Split mechanism for asset risk targeting

Abstract

A digital assets investments platform that allows investors to modulate their risk and volatility. The platform includes seven components; (1) a risk engine module containing quantitative derivative pricing and volatility models that are used to determine valuation of new synthetic instruments on an ongoing basis; (2) a reference asset of which the new synthetic instruments are derivatives; (3) a collateral asset that is deposited with the platform and has a value that underwrites the value of the new synthetic instruments; (4) a tokenization module into which some amount of the collateral asset is deposited; (5) a settlement period at the end of which the synthetic instruments are settled and a new period of time begins; (6) embedded derivative exposures that are embedded in the new synthetic instruments; and (7) derivatives parameters which define the embedded derivatives. Also disclosed are a related system and computer-readable non-transitory storage media embodying software.

Description

SPLIT MECHANISM FOR ASSET RISK TARGETINGRELATED APPLICATION

[0001] This application claims the benefit of priority to U.S. Provisional Patent Application Serial Number 63 / 471,517. filed on June 7, 2023, the contents of which are incorporated in this application by reference.TECHNICAL FIELD

[0002] The present disclosure relates generally to risk management and, more particularly, to a computerized platform or system for managing volatility.BACKGROUND OF THE DISCLOSURE

[0003] “Fintech” (or Financial Technology) broadly refers to the point at which financial services and technology intersect. Disruptive technologies are being developed and launched not by traditional regulated financial services companies but by innovative and nimble start-ups. The legacy business models of traditional financial services providers are under threat. Recognizing the potentially massive revenue opportunity presented by such disruptive forces, institutional investors have backed fintech start-ups with significant amounts of capital. Fig. 1 is a chart that illustrates the soaring Fintech demand. Within fintech, blockchain / digital assets are considered to represent the next evolutionary wave in technology. Fig. 2 is a chart highlighting that blockchain / digital assets are an increasingly important component of the fintech sector.

[0004] Traditionally new models of computing have emerged every 10 to 15 years. Mainframes arrived in the 1960s, PCs in the late 1970s, the internet in the 1990s, and smartphones in the 2000s. The digital assets sector is considered bytechnologists to be the next major innovative stage propelling the technology sector forward. This new wave of innovation is evolving rapidly and finding revolutionary applications across not just finance but a wide swath of other industries.

[0005] Exchanges and asset managers in the digital currencies sector have proven to be highly profitable business models. For example. Coinbase is a cryptocurrency exchange and custodian founded in 2012 that went public in the second quarter of 2021 . Its clients include 43 million retail users and 7,000 institutions. The current valuation of Coinbase is about $61 billion. Grayscale is the world’s largest digital assets manager with assets under management of over $40 billion. Founded in 2013, Grayscale manages over-the-counter products, which are essentially single asset vehicles, and its listed products have fees over 2%. Bitwise is another digital assets manager with assets under management of over $1.2 billion. Founded in 2017, Bitwise recently raised $70 million in funding.

[0006] Although the data are favorable for certain exchanges and asset managers in the digital currencies sector, the blockchain sector is still nascent and in the early stages of its evolution. Firms that are able to carve out distinctive niches within the blockchain ecosystem have significant growth potential and the wind at their back. The digital assets sector represents a substantial growth opportunity as institutional investors start deploying capital to this emerging new asset class. Fig. 3 is a chart that shows blockchain funding is at a record high.

[0007] One of the main factors precluding more mainstream adoption of cryptocurrency is its volatility. In order to realize the promise of mainstream adoption and institutions entering the sector, the volatility' of cryptocurrency must be tamed. More generally, there is a critical need for investors in the blockchain / digital assets sector to control their risk and volatility exposure. A number of existing references are directed to reducing volatility or transferring / targeting risk. Four such references are summarized below.

[0008] U.S. Patent Application Publication No. 2011 / 0295766 filed by Tompkins was granted as U.S. Patent No. 8,626,631 (now- expired for failure to pay a maintenance fee) and titled “Adaptive Closed Loop Investment Decision Engine.” The disclosed decision engine seeks to reduce volatility7but it is in the context of a portfolio of various assets and asset allocation decisions to improve overall portfolio returns and reduce volatility. The engine outputs actionable alerts regarding assetholdings and allocations to reduce investment volatility and improve returns over market and sector cycles without unnecessary trading activity. The engine performs a statistical analysis on pricing trends that generates threshold decision points for investing in or avoiding assets and for determining asset allocation weightings within a portfolio. The engine ostensibly operates in a way that yields higher returns, dramatically reduces maximum drawdown, and offers low er volatility over market cycles. It identifies conditional probabilities, when they exist, to establish decision parameters that are applied to individual investment vehicles or to portfolios of investments. If asset pricing w ere a purely random event, then no conditional probability advantage would exist to yield a statistical benefit. Historical data and empirical evidence indicate, however, that for broad market indices and many investable assets (e.g., funds and exchange traded funds) pricing variability deviates from a purely random (Gaussian) nature. Specifically, some trends have a higher probability of continuing for some period of time. Furthermore, these conditional relationships can be detected and used to establish decision parameters that can improve asset returns and lower volatility over single and multiple market corrections. Any conditional relationship that has existed in the past may not continue into the future and the disclosed engine can detect if those relationships are changing and adapt to those changes. The engine provides a w ell-developed statistical model of the market and an adaptive tool to deal objectively with market turbulence.

[0009] U.S. Patent Application Publication No. 2005 / 0131796 filed by Bridges et al. w as titled "Reduction Of Financial Instrument Volatility’" and abandoned for failure to respond to an Office Action. The application is concerned with reducing earnings volatility of a company by purchasing hedging financial instruments that have more favorable accounting attributes. The disclosed earnings volatility reduction procedure includes determining a first sensitivity value of a portfolio to underlying market conditions, trading in an immunizing instrument having a second sensitivity value substantially equal in magnitude and opposite in value of the first sensitivity value, and trading in a qualifying instrument having a third sensitivity value substantially equal to the first sensitivity value. A derivative portfolio (in particular, one that includes a financial instrument for which changes invalue are characterized as earnings pursuant to FAS 133) is structured by determining a sensitivity of the derivative portfolio with respect to financial conditions in a trading market, executing an immunizing purchase of a second trading instrument in an amount equal to the magnitude of the current sensitivity and opposite in value, and executing a qualifying sale of a third trading instrument in an amount equal to the amount of the current sensitivity.

[0010] U.S. Patent Application Publication No. 2014 / 0344131 filed by Sloan was titled “Alternative Risk Transfer Platform” and abandoned for failure to respond to an Office Action. The application focuses on alternative risk transfer (ART) through a risk crossing network of participants with the objective of transferring risk through loss financing offers. More specifically, the application discloses a platform that includes a risk database configured to store risk data, a participant database configured to store a registry' of ART participants, a ratings database configured to store ratings data regarding the risk data and the ART participants, and a risk crossing network database configured to store loss financing offers submitted by the ART participants, each of the loss financing offers being associated with one or more criteria. The ART platform can be used to match loss financing offers from different ART participants, which can then transfer risk according to the terms of the matched loss financing offers.

[0011] U.S. Patent Application Publication No. 2002 / 0055897 filed by Shidler et al. was granted as U.S. Patent No. 7,333,950 (now' expired for failure to pay a maintenance fee) and titled “System for Creating, Pricing and Managing Electronic Trading and Distribution of Credit Risk Transfer Products.” The document relates to a system of creating, pricing, and managing credit risk transfer products and the trading and distribution of such products through electronic distribution networks, financial information systems, intranet systems, and the Internet. More specifically, a computerized system creates and prices synthetic credit products on demand and distributes them to customers electronically through financial information systems and online trading websites. The system includes: (a) a Capacity Creation module for assessing the capacity' of a defined financial market to absorb defined credit products at a minimum level of default risk; (b) a Product Creation module for creatingsynthetic credit products on demand, including a Product Creation engine for creating credit products matched to qualified reference entities based upon internal templates in accordance with the determined portfolio capacity; and (c) a Pricing Creation module which tracks financial, pricing, and interest rate data available from external sources, and determines the pricing of the credit products consistent with the determined portfolio capacity'. The credit products may cover a wide range of conventionally known financial instruments, such as credit swaps, leters of credit, and credit insurance, which allow customers to trade and transfer the risks of various ty pes of credit obligations (bond, loan, or receivable), as well as new types of credit risk transfer and enhancement products enabled by the invention system. Credit product sellers use these credit risk transfer, insurance, or enhancement products to isolate, modify, or unbundled credit nsks from other risks found in obligations owed to them by third parties. These risks are transferred to credit risk buyers for a price that is based on the level of risk assumed. Credit products, particularly credit swaps, can also be used to construct a new generation of innovative structured products, such as credit-linked notes, synthetic CDOs, and principal-protected notes.

[0012] The recent worldwide market correction and increased volatility' have emphasized the need that investors have for guidance and managing volatility'. Volatility is typically managed by investment professionals using sophisticated derivatives such as options, futures, forwards, and swaps. Most individuals and institutions do not possess the knowledge required, however, to effectively use such derivative instruments to manage their exposure to volatility'. Derivatives are complex financial instruments and as a result a large percentage of the investor base feels insecure about using them. When they are used, market volatility often subjects the investors to repeated margin calls and the threat of liquidation of their assets.

[0013] A need exists for a volatility management system that is transparent, has demonstrable effectiveness, and is easily accessible and understood by investors. Investors need to be able to invest according to their risk tolerance levels. Therefore, objects of the present disclosure are to harness volatility' in a fully collateralized manner, using a novel spliting mechanism that abstracts away the complexities of the embedded derivatives.SUMMARY OF THE DISCLOSURE

[0014] To meet this and other needs, to achieve these and other objects, and in view of its purposes, the present disclosure provides a process for managing asset volatility. The process identifies a reference asset having returns that an investor is interested in splitting and the volatility profile the investor is interested in. A collateral asset is deposited in either a centralized or decentralized system. The reference asset is split into two or more portions having an aggregate value equal to the value of the collateral asset with each portion having risk-return characteristics achieved by using synthetic derivatives rather than actual derivatives on any outside exchange or other liquidity source. The step of splitting is based not on any existing standardized or legal claims on returns but on the basis of fully discretionary and newly created claims on the reference asset. The synthetic derivatives have the flexibility to achieve any risk-return target.

[0015] The assets or claims on assets created by the process summarized above also comprise part of the disclosure. Another part of the disclosure is an index (which may be a risk index) created using data series derived from the assets created by the process summarized above. A financial instrument, product, or vehicle (which may be a digital product, a public or private fund, a separate account or another type of financial instrument or vehicle) created using the process summarized above also comprises part of the disclosure.

[0016] The present disclosure also provides a digital assets investments platform that allows investors to modulate their risk and volatility. The platform includes seven components: (1) a risk engine module containing quantitative derivative pricing and volatility^ models that are used to determine valuation of new synthetic instruments on an ongoing basis; (2) a reference asset of which the new synthetic instruments are derivatives; (3) a collateral asset that is deposited with the platform and has a value that underwrites the value of the new synthetic instruments;(4) a tokenization module into which some amount of the collateral asset is deposited;(5) a settlement period at the end of which the synthetic instruments are settled and a new period of time begins; (6) embedded derivative exposures that are embedded inthe new synthetic instruments; and (7) derivatives parameters which define the embedded derivatives.

[0017] Further provided is a volatility management system that includes five stages. First, an investor decides which asset has returns that the investor is interested in splitting (this is the underlying or the reference asset) and decides the volatility profile the investor is interested in. Next, a collateral asset is deposited into the system which can be either centralized or decentralized. Third, the system uses a risk engine to split the reference asset retums / volatility and creates new synthetic derivative instruments that have deterministic payoffs and can be valued objectively. These new synthetic derivative instruments are defined to have equal and offsetting derivative positions. Fourth, the investor chooses which synthetic derivative instruments to hold and which to trade. At any time, the investor can deposit the new synthetic derivative instruments to receive back the collateral asset. Finally, at the end of the settlement period, settlement is achieved and the existing derivative instruments are rolled over into new derivative instruments for the next period.

[0018] Still further provided are a related system and at least one computer- readable non-transitory storage media embodying software. The one or more computer-readable non-transitory storage media embodying software is operable when executed, in one embodiment, to perform a series of steps using the system implementing a split mechanism for asset risk targeting. A computerized system is provided for creating, pricing, trading, managing, and distributing the assets created by the process summarized above through electronic distribution networks, financial information systems, the internet, and blockchain.

[0019] ft is to be understood that both the foregoing general description and the following detailed description are exemplary, but are not restrictive, of the disclosure.BRIEF DESCRIPTION OF THE DRAWING

[0020] The disclosure is best understood from the following detailed description when read in connection with the accompanying drawing. Included in the drawing are the following figures:

[0021] Fig. 1 is a chart that illustrates the soaring Fintech demand;

[0022] Fig. 2 is a chart highlighting that blockchain / digital assets are an increasingly important component of the fintech sector;

[0023] Fig. 3 is a chart that shows blockchain funding is at a record high;

[0024] Fig. 4 show s how the risk-targeting mechanism works according to the present disclosure;

[0025] Fig. 5 shows that combining the synthetic instruments yields back the underlying collateral;

[0026] Fig. 6 identifies five examples illustrating that the disclosed volatility management system can be used to create multiple strategies;

[0027] Fig. 7 summarizes the first of the five examples identified in Fig. 6;

[0028] Fig. 8 shows, through a simulation, that BTC RiskOFF provides a stable alternative to Bitcoin;

[0029] Fig. 9 shows, also using a simulation, that BTC RiskON significantly outperforms Bitcoin in rising markets;

[0030] Fig. 10 shows, again using a simulation, that the synthetic tokens BTC RiskON and BTC RiskOFF are designed to aggregate to the Bitcoin price;

[0031] Fig. 11 provides an analysis in three-year quarterly returns, based on the simulation, illustrating the BTC RiskOFF downside protection and the BTC RiskON upside outperformance;

[0032] Fig. 12 provides a summan' of the simulation statistics for the example in Fig. 7;

[0033] Fig. 13 shows that, for most periods, the net asset values (NAVs) float outside the strikes, illustrating the utility of the synthetic tokens;

[0034] Fig. 14 summarizes the second of the five examples identified in Fig. 6;

[0035] Fig. 15 shows, through a simulation, that the synthetic token BTC CoveredCall generates income with lower volatility;

[0036] Fig. 16 shows, also through a simulation, that the synthetic token BTC LeveredCall outperforms Bitcoin in rising markets;

[0037] Fig. 17 provides an analysis in three-year quarterly returns, based on the simulation, illustrating that the split-token mechanism summarized in Fig. 14 delivers desired results;

[0038] Fig. 18 summarizes the third of the five examples identified in Fig. 6;

[0039] Fig. 19 shows, through a simulation, that the synthetic token BTC ProtectivePut has lower downside volatility than Bitcoin;

[0040] Fig. 20 shows, also through a simulation, that the synthetic token BTC ShortPut is an income generation strategy with low beta;

[0041] Fig. 21 provides an analysis in three-year quarterly returns, based on the simulation, illustrating that the split-token mechanism summarized in Fig. 18 delivers desired results;

[0042] Fig. 22 summarizes the fourth of the five examples identified in Fig. 6;

[0043] Fig. 23 provides an analysis of returns over the fourth quarter of 2021, based on a simulation, for the example summarized in Fig. 22;

[0044] Fig. 24 provides an analysis of returns over the first quarter of 2022. based on a simulation, for the example summarized in Fig. 22;

[0045] Fig. 25 summarizes the fifth of the five examples identified in Fig. 6;

[0046] Fig. 26 provides an analysis of returns over the fourth quarter of 2021, based on a simulation, for the example summarized in Fig. 25;

[0047] Fig. 27 provides an analysis of returns over the first quarter of 2022, based on a simulation, for the example summarized in Fig. 25;

[0048] Fig. 28 illustrates the four components of an example digital assets investment platform according to the present disclosure;

[0049] Fig. 29 summarizes the characteristics of each of the four components of the investment platform illustrated in Fig. 28; and

[0050] Fig. 30 illustrates an example computer system for use in connection with the digital assets investments platform.DETAILED DESCRIPTION OF THE DISCLOSURE

[0051] In this specification and in the claims that follow, reference will be made to a number of terms which shall be defined to have the following meanings ascribed to them. The term "substantially." as used in this document, is a descriptive term that denotes approximation and means “considerable in extent7’ or “largely but not wholly that which is specified” and is intended to avoid a strict numerical boundary7to the specified parameter. Directional terms as used in this disclosure — for example up, down, right, left, front, back, top, bottom — are made only with reference to the figures as drawn and are not intended to imply absolute orientation.

[0052] The term “about” means those amounts, sizes, formulations, parameters, and other quantities and characteristics are not and need not be exact, but may be approximate and / or larger or smaller, as desired, reflecting tolerances, conversion factors, rounding off, measurement error and the like, and other factors known to those of skill in the art. When a value is described to be about or about equal to a certain number, the value is within ± 10% of the number. For example, a value that is about 10 refers to a value between 9 and 11, inclusive. When the term“about” is used in describing a value or an end-point of a range, the disclosure should be understood to include the specific value or end-point. Whether or not a numerical value or end-point of a range in the specification recites “about.” the numerical value or end-point of a range is intended to include two embodiments: one modified by “about” and one not modified by “about.” It will be further understood that the endpoints of each of the ranges are significant both in relation to the other end-point and independently of the other end-point.

[0053] The term “about” further references all terms in the range unless otherwise stated. For example, about 1, 2, or 3 is equivalent to about 1, about 2, or about 3, and further comprises from about 1-3, from about 1-2, and from about 2-3. Specific and preferred values disclosed for components and steps, and ranges thereof, are for illustration only; they do not exclude other defined values or other values within defined ranges. The components and method steps of the disclosure include those having any value or any combination of the values, specific values, more specific values, and preferred values described.

[0054] The indefinite article “a” or “an” and its corresponding definite article “the” as used in this disclosure means at least one. or one or more, unless specified otherwise. “Include.” “includes.” “including,” “have.” “has,” “having,” comprise,” “comprises,” “comprising,” or like terms mean encompassing but not limited to, that is, inclusive and not exclusive.

[0055] Disclosed is a volatility management system that uses synthetic derivatives to split any underlying asset, index, or data series into two (or more) portions (each such a portion designated a “Risk-Targeted Asset,” or “RT Asset” for short, and collectively an “RT Asset Pair”) each with a distinct and customizable volatility profile underwritten by a contractual claim on the value of a collateral asset. An asset (“Collateral Asset”) is deposited as collateral and the aggregate of the value of the new RT Asset Pair always equals the value of the Collateral Asset. The derivatives embedded in the new RT Asset Pair may be based, however, on the value of an asset, index, or data series (the “Reference Asset”) that is different from the Collateral Asset. In one aspect, the system is unique because the Reference Asset isnot split on the basis of an existing claim on the asset’s returns (such as for instance splitting a bond into principal payments and interest payments). Instead, the split is on the basis of a discretionary mechanism to achieve an investor’s goal of modulating the volatility of the returns of the Reference Asset to suit the investor’s risk tolerance. The RT Assets created through the split are a unique new form of assets. RT Assets offer different risk flavors of the underlying Reference Asset with each flavor having different risk-return characteristics. They are designed to be objectively quantifiable because the aggregate of the value of the RT Asset Pair equals the value of the Collateral Asset and any derivatives exposures of one of the RT Assets instruments is offset exactly by the derivatives exposure of the other RT Asset in the RT Asset Pair.

[0056] The disclosed volatility management system makes it possible for investors to conveniently access sophisticated risk management through RT Assets without requiring any knowledge of derivatives and without requiring any actual purchase or sale of the underlying derivatives (because the derivatives are synthetic). RT Assets abstract away the complexities of derivatives and democratize investor access to sophisticated risk management capabilities. The disclosed system includes the following seven primary components:

[0057] 1. A risk engine module containing quantitative derivative pricing and volatility models that is used to determine valuation of the new RT Assets on an ongoing basis;

[0058] 2. A Reference Asset (the new RT Assets are derivatives of the Reference Asset);

[0059] 3. A Collateral Asset that is deposited with the system and has a value that underwrites the value of the new RT Assets;

[0060] 4. A tokenization module that is responsible for the creation and the redemption of the RT Assets and into which the Collateral Asset is deposited to serve as escrow;

[0061] 5. A settlement period at the end of which the RT Assets are settled and a new period of time begins (the new RT Assets are designed to achieve a specific payoff at the end of a pre-defined period which could be days, weeks, months, or another longer / shorter period);

[0062] 6. Synthetic derivative exposures are embedded in the new RT Assets that typically (but not necessarily) are in the form of options on the Reference Asset; and

[0063] 7. Parameters which define the RT Assets and include such parameters as the strike, maturity, volatility, underlying asset, units of underlying asset etc.

[0064] The RT Assets are created by depositing the Collateral Asset with the tokenization module. The tokenization module keeps the Collateral Asset in escrow and issues new instruments, the RT Assets, that would typically (but not necessarily) have a 1: 1 ratio with the Collateral Asset (for instance 1 unit of Collateral Asset would create 1 unit each of an RT Asset in an RT Asset Pair). This module also operates in reverse. Users can deposit 1 unit each of an RT Asset in an RT Asset Pair and receive back 1 unit of the Collateral Asset.

[0065] The disclosed volatility management system includes the following five broad stages:

[0066] 1. The investor decides which asset has returns that the investor is interested in splitting (this is the Reference Asset) and decides the volatility' profile the investor is interested in;

[0067] 2. A Collateral Asset is deposited into the system which can be either traditional finance or blockchain based, centralized or decentralized;

[0068] 3. The system uses a risk engine to split the Reference Asset retums / volatility and creates new synthetic derivative instruments that have deterministic payoffs, can be valued objectively, and are defined to have equal and offsetting derivative positions;

[0069] 4. The investor chooses which synthetic derivative instruments to hold and which to trade; and

[0070] 5. At the end of the period, settlement happens and the existing derivative instruments are rolled over into new derivative instruments for the next period.

[0071] In volatility management system disclosed above, the investor can at any time deposit the new synthetic derivative instruments to receive back the Collateral Asset.

[0072] Also disclosed is an innovative digital assets investment platform that allows crypto investors to modulate their risk / volatility through a unique “risktargeting"’ mechanism. The mechanism allows investors to own cryptocurrency without the extreme volatility and own the risk they are comfortable with and trade out of the residual. Thus, the disclosed volatility management system seeks to address one of the primary factors that currently stands in the w ay of more mainstream adoption of digital assets: their volatility. The system does so byemploying a “risk-targeting’" engine that dampens volatility by synthetically creating desired risk / volatility exposures. Using the risk engine, investors can (1) strip each digital currency into tw o components, namely a return series that has the desired risk / retum characteristics and the residual; (2) hold the risk exposure with which they are comfortable; and (3) trade out of the risk exposure that they do not want.

[0073] With reference to the figures, an example is presented to illustrate the volatility management process. Fig. 4 shows how7the risk-targeting mechanism works. Fig. 5 show s that combining the synthetic instruments yields back the underlying collateral. Fig. 6 identifies five examples illustrating that the disclosed volatility management system can be used to create multiple strategies.

[0074] The first of the five examples identified in Fig. 6 is summarized in Fig. 7. As illustrated in Fig. 7, an investor uses collateral of one Bitcoin (BTC) to create two new crypto tokens: BTC RiskON and BTC RiskOFF. BTC RiskOFF represents a synthetic derivative equivalent to 0.5 BTC - Short Call (on 0.5 BTC) at strike equal toabout 125% of Spot + Long Put (on 0.5 BTC) with strike equal to about 85% of current spot. BTC RiskOFF is designed to reduce volatility by floating within a band of about 85% - 125% of spot BTC. BTC RiskOn represents a synthetic derivative equivalent to 0.5 BTC + Long Call (on 0.5 BTC) at strike equal to about 125% of Spot - Short Put (on 0.5 BTC) with strike equal to about 85% of current spot.RiskON is designed to get a levered return on BTC. The call and put positions of RiskOn and RiskOFF exactly offset each other such that if an investor holds RiskON and RiskOFF, the investor is essentially holding 0.5 BTC + 0.5 BTC = 1 BTC. Returns of BTC RiskON + BTC RiskOFF = Returns of the Collateral Asset which in this example = 1 BTC. Using the disclosed process, BTC’s returns have been split into a low er volatility instrument (RiskOFF) and a higher volatility version of BTC (RiskON). Now an investor who wants exposure to bitcoin but is w ary of the volatility can choose to invest solely in BTC RiskOFF. The options are designed such that RiskON and RiskOFF are always fully collateralized - there are no margin calls. RiskON and RiskOFF are designed to allow objective valuation of these synthetic derivative instruments and have deterministic payoffs.

[0075] Turning to evaluations of the example summarized in Fig. 7, Fig. 8 shows, through a simulation, that BTC RiskOFF provides a stable alternative to BTC. Fig. 9 shows, also using a simulation, that BTC RiskON significantly outperforms BTC in rising markets. Fig. 10 shows, again using a simulation, that the synthetic tokens (BTC RiskON and BTC RiskOFF) are designed to aggregate to the BTC price. Fig. 11 provides an analysis in three-year quarterly returns, based on the simulation, illustrating the BTC RiskOFF downside protection and the BTC RiskON upside outperformance. Fig. 12 provides a summary of the simulation statistics for the example in Fig. 7. Finally, Fig. 13 shows that, for most periods, the net asset values (NAVs) float outside the strikes, illustrating the utility of the tokens.

[0076] The same splitting mechanism can be used to create different risk solutions other than RiskON and RiskOFF. As identified in Fig. 6, additional risk solutions can be based on the same splitting mechanism. These other solutions allow7users to get exposure to volatility7, obtain leverage, earn yield, etc. Therefore, thedisclosed volatility' management system can be used to offer new forms and types of investment strategies to investors.

[0077] The second of the five examples identified in Fig. 6 is summarized in Fig. 14. As illustrated in Fig. 14, an investor uses collateral of one Bitcoin to create two new cry pto tokens: BTC CoveredCall and BTC LeveredCall. Turning to evaluations of the example summarized in Fig. 14, Fig. 15 shows, through a simulation, that the synthetic token BTC CoveredCall generates income with lower volatility. Fig. 16 shows, also through a simulation, that the synthetic token BTC LeveredCall outperforms BTC in rising markets. Fig. 17 provides an analysis in three-year quarterly returns, based on the simulation, illustrating that the split-token mechanism delivers desired results.

[0078] The third of the five examples identified in Fig. 6 is summarized in Fig. 18. As illustrated in Fig. 18, an investor uses collateral of one Bitcoin plus stablecoin to create two new crypto tokens: BTC ProtectivePut and BTC ShortPut. A stablecoin is a digital currency that is pegged to a ‘‘stable” reserve asset like the U.S. dollar or gold. Stablecoins are designed to reduce volatility' relative to unpegged cryptocurrencies like Bitcoin.

[0079] Turning to evaluations of the example summarized in Fig. 18, Fig. 19 shows, through a simulation, that the synthetic token BTC ProtectivePut has lower downside volatility than Bitcoin. Fig. 20 shows, also through a simulation, that the synthetic token BTC ShortPut is an income generation strategy with low beta. Fig. 21 provides an analysis in three-year quarterly returns, based on the simulation, illustrating that the split-token mechanism delivers desired results.

[0080] With reference to Fig. 15, 16. 19. and 20, portfolio returns are often measured using an alpha-beta framework. An equation is derived with linear regression analysis by using the portfolio’s return compared to the return of the market over the same period of time. The equation calculated from the regression analysis will be a simple line equation that best fits the data. The slope of the lineproduced from this equation is the portfolio’s beta, and the y-intercept (the part that cannot be explained by market returns) is the alpha that was generated.

[0081] Beta is the return generated from a portfolio that can be attributed to overall market returns. Exposure to beta is equivalent to exposure to systematic risk. Systematic risk is the risk that comes from investing in any security within the market. The level of systematic risk that an individual security possesses depends on how correlated it is with the overall market. This is quantitatively represented by beta exposure.

[0082] In contrast to beta, alpha is the portion of a portfolio's return that cannot be attributed to market returns and, therefore, is independent of them. Exposure to alpha is equivalent to exposure to idiosyncratic risk. Idiosyncratic risk is the risk that comes from investing in a single security (or investment class). The level of idiosyncratic risk that an individual security possesses is highly dependent on its own unique characteristics. This is quantitatively represented by alpha exposure. (Note that a single alpha position has its own idiosyncratic risk. When a portfolio contains more than one alpha position, the portfolio will then reflect each alpha position’s idiosyncratic risk collectively.)

[0083] The fourth of the five examples identified in Fig. 6 is summarized in Fig. 22. As illustrated in Fig. 22, an investor uses collateral of one stablecoin to create two new crypto tokens: BTC ShortVol and BTC LongVol. Turning to evaluations of the example summarized in Fig. 22, Figs. 23 and 24 provide analyses of returns over one quarter (13 weeks) based on a simulation. The analysis of Fig. 23 covers the fourth quarter of 2021; the analysis of Fig. 24 covers the first quarter of 2022. The analyses show that both synthetic tokens (BTC ShortVol and BTC LongVol) achieve risk-managed exposures with zero liquidation risk and no margin calls.

[0084] The fifth of the five examples identified in Fig. 6 is summarized in Fig. 25. As illustrated in Fig. 25, an investor uses collateral of one stablecoin to create two new crypto tokens: BTC Levered and BTC Inverse. Turning to evaluations of theexample summarized in Fig. 25, Figs. 26 and 27 provide analyses of returns over one quarter (13 weeks) based on a simulation. The analysis of Fig. 26 covers the fourth quarter of 2021; the analysis of Fig. 27 covers the first quarter of 2022. The analyses show that both synthetic tokens (BTC Levered and BTC Inverse) achieve cash efficient exposure to volatility with zero liquidation risk.

[0085] Fig. 28 illustrates the four components of an example digital assets investment platform. The components are risk engine, tokenization engine, risk marketplace, and research & indices. Fig. 29 summarizes the characteristics of each of the four components of the investment platform illustrated in Fig. 28. There is no comparable product either in digital currencies or in traditional capital markets. The disclosed investment platform meets a real need, allowing investors to access digital currencies conveniently and confidently without the gut-wrenching volatility and without having to employ complicated hedging strategies. The risk targeting mechanism creates products with risk / retum attributes that appeal to both risk averse investors as well as active traders. The disclosed investment platform offers a significant revenue opportunity because the risk targeting mechanism is applicable to the entire $2.5 trillion digital currency sector. Unlike the majority of the digital currency spot and derivative exchanges, the disclosed investment platform differentiates itself by embracing the highest regulatory standards (including transparency).

[0086] Methods of the disclosure may be implemented in a computer apparatus that includes a processor, database, and stored instructions to configure the processor to process data in accordance with the methods of the disclosure.

[0087] Fig. 30 illustrates an example computer system 200. In particular embodiments, one or more computer systems 200 engage with one or more components, and perform one or more steps of one or more methods, described or illustrated in this document. In particular embodiments, one or more computer systems 200 provide functionality described or illustrated in this document. In particular embodiments, software running on one or more computer systems 200 performs one or more steps of one or more methods described or illustrated in thisdocument or provides functionality described or illustrated in this document. Particular embodiments include one or more portions of one or more computer systems 200. In this document, reference to a computer system may encompass a computing device, and vice versa, where appropriate. Moreover, reference to a computer system may encompass one or more computer systems, where appropriate.

[0088] This disclosure contemplates any suitable number of computer systems 200. This disclosure contemplates the computer system 200 taking any suitable physical form. As example and not by way of limitation, the computer system 200 may be an embedded computer system, a system-on-chip (SOC), a single-board computer system (SBC) (such as, for example, a computer-on-module (COM) or system-on-module (SOM)), a desktop computer system, a laptop or notebook computer system, an interactive kiosk, a mainframe, a mesh of computer systems, a mobile telephone, a personal digital assistant (PDA), a server, a tablet computer system, or a combination of two or more of these devices. Where appropriate, the computer system 200 may include one or more computer systems 200; be unitary or distributed; span multiple locations; span multiple machines; span multiple data centers; or reside in a cloud, which may include one or more cloud components in one or more networks. Where appropriate, one or more computer systems 200 may perform without substantial spatial or temporal limitation one or more steps of one or more methods described or illustrated in this document. As an example and not by way of limitation, the one or more computer systems 200 may perform in real time or in batch mode one or more steps of one or more methods described or illustrated in this document. The one or more computer systems 200 may perform at different times or at different locations one or more steps of one or more methods described or illustrated in this document, where appropriate.

[0089] In particular embodiments, the computer system 200 includes a processor 202, memory 204. storage 206, an input / output (I / O) interface 208. a communication interface 210, and a bus 212. Although this disclosure describes and illustrates a particular computer system having a particular number of particular components in a particular arrangement, this disclosure contemplates any suitablecomputer system having any suitable number of any suitable components in any suitable arrangement.

[0090] In particular embodiments, the processor 202 includes hardware for executing instructions, such as those making up a computer program. As an example and not by way of limitation, to execute instructions, the processor 202 may retrieve (or fetch) the instructions from an internal register, an internal cache, the memory 204, or the storage 206; decode and execute them; and then write one or more results to an internal register, an internal cache, the memory 204, or the storage 206. In particular embodiments, the processor 202 may include one or more internal caches for data, instructions, or addresses. This disclosure contemplates the processor 202 including any suitable number of any suitable internal caches, where appropriate. As an example and not by way of limitation, the processor 202 may include one or more instruction caches, one or more data caches, and one or more translation lookaside buffers (TLBs). Instructions in the instruction caches may be copies of instructions in the memory' 204 or the storage 206, and the instruction caches may speed up retrieval of those instructions by the processor 202. Data in the data caches may' be copies of data in the memory' 204 or the storage 206 for instructions executing at the processor 202 to operate on; the results of previous instructions executed at the processor 202 for access by subsequent instructions executing at the processor 202 or for writing to the memory' 204 or the storage 206; or other suitable data. The data caches may speed up read or write operations by the processor 202. The TLBs may' speed up virtual- address translation for the processor 202. In particular embodiments, the processor 202 may include one or more internal registers for data, instructions, or addresses. This disclosure contemplates the processor 202 including any suitable number of any suitable internal registers, where appropriate. Where appropriate, the processor 202 may include one or more arithmetic logic units (ALUs); be a multi-core processor; or include one or more processors 202. Although this disclosure describes and illustrates a particular processor, this disclosure contemplates any suitable processor.

[0091] In particular embodiments, the memory' 204 includes main memory for storing instructions for the processor 202 to execute or data for the processor 202 to operate on. As an example and not by way of limitation, the computer system 200may load instructions from the storage 206 or another source (such as, for example, another computer system 200) to the memory 204. The processor 202 may then load the instructions from the memory 204 to an internal register or internal cache. To execute the instructions, the processor 202 may retrieve the instructions from the internal register or internal cache and decode them. During or after execution of the instructions, the processor 202 may write one or more results (which may be intermediate or final results) to the internal register or internal cache. The processor 202 may then write one or more of those results to the memory 204. In particular embodiments, the processor 202 executes only instructions in one or more internal registers or internal caches or in the memory 204 (as opposed to the storage 206 or elsewhere) and operates only on data in one or more internal registers or internal caches or in the memory 204 (as opposed to the storage 206 or elsewhere). One or more memory buses (which may each include an address bus and a data bus) may couple the processor 202 to the memory 204. The bus 212 may include one or more memory' buses, as described below. In particular embodiments, one or more memory management units (MMUs) reside between the processor 202 and the memory 204 and facilitate accesses to the memory 204 requested by the processor 202. In particular embodiments, the memory' 204 includes random access memory (RAM). This RAM may be volatile memory7, where appropriate. Where appropriate, this RAM may be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, where appropriate, this RAM may be single-ported or multi-ported RAM. This disclosure contemplates any suitable RAM. The memory' 204 may include one or more memories 204, where appropriate. Although this disclosure describes and illustrates particular memory, this disclosure contemplates any suitable memory.

[0092] In particular embodiments, the storage 206 includes mass storage for data or instructions. As an example and not by way of limitation, the storage 206 may include a hard disk drive (HDD), a floppy disk drive, flash memory, an optical disc, a magneto-optical disc, magnetic tape, or a Universal Serial Bus (USB) drive or a combination of two or more of these. The storage 206 may include removable or nonremovable (or fixed) media, where appropriate. The storage 206 may be internal or external to the computer system 200, where appropriate. In particular embodiments,the storage 206 is non-volatile, solid-state memory. In particular embodiments, the storage 206 includes read-only memory (ROM). Where appropriate, this ROM may be mask-programmed ROM. programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), electrically alterable ROM (EAROM), or flash memory or a combination of two or more of these. This disclosure contemplates the storage 206 taking any suitable physical form. The storage 206 may include one or more storage control units facilitating communication between the processor 202 and the storage 206, where appropriate. Where appropriate, the storage 206 may include one or more storages 206. Although this disclosure describes and illustrates particular storage, this disclosure contemplates any suitable storage.

[0093] In particular embodiments, the I / O interface 208 includes hardware, software, or both, providing one or more interfaces for communication between the computer system 200 and one or more I / O devices. The computer system 200 may include one or more of these I / O devices, where appropriate. One or more of these I / O devices may enable communication between a person and the computer system 200. As an example and not by way of limitation, an I / O device may include a keyboard, keypad, microphone, monitor, mouse, printer, scanner, speaker, still camera, stylus, tablet, touch screen, trackball, video camera, another suitable I / O device or a combination of two or more of these. An I / O device may include one or more sensors. This disclosure contemplates any suitable I / O devices and any suitable I / O interfaces 208 for them. Where appropriate, the I / O interface 208 may include one or more device or software drivers enabling the processor 202 to drive one or more of these I / O devices. The I / O interface 208 may include one or more I / O interfaces 208, where appropriate. Although this disclosure describes and illustrates a particular I / O interface, this disclosure contemplates any suitable I / O interface.

[0094] In particular embodiments, the communication interface 210 includes hardware, software, or both providing one or more interfaces for communication (such as, for example, packet-based communication) between the computer system 200 and one or more other computer systems 200 or one or more networks. As an example and not by way of limitation, the communication interface 210 may include anetwork interface controller (NIC) or network adapter for communicating with an Ethernet or other wire-based network or a wireless NIC (WNIC) or wireless adapter for communicating with a wireless network, such as a WI-FI network. This disclosure contemplates any suitable network and any suitable communication interface 210 for it. As an example and not by way of limitation, the computer system 200 may communicate with an ad hoc network, a personal area network (PAN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), or one or more portions of the Internet or a combination of two or more of these. One or more portions of one or more of these networks may be wired or wireless. As an example, the computer system 200 may communicate with a wireless PAN (WPAN) (such as, for example, a BLUETOOTH WPAN), a WI-FI network, a Wl-MAX network, a cellular telephone network (such as. for example, a Global System for Mobile Communications (GSM) network), or other suitable wireless network or a combination of two or more of these. The computer system 200 may include any suitable communication interface 210 for any of these networks, where appropriate. The communication interface 210 may include one or more communication interfaces 210, where appropriate. Although this disclosure describes and illustrates a particular communication interface, this disclosure contemplates any suitable communication interface.

[0095] In particular embodiments, the bus 212 includes hardware, software, or both coupling components of the computer system 200 to each other. As an example and not by way of limitation, the bus 212 may include an Accelerated Graphics Port (AGP) or other graphics bus. an Enhanced Industry Standard Architecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT) interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBAND interconnect, a low-pin-count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCIe) bus, a serial advanced technology attachment (SATA) bus, a Video Electronics Standards Association local (VLB) bus, or another suitable bus or a combination of two or more of these. The bus 212 may include one or more buses 212, where appropriate. Although this disclosuredescribes and illustrates a particular bus, this disclosure contemplates any suitable bus or interconnect.

[0096] In this document, a computer-readable non-transitory storage medium or media may include one or more semiconductor-based or other integrated circuits (ICs) (such, as for example, field-programmable gate arrays (FPGAs) or applicationspecific ICs (ASICs)), hard disk drives (HDDs), hybrid hard drives (HHDs), optical discs, optical disc drives (ODDs), magneto-optical discs, magneto-optical drives, floppy diskettes, floppy disk drives (FDDs), magnetic tapes, solid-state drives (SSDs), RAM-drives, SECURE DIGITAL cards or drives, any other suitable computer- readable non-transitory storage media, or any suitable combination of two or more of these, where appropriate. A computer-readable non-transitory storage medium may be volatile, non-volatile, or a combination of volatile and non-volatile, where appropriate.

[0097] This disclosure contemplates one or more computer-readable storage media implementing any suitable storage. In particular embodiments, a computer- readable storage medium implements one or more portions of the processor 202 (such as, for example, one or more internal registers or caches), one or more portions of the memory 204. one or more portions of the storage 206. or a combination of these, where appropriate. In particular embodiments, a computer-readable storage medium implements RAM or ROM. In particular embodiments, a computer-readable storage medium implements volatile or persistent memory . In particular embodiments, one or more computer-readable storage media embody software. In this document, reference to software may encompass one or more applications, bytecode, one or more computer programs, one or more executables, one or more instructions, logic, machine code, one or more scripts, or source code, and vice versa, where appropriate . In particular embodiments, software includes one or more application programming interfaces (APIs). This disclosure contemplates any suitable software written or otherwise expressed in any suitable programming language or combination of programming languages. In particular embodiments, software is expressed as source code or object code. In particular embodiments, software is expressed in a higher- level programming language, such as, for example, C. Perl, or a suitable extensionthereof. In particular embodiments, software is expressed in a lower-level programming language, such as assembly language (or machine code). In particular embodiments, software is expressed in JAVA. In particular embodiments, software is expressed in Hyper Text Markup Language (HTML), Extensible Markup Language (XML), JavaScript Object Notation (JSON) or other suitable markup language.

[0098] The disclosed volatility management system uses synthetic derivatives to split any asset into two (or more) components each with a distinct and customizable volatility / retums profile underwritten by a contractual claim on the Collateral Asset. The objective of the system is to manage and control volatility' of any asset by specifying desired risk targets and achieving those synthetically through a tradeable and fully collateralized financial instrument.

[0099] Among the attributes of the system are: (1) at creation, buyer and seller are the same person and, as a result, there is zero slippage and zero trading cost; (2) the system is 100% collateralized with no subsequent margin requirements; (3) the system is applicable to any' asset (financial and non-financial); (4) the system is 100% synthetic, i.e., it does not require buying or selling of any actual financial instruments;(5) the system is fully discretionary and a split can be done on any basis rather than follow pre-existing specifications as long as the derivatives exposure of one instrument is exactly offset by the opposite exposures held by the other instrument(s);(6) the system offers a unique creation and redemption process by which splitting an existing asset creates the new exposures and redeeming the new instruments results in the extinguishing of all options and reversion back to the original asset; and (7) the system results in efficient risk allocation because investors hold the instrument that is most closely aligned with their risk targets.

[0100] Further attributes of the system are: (1) the system splits an asset’s volatility / retums on a discretionary basis (creation process) to solve for an investor’s desired risk target / tolerance; (2) the creation process involves depositing collateral that underwrites the returns of the new synthetic instruments; (3) the redemption process mimics the creation process in reverse so that the synthetic instruments created initially can be redeemed for the collateral on deposit; (4) thesystem offers a packaged solution that is purely synthetic and does not require the purchase or sale of any extemal / market financial instruments; (5) the system creates new synthetic instruments with embedded optionality; (6) the options exposure of one instrument is exactly offset by the options exposure of another such that when the instruments are combined, the options cancel each other; (7) the aggregate of the values of the sy nthetic instruments always equals the value of the Collateral Asset (the synthetic instruments essentially represent a claim on the collateral); (8) the Reference Asset (which is the basis of valuation for the new synthetic instruments) and the Collateral Asset need not be the same; (9) the synthetic instruments created using the disclosed process are 100% collateralized and do not require posting of any subsequent margin; (10) the system is applicable for any asset, not just financial assets; and (1 1) the system is typically most relevant in situations where an investor is interested in the risk profile of one or the other new synthetic instrument and trades out of the other(s).

[0101] The system can be used to carve out returns streams with desired volatility characteristics by splitting virtually any asset, index, or data series. For instance, one could have a RiskON and RiskOFF version of the S&P500 or the Nasdaq Composite. Such splitting would allow investors to invest according to their risk tolerance / target without requiring them to have knowledge of or implement complicated derivative trades. In that sense, the system democratizes access to sophisticated risk targeting solutions. The same can be done with respect to investing in fine arts, carbon markets, private equity investment opportunities and hard assets, etc.

[0102] Another example could be home ownership. An individual or a couple nearing retirement might want to mitigate their capital risk by only retaining the lower volatility claim on the value of their house and selling the higher volatility portion of their ownership in the house. They might be happy to forgo any significant capital appreciation in return for the peace of mind from having their downside protected.

[0103] Commercial real estate is another example. Investors in real estate ventures could now achieve a more precise risk exposure by splitting up an asset’s returns into portions with desired volatility attributes. Another embodiment is indices created using data series derived from various types of RT Assets. A further embodiment is risk indices created using data series derived from various types of RT Assets. Still another embodiment is asset management products, trading instruments, or other financial instruments (whether in traditional finance or cryptocurrency, whether offered as decentralized or centralized products) created using an RT process, method, system, or apparatus. A further example is digital assets products, trading instruments, or other financial instruments (whether offered as decentralized or centralized products) created using an RT process, method, system, or apparatus. An embodiment is Exchange Traded Funds. Exchange Traded Notes. Exchange Traded Commodities, and other Exchange Traded Products created using an RT process, method, system, or apparatus or based on an RT Index. An embodiment is open-end and closed-end funds and separate accounts created using an RT process, method, system, or apparatus or based on an RT Index.

[0104] Disclosed is a process, method, system, or apparatus, designated as an RT process, method, system, or apparatus, for splitting any asset into two (or more) halves with each half programmed to have certain desirable risk-return characteristics. The assets are split not on the basis of any existing standardized or legal claims on returns (as in the case of interest and principal) but on the basis of fully discretionary, newly created claims on the Reference Asset. The newly created risk-return attributes are achieved by using synthetic derivatives whereby the two halves (or more portions) are counterparties to each other. No actual derivatives on any outside exchange or other liquidity source are purchased. Instead, they are synthetically created and have the flexibility to achieve any risk-return target subj ect only to the condition that both halves (or more portions) in the aggregate must equal the Collateral Asset.

[0105] Disclosed is a unique category of assets, called RT Assets, created by splitting an existing asset, index, or data series into two halves (or more portions) with each half programmed to have certain desirable risk-returncharacteristics. The assets, indices, or data series are split not on the basis of any existing standardized or legal claims on returns (as in the case of interest and principal) but on the basis of fully discretionary, newly created claims on the Reference Asset. The newly created risk-return attributes are achieved by using synthetic derivatives whereby the two halves (or more portions) are counterparties to each other. No actual derivatives on any outside exchange or other liquidity source are purchased. Instead, they are synthetically created and have the flexibility to achieve any risk-return target subject only to the condition that both halves (or more portions) in the aggregate must equal the Collateral Asset.

[0106] Disclosed is a computerized system for creating, pricing, trading, managing, and distributing RT Assets through electronic distribution networks, financial information systems, the internet, and blockchain.

[0107] Although illustrated and described above with reference to certain specific embodiments and examples, the present disclosure is nevertheless not intended to be limited to the details shown. Rather, various modifications may be made in the details within the scope and range of equivalents of the claims and without departing from the spirit of the disclosure.

Claims

What is Claimed:

1. A process for managing asset volatility, the process comprising: identifying a reference asset having returns that an investor is interested in splitting and the volatility profile the investor is interested in; depositing a collateral asset having a value; and splitting the reference asset into two or more portions having an aggregate value equal to the value of the collateral asset w ith each portion having risk-return characteristics achieved by using synthetic derivatives rather than actual derivatives on any outside exchange or other liquidity source, the step of splitting based not on any existing standardized or legal claims on returns but on the basis of fully discretionary and newly created claims on the reference asset, wherein the synthetic derivatives have the flexibility to achieve any risk-return target.

2. The assets or claims on assets created by the process of claim 1.

3. An index created using data series derived from the assets of claim 2.

4. The index of claim 3 wherein the index is a risk index.

5. A financial instrument or product created using the process of claim 1.

6. The financial instrument or product of claim 5 wherein the financial instrument or product is a digital product.

7. The financial instrument or product of claim 5 wherein the financial instrument or product is a fund or other investment vehicle.

8. A computerized system for creating, pricing, trading, managing, and distributing the assets of claim 2 through electronic distribution networks, financial information systems, the internet, and blockchain.

9. A digital assets investments platform that allows investors to modulate their risk and volatility, the platform comprising: a risk engine module containing quantitative derivative pricing and volatility models that are used to determine valuation of new synthetic instruments on an ongoing basis; a reference asset of which the new synthetic instruments are derivatives; a collateral asset that is deposited with the platform and has a value that underwrites the value of the new synthetic instruments; a tokenization module into which some amount of the collateral asset is deposited; a settlement period at the end of which the synthetic instruments are settled and a new period of time begins; embedded derivative exposures that are embedded in the new synthetic instruments; and derivatives parameters which define the embedded derivatives.

10. A process for splitting an asset into two or more portions with each portion having predetermined risk-return characteristics, the process comprising the following steps: deciding which reference asset has returns that an investor is interested in splitting and the volatility profile the investor is interested in: depositing a collateral asset into the system which can be either centralized or decentralized; using a risk engine to split the reference asset returns / vol tility and create new synthetic derivative instruments that have deterministic payoffs and can be valued objectively; defining the new synthetic derivative instruments to have equal and offsetting derivative positions; selecting which synthetic derivative instruments to hold and which to trade: optionally, depositing at any time the new synthetic derivative instruments to receive back the collateral asset; andachieving setlement at the end of a setlement period by which the existing derivative instruments are rolled over into new derivative instruments for the next period .

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