving price data associated with each of the plurality of assets over a time window; calculating returns over the time window; determining a plurality of correlation measures and correlation and risk indicators between the plurality of assets over the time window using the retrieved price and/or calculated returns data, wherein a plurality of weights are assigned to the plurality of assets; generating a correlation risk index for the plurality of assets based on the plurality of correlation measures and correlation risk indicators; and providing the correlation risk index.

“In accordance with some embodiments, a system for providing a risk index is provided, the system comprising a hardware processor that is configured to: select a plurality of assets; retrieve price data associated with each of the plurality of assets over a time window; calculating returns over the time window; determine a plurality of correlation measures and correlation risk indicators between the plurality of assets over the time window using the retrieved price and/or calculated returns data, wherein a plurality of weights are assigned to the plurality of assets; generate a correlation risk index for the plurality of assets based on the plurality of correlation measures and correlation risk indicators; and provide the correlation risk index.

“In accordance with some embodiments, a non-transitory computer-readable medium containing computer-executable instructions that, when executed by a processor, cause the processor to perform a method for providing a risk index, is provided. The method comprising: selecting a plurality of assets; retrieving price data associated with each of the plurality of assets over a time window; calculating returns over the time window; determining a plurality of correlation measures and correlation risk indicators between the plurality of assets over the time window using the retrieved price data and/or calculated returns data, wherein a plurality of weights are assigned to the plurality of assets; generating a correlation risk index for the plurality of assets based on the plurality of correlations measures and correlation risk indicators; and providing the correlation risk index.

“In some embodiments, the method can include: alerts, displays, graphs, GUIs, comparisons, trends over time, whether the correlation risk index is increasing or decreasing, a scrolling ticker of correlation risk index levels and changes (numerical and percent change), price impact of correlation risk on securities, predictive power of correlation risk for returns, etc.”

The claims supplied by the inventors are:

“1. A computer-implemented method, the method comprising: accessing data including a return on each asset in a plurality of assets over a plurality of points in time in a time period, wherein the return on each asset varies over the plurality of points in time in the time period and the return on each asset in the plurality of assets has at least one unquantified correlation with a return on another asset in the plurality of assets; quantifying a plurality of correlation measures between the plurality of asset returns over the plurality of points in time in the time period; generating a correlation risk index for the plurality of assets based on the plurality of quantified correlation measures, wherein the correlation risk index has a value and corresponds to a quantification of the plurality of correlation measures; accessing updated return data for one or more of the assets in the plurality of assets; updating, on an on-going basis, the value of the correlation risk index based on repeating the quantifying and the generating using the updated return data to obtain an updated correlation risk index, and displaying, via a graphical user interface, information for the updated correlation risk index.

“2. The method of claim 1, further comprising: tracking the updated correlation risk index with a financial instrument issued based on the value of the correlation risk index.

“3. The method of claim 2, wherein the financial instrument comprises at least one of: a derivative instrument, a future, an option, an option on a future, and an Exchange Traded Fund.

“4. The method of claim 1, wherein the return data is based on at least one of: a percent change of return on at least one asset in the plurality of assets; and a log return on at least one asset in the plurality of assets; and wherein each of the correlation measures-comprises at least one of: a Spearman correlation; a Pearson correlation; a Kendall correlation; a time-varying correlation; a dynamic correlation; and a stochastic correlation.

“5. The method of claim 1, further comprising: generating an alert indicating whether the value of the updated correlation risk index increased or decreased.

“6. The method of claim 5, wherein the alert is based on the value of the updated correlation risk index varying from a defined threshold value.

“7. The method of claim 1, further comprising: testing the updated correlation risk index against actual outcomes in the variable data.

“8. The method of claim 1, wherein the displaying, via the graphical user interface, information for the updated correlation risk index comprises a display of one or more of: a value of the correlation risk index over at least the time period; a trend of the correlation risk index over at least the time period; a variation of the correlation risk index by a plurality of groups of assets in the plurality of assets; a concurrent display of the trend over at least the time period and the variation of the correlation risk index by a plurality of groups of assets in the plurality of assets; an overall determination of correlation risk in at least one financial market per unit of time; a forecast of correlation risk over a future time period for at least one asset in the plurality of assets; a forecast of correlation risk over a future time period for a plurality of assets with a hypothetical change in a composition of the plurality of assets; a trade recommendation for buying or selling assets based on a forecasted change in correlation risk; the correlation risk index of at least one asset in the plurality of assets relative to a financial risk or an economic risk; and the correlation risk index of the at least one asset in the plurality of assets relative to the correlation risk index of a different asset in the plurality of assets.

“9. The method of claim 1, wherein the generating comprises: computing a matrix from the plurality of correlation measures and wherein the matrix comprises at least one of: a standard correlation matrix; a standard covariance matrix; a matrix of pairwise correlations; and a matrix of pairwise covariances.

“10. The method of claim 9, further comprising: dynamically checking at least one of a shape and a size of the computed matrix; and repeating the generating when the at least one of the shape and the size of the matrix has changed.

“11. The method of claim 1, wherein the generating comprises determining a weight for each of the plurality of correlation measures.

“12. The method of claim 11, wherein the generating further comprises: applying the weight to each of the plurality of correlation measures; determining a weighted linear combination of the plurality of correlations measures; and generating the correlation risk index.

“13. The method of claim 11, wherein the determining the weight for each of the plurality of correlation measures comprises one or more of applying: an eigenvalue decomposition of the computed matrix; a signal extraction formula to the correlation measures; and a value to each of the correlation measures.

“14. The method of claim 1, further comprising: retrieving price data for each asset in the plurality of assets over at least some of the plurality of points in the time period; and calculating the return for each asset in the plurality of assets over at least some of the plurality of points in time in the time period based on the price data.

“15. The method of claim 14, further comprising: determining a plurality of unique asset return pairs in the plurality of assets, and wherein the quantifying the plurality of correlation measures between the plurality of asset returns comprises computing a correlation measure, a covariance measure, a cointegration measure, a co-movement measure, or a measure of dependence over a defined time window within the time period for each unique asset return pair.

“16. The method of claim 15, further comprising: expanding or rolling the defined time window by a set number of time periods to identify an expanded or rolled time window; and determining a new correlation measure for each unique asset return pair at each expanded or rolled time window.

“17. The method of claim 16, further comprising: determining an initial ramp-up time period; computing a matrix of the asset return pairs’ correlation measures for the initial ramp-up time period, wherein the computed matrix comprises one of a standard correlation matrix, a standard covariance matrix, and a matrix of pairwise correlations, and a matrix of pairwise covariances; performing a first eigenvalue decomposition on the matrix to obtain a plurality of eigenvectors, each eigenvector comprising a plurality of loadings with each loading corresponding to the correlation measure for a unique asset pairs’ returns; and determining, from the plurality of eigenvectors, at least a first set of eigenvector loadings comprising weights corresponding to the correlation measure for each unique asset pairs’ returns in the plurality of assets.

“18. The method of claim 17, further comprising: expanding or rolling the initial ramp-up time period by a set number of time periods to identify an expanded or rolled time period; computing a new matrix at each expanded or rolled time period; performing a new eigenvalue decomposition on each new matrix at each expanded or rolled time period to obtain a new plurality of eigenvectors, each eigenvector comprising a new plurality of loadings with each loading corresponding to the correlation measure for a unique asset pairs’ returns; and determining, at each expanded or rolled time period, a new set of eigenvector loadings comprising weights corresponding to the correlation measure for each unique asset pairs’ returns in the plurality of assets.”

There are additional claims. Please visit full patent to read further.

For more information, see this patent: Guzman, Giselle Claudette. Methods and systems to quantify and index correlation risk in financial markets and risk management contracts thereon. U.S. Patent Number 11080789, filed June 18, 2020, and published online on August 3, 2021. Patent URL: http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=11080789.PN.&OS=PN/11080789RS=PN/11080789

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