# Can a Factor-Adjusted Model Be Applied to Asset Classes Other than Equities Such as Futures or Fx? ▴ Question

**Published:** 2025-08-05
**Categories:** Question

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## Concept

The extension of factor-adjusted models beyond the equities domain into [asset classes](https://prime.greeks.live/area/asset-classes/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) like futures and [foreign exchange](https://prime.greeks.live/area/foreign-exchange/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) (FX) represents a significant evolution in portfolio construction and risk management. This progression is built on the foundational idea that systematic drivers of return, or risk premia, are not exclusive to stock markets. They are observable phenomena across all liquid, traded markets. The core task is to correctly identify and specify these factors for each unique market structure.

The architecture of an equity [factor model](https://prime.greeks.live/area/factor-model/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite), which decomposes returns into exposures to systematic factors like value, momentum, size, and quality, provides a robust blueprint. Applying this blueprint to other asset classes requires a translation of these concepts into the native language of that market.

For instance, in the context of foreign exchange, the concept of ‘value’ is often proxied by purchasing power parity (PPP), which suggests that exchange rates should adjust to equalize the price of a basket of goods between two countries. Similarly, the ‘carry’ factor in FX is analogous to the dividend yield in equities; it captures the return from holding a higher-yielding currency funded by borrowing in a lower-yielding one. In [commodity futures](https://prime.greeks.live/area/commodity-futures/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite), the ‘[term structure](https://prime.greeks.live/area/term-structure/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite)’ or ‘roll yield’ factor, derived from the shape of the futures curve, serves a similar function, capturing returns from the price convergence of futures contracts to the spot price over time. The successful application of these models hinges on this careful, asset-class-specific translation of universal risk principles.

> The core principle is that systematic drivers of return exist in all liquid markets; the challenge is defining them within each market’s unique structure.

The intellectual heritage of this approach comes from the desire to create a more granular and accurate picture of portfolio risk and return. A traditional asset allocation model might simply categorize an investment as ‘commodities’ or ‘currencies’. A factor-based model provides a much deeper level of analysis. It can reveal that a seemingly diversified portfolio is, in reality, heavily concentrated in a single factor, such as momentum, across multiple asset classes.

This insight is operationally critical. It allows portfolio managers to distinguish between returns generated from genuine alpha (skill) and those that are simply compensation for bearing systematic factor risk. This clarity enables more precise risk budgeting, hedging, and the construction of portfolios with more resilient and diversified sources of return.

Ultimately, the application of factor models to futures and FX is an exercise in systems thinking. It treats different asset classes not as isolated silos, but as interconnected systems governed by a common set of economic forces. The objective is to build a unified framework that can explain and potentially forecast returns across the entire investment universe, providing a powerful lens for strategic and tactical asset allocation decisions.

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## Strategy

Deploying a [factor-adjusted model](https://prime.greeks.live/area/factor-adjusted-model/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) in non-equity asset classes is a strategic initiative to move beyond traditional diversification and toward a more robust framework of [risk premia](https://prime.greeks.live/area/risk-premia/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) harvesting. The strategy involves a systematic process of identifying, defining, and integrating asset-class-specific factors that have a strong theoretical basis and empirical validation. This approach provides a superior method for portfolio construction, risk attribution, and performance evaluation compared to conventional methods that often conflate market beta, factor exposures, and true alpha.

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## Identifying Core Factors in New Domains

The initial strategic step is the translation of established equity factor concepts into the language of futures and FX markets. This is not a simple one-to-one mapping but a re-interpretation based on the economic drivers of each asset class. Academic research provides a strong foundation for this process.

- 
**Value** ▴ In equities, this is often measured by price-to-book or price-to-earnings ratios. In FX, its counterpart is often a model based on purchasing power parity (PPP) or real effective exchange rate (REER) deviations. For commodities, value can be conceptualized as the spot price relative to a long-term equilibrium price or cost of production.

- 
**Momentum** ▴ This factor, which captures the tendency of past winners to continue winning, is highly portable across asset classes. It can be applied directly to currency pairs and individual commodity futures contracts, typically based on their trailing returns over a specific period (e.g. 3-12 months).

- 
**Carry** ▴ This factor seeks to capture the excess return from holding a higher-yielding asset. In FX, this is the interest rate differential between two countries. In commodity futures, it is the ‘[roll yield](https://prime.greeks.live/area/roll-yield/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite)’ ▴ the return generated from the difference between the futures price and the spot price. A downward-sloping futures curve (backwardation) typically generates a positive roll yield, while an upward-sloping curve (contango) results in a negative one.

- 
**Liquidity** ▴ In all markets, less liquid assets tend to offer a return premium. While its role in equities and bonds is well-documented, its application to commodity futures is a more recent area of study, showing that it is a priced factor.

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## What Is the Process for Model Construction?

Once factors are defined, the strategy shifts to model construction and integration. This involves a disciplined, quantitative process. A portfolio’s returns are regressed against the returns of these newly constructed factor portfolios.

The output of this regression analysis provides the portfolio’s ‘factor loadings’ or ‘betas’ to each [systematic risk](https://prime.greeks.live/area/systematic-risk/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) premium. This process allows a portfolio manager to understand, for instance, what portion of their returns came from a broad exposure to the [commodity momentum](https://prime.greeks.live/area/commodity-momentum/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) factor versus their specific security selection decisions.

> A factor-based strategy decomposes portfolio returns into exposures to systematic risk premia, enabling more precise risk management and alpha identification.

The strategic advantage of this approach is multifaceted. It enables the creation of ‘factor-mimicking portfolios’ ▴ portfolios designed to provide pure exposure to a single [risk premium](https://prime.greeks.live/area/risk-premium/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite), such as FX carry or commodity term structure. These can be used as building blocks for a broader asset allocation, as hedging instruments, or as benchmarks against which to measure the performance of active managers.

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## Comparing Factor Models across Asset Classes

The table below outlines a strategic comparison of how common factors are specified and interpreted across equities, FX, and commodity futures. This highlights the adaptability of the core concepts.

| Factor Concept | Equities Implementation | Foreign Exchange (FX) Implementation | Commodity Futures Implementation |
| --- | --- | --- | --- |
| Value | Low Price-to-Book or Price-to-Earnings Ratios | Purchasing Power Parity (PPP) Deviations | Spot Price vs. Long-Term Equilibrium / Cost of Production |
| Momentum | Trailing 12-Month Price Performance | Trailing 3- to 12-Month Exchange Rate Performance | Trailing 3- to 12-Month Futures Contract Performance |
| Carry / Yield | High Dividend Yield | High Short-Term Interest Rate Differentials | High Roll Yield (Backwardated Futures Curves) |
| Low Volatility | Low Historical Price Volatility | Low Historical Exchange Rate Volatility | Low Historical Futures Price Volatility |

By implementing a factor-based strategy, an institution can build a more resilient and transparent investment process. It allows for the diversification of risk sources, moving away from a simple reliance on asset class labels. This systematic approach provides a clear framework for understanding the underlying drivers of portfolio performance, leading to more informed and robust strategic decisions.

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## Execution

The execution of a factor-adjusted modeling framework for futures and FX markets is a complex undertaking that requires a robust infrastructure for data management, quantitative analysis, and system integration. This is where theoretical concepts are translated into an operational reality. A successful execution requires a disciplined, multi-stage process that encompasses everything from raw data ingestion to the final integration of model outputs into the portfolio management workflow.

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## The Operational Playbook

Implementing a factor model for non-equity asset classes follows a structured, sequential playbook. Each step is critical for the integrity and efficacy of the final model.

- 
**Data Acquisition and Cleansing** ▴ The foundation of any quantitative model is high-quality data. For FX, this includes daily spot exchange rates for a broad universe of currencies, as well as short-term interest rate data (e.g. 3-month LIBOR or its equivalent) for calculating carry. For futures, this requires continuous [futures contract](https://prime.greeks.live/area/futures-contract/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) data for a diverse set of commodities, including daily prices, volumes, and open interest. This data must be meticulously cleansed to handle contract rolls, expirations, and any potential errors in the raw feeds.

- 
**Factor Construction and Specification** ▴ This is the core quantitative task. For each defined factor (e.g. FX Carry, Commodity Momentum), a precise mathematical definition is required. This involves creating long-short portfolios based on the factor’s signal. For example, the [FX Carry factor](https://prime.greeks.live/area/fx-carry-factor/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) portfolio would go long the top quintile of currencies ranked by their interest rate differential and short the bottom quintile.

- 
**Backtesting and Validation** ▴ The constructed factor portfolios must be rigorously backtested over a long historical period, covering various market regimes. Key performance metrics to analyze include average returns, volatility, Sharpe ratio, and maximum drawdown. It is also critical to assess the factor’s correlation with traditional asset classes and other factors to understand its diversification properties.

- 
**[Portfolio Attribution](https://prime.greeks.live/area/portfolio-attribution/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite) Analysis** ▴ Once the factors are validated, they can be used to analyze existing portfolios. This involves running a multiple regression of the portfolio’s historical returns against the returns of the constructed factor portfolios. The resulting coefficients (betas) show the portfolio’s sensitivity to each risk premium.

- 
**Integration with Portfolio Management Systems** ▴ The final step is to integrate the factor model’s outputs into the day-to-day investment process. This could involve creating custom reports that show a portfolio’s factor exposures in real-time, or using the model to guide the construction of new portfolios with specific factor tilts.

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## Quantitative Modeling and Data Analysis

The quantitative heart of the execution process is the construction and analysis of the factor portfolios. The goal is to create a time series of returns for each factor that is investable and accurately represents the risk premium it is designed to capture. This requires careful attention to detail in the portfolio construction methodology.

Consider the construction of a Commodity Term Structure (Carry) factor. The process involves ranking all commodities in the investment universe based on their roll yield at the end of each month. The roll yield can be calculated as the percentage difference between the price of a longer-dated futures contract and the front-month contract.

> Rigorous backtesting across different market regimes is essential to validate the robustness and economic significance of any constructed factor.

The table below presents a simplified, hypothetical example of the monthly sorting process for a commodity [carry factor](https://prime.greeks.live/question/why-does-a-bilateral-rfq-carry-more-inherent-settlement-risk-than-an-exchange-traded-future/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite). Commodities are ranked by their roll yield, and a long-short portfolio is formed.

| Commodity | Front-Month Price | Second-Month Price | Roll Yield (Annualized) | Quintile | Portfolio Action |
| --- | --- | --- | --- | --- | --- |
| Natural Gas | $2.50 | $2.40 | 57.2% | 1 (Top) | Long |
| WTI Crude Oil | $75.00 | $74.50 | 8.1% | 1 (Top) | Long |
| Corn | $4.00 | $4.01 | -3.0% | 3 (Middle) | Neutral |
| Gold | $1900 | $1905 | -3.1% | 3 (Middle) | Neutral |
| Soybeans | $13.00 | $13.10 | -9.1% | 5 (Bottom) | Short |
| VIX Futures | $15.00 | $16.00 | -69.3% | 5 (Bottom) | Short |

This process is repeated monthly, with the portfolio being rebalanced to reflect the new rankings. The resulting time series of the long-short portfolio’s returns represents the pure return of the commodity carry factor. Similar processes are used to construct momentum, value, and other factors for both commodities and currencies.

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## How Does a Factor Model Influence Hedging Decisions?

A significant application of a fully executed factor model is in the realm of strategic hedging. An institution might find that its core business operations give it a natural, unhedged short exposure to energy prices. A traditional approach would be to simply go long a crude oil futures contract. A factor-based approach allows for a more sophisticated hedging strategy.

By decomposing the returns of crude oil into its factor exposures, the institution might discover that a large portion of its risk is an exposure to the commodity momentum factor. Instead of a simple hedge, it could construct a more precise hedge by taking an offsetting position in a commodity momentum factor portfolio. This hedge would be more robust as it targets the underlying systematic risk driver, rather than the idiosyncratic movements of a single commodity.

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## System Integration and Technological Architecture

The technological architecture required to support a factor modeling system is non-trivial. It involves several key components that must work in concert.

- 
**Data Warehouse** ▴ A centralized database is required to store and manage the vast amounts of historical price and economic data needed for the model. This database must be designed for efficient time-series analysis.

- 
**Quantitative Analysis Engine** ▴ This is the computational core of the system. It is typically built using a statistical programming language like Python (with libraries such as Pandas, NumPy, and Statsmodels) or R. This engine runs the code for factor construction, backtesting, and portfolio attribution.

- 
**Reporting and Visualization Layer** ▴ The outputs of the analysis engine need to be presented in a clear and intuitive way to portfolio managers. This often involves a business intelligence tool (like Tableau) or a custom-built web dashboard that displays factor exposures, performance attribution, and other key metrics.

- 
**API Integration with OMS/EMS** ▴ For the model to be fully operational, its outputs must connect with the firm’s Order Management System (OMS) or Execution Management System (EMS). This allows for the seamless translation of model-driven insights into actual trades, whether for rebalancing a factor portfolio or executing a sophisticated factor-based hedge.

The execution of a factor-adjusted model is a significant commitment of resources, but it provides a powerful and enduring analytical framework. It elevates the investment process from a reliance on broad asset class labels to a sophisticated, data-driven methodology focused on harvesting systematic risk premia across the entire investment universe.

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## References

- Arnott, R. Ehsani, S. & Linnainmaa, J. T. (2023). Factor Momentum. Journal of Finance.

- Blocher, J. Cooper, R. & Molyboga, M. (2016). BCM Commodity Factor Model. CME Group.

- Christoffersen, P. Lunde, A. & Olesen, K. V. (2019). Factor Structure in Commodity Futures Return and Volatility. Journal of Financial and Quantitative Analysis, 54(3), 1083-1115.

- Fama, E. F. & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.

- Gupta, T. & Kelly, B. (2019). Factor momentum everywhere. The Journal of Portfolio Management, 45(5), 13-28.

- Roncalli, T. (2017). Factor Investing in Currency Markets ▴ Does it Make Sense?. Amundi Asset Management. Working Paper.

- Szymanowska, M. de Roon, F. & Nijman, T. (2014). An anatomy of commodity risk premia. Journal of Finance, 69(1), 453-482.

- Yang, C. (2013). The term structure of commodity futures prices. Journal of Futures Markets, 33(10), 917-951.

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## Reflection

The successful implementation of a factor-adjusted model across diverse asset classes like futures and FX is more than a quantitative exercise. It represents a fundamental shift in an institution’s operational philosophy. It moves the focus from chasing returns to systematically understanding and managing the underlying drivers of those returns. The framework provides a common language for risk across the entire organization, from the trading desk to the chief investment officer.

As you consider this architectural approach, the pertinent question becomes ▴ how are your portfolio’s returns truly generated? Are you being compensated for risks you understand and have deliberately chosen to take, or are you unknowingly exposed to systematic factors that happen to be in favor? The answers to these questions have profound implications for the resilience of your portfolio and the long-term strategic direction of your firm. The model is a tool, but its greatest value lies in the clarity of thought it enforces.

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## Glossary

<a href="https://prime.greeks.live/area/foreign-exchange/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Foreign Exchange

Meaning ▴ Foreign Exchange (FX), traditionally defining the global decentralized market for currency trading, extends its conceptual framework within the crypto domain to encompass the trading of cryptocurrencies against fiat currencies or other cryptocurrencies. </a><a href="https://prime.greeks.live/area/asset-classes/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Asset Classes

Meaning ▴ Asset Classes, within the crypto ecosystem, denote distinct categories of digital financial instruments characterized by shared fundamental properties, risk profiles, and market behaviors, such as cryptocurrencies, stablecoins, tokenized securities, non-fungible tokens (NFTs), and decentralized finance (DeFi) protocol tokens. </a><a href="https://prime.greeks.live/area/factor-model/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Factor Model

Meaning ▴ A Factor Model, within the quantitative analysis of crypto investing, is a statistical or econometric framework used to explain and predict the returns or risk of digital assets by identifying and measuring their sensitivity to a set of underlying economic, market, or blockchain-specific variables. </a><a href="https://prime.greeks.live/area/commodity-futures/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Commodity Futures

Meaning ▴ Commodity Futures are standardized, legally binding agreements to transact a specified quantity of a commodity at a predetermined price on a future date. </a><a href="https://prime.greeks.live/area/term-structure/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Term Structure

Meaning ▴ Term Structure, in the context of crypto derivatives, specifically options and futures, illustrates the relationship between the implied volatility (for options) or the forward price (for futures) of an underlying digital asset and its time to expiration. </a><a href="https://prime.greeks.live/area/factor-adjusted-model/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Factor-Adjusted Model

Meaning ▴ A Factor-Adjusted Model represents a quantitative framework that modifies or enhances an existing financial model by incorporating specific risk or return factors not initially accounted for in its base structure. </a><a href="https://prime.greeks.live/area/risk-premia/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Risk Premia

Meaning ▴ Risk premia refer to the expected return in excess of the risk-free rate that an investor demands as compensation for bearing a particular type of risk. </a><a href="https://prime.greeks.live/area/roll-yield/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Roll Yield

Meaning ▴ Roll Yield, within the sophisticated realm of crypto futures and options, represents the profit or loss systematically generated when an investor closes an expiring futures contract or option position and simultaneously establishes a new position in a further-dated contract for the identical underlying digital asset. </a><a href="https://prime.greeks.live/area/commodity-momentum/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
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## Commodity Momentum

Meaning ▴ Commodity Momentum refers to a quantitative investment factor that captures the tendency for commodity prices, or their related derivatives, to continue in their recent direction of movement. </a><a href="https://prime.greeks.live/area/systematic-risk/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A cutaway view reveals the intricate core of an institutional-grade digital asset derivatives execution engine. The central price discovery aperture, flanked by pre-trade analytics layers, represents high-fidelity execution capabilities for multi-leg spread and private quotation via RFQ protocols for Bitcoin options](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-prime-os-rfq-protocol-engine-for-high-fidelity-execution-and-price-discovery-on-volatility-surface.jpg)

## Systematic Risk

Meaning ▴ Systematic Risk, also known as market risk or non-diversifiable risk, refers to the inherent risk associated with the overall market or economy, affecting a broad range of assets simultaneously. </a><a href="https://prime.greeks.live/area/risk-premium/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![Three metallic, circular mechanisms represent a calibrated system for institutional-grade digital asset derivatives trading. The central dial signifies price discovery and algorithmic precision within RFQ protocols](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-protocols-and-execution-precision.jpg)

## Risk Premium

Meaning ▴ Risk Premium represents the additional return an investor expects or demands for holding a risky asset compared to a risk-free asset. </a><a href="https://prime.greeks.live/area/futures-contract/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A refined object, dark blue and beige, symbolizes an institutional-grade RFQ platform. Its metallic base with a central sensor embodies the Prime RFQ Intelligence Layer, enabling High-Fidelity Execution, Price Discovery, and efficient Liquidity Pool access for Digital Asset Derivatives within Market Microstructure](https://options.greeks.live/wp-content/uploads/2025/08/prime-os-rfq-protocols-for-digital-asset-derivatives-execution.jpg)

## Futures Contract

Meaning ▴ A futures contract, in the realm of crypto investing, is a standardized legal agreement to buy or sell a specific quantity of an underlying digital asset at a predetermined price on a specified future date. </a><a href="https://prime.greeks.live/area/fx-carry-factor/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A sophisticated apparatus, potentially a price discovery or volatility surface calibration tool. A blue needle with sphere and clamp symbolizes high-fidelity execution pathways and RFQ protocol integration within a Prime RFQ](https://options.greeks.live/wp-content/uploads/2025/07/prime-os-rfq-protocol-institutional-digital-asset-derivatives-execution.jpg)

## Fx Carry Factor

Meaning ▴ FX Carry Factor represents a quantitative investment strategy that aims to profit from interest rate differentials between two currencies. </a><a href="https://prime.greeks.live/area/portfolio-attribution/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A sleek, light interface, a Principal's Prime RFQ, overlays a dark, intricate market microstructure. This represents institutional-grade digital asset derivatives trading, showcasing high-fidelity execution via RFQ protocols](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-prime-os-execution-system.jpg)

## Portfolio Attribution

Meaning ▴ Portfolio Attribution is a quantitative analytical technique used in investment management to decompose a portfolio's returns into various constituent sources, such as asset allocation, security selection, and currency effects. </a><a href="https://prime.greeks.live/question/why-does-a-bilateral-rfq-carry-more-inherent-settlement-risk-than-an-exchange-traded-future/?utm_source=options.greeks.live&utm_medium=referral&utm_campaign=satellite" class="glossary-item-box">
![A precision algorithmic core with layered rings on a reflective surface signifies high-fidelity execution for institutional digital asset derivatives. It optimizes RFQ protocols for price discovery, channeling dark liquidity within a robust Prime RFQ for capital efficiency](https://options.greeks.live/wp-content/uploads/2025/07/institutional-digital-asset-derivatives-rfq-engine-optimized-for-precision.jpg)

## Carry Factor

A bilateral RFQ's settlement risk is higher due to direct counterparty exposure, unlike a future's centrally cleared guarantee. </a>

**Tags:** [Systematic Risk](https://options.greeks.live/area/systematic-risk/), [Factor-Adjusted Model](https://options.greeks.live/area/factor-adjusted-model/), [Factor Investing](https://options.greeks.live/area/factor-investing/), [Quantitative Modeling](https://options.greeks.live/area/quantitative-modeling/), [Futures Contract](https://options.greeks.live/area/futures-contract/), [Foreign Exchange](https://options.greeks.live/area/foreign-exchange/)	

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Profit from market fear by systematically trading VIX backwardation, turning crowd panic into a harvestable risk premium.

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Master the market's hidden dimension by trading the entire volatility surface for a definitive strategic edge.

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Master the VIX curve to decode the market's fear index and position your portfolio for its next seismic shift.

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Harness market volatility as a distinct asset class to generate a systematic, non-correlated income stream.

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Maker-taker fees incentivize liquidity provision, while MQL rules impose a time-based risk, forcing a quantitative recalibration of strategy.

---

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---

**Original URL:** https://options.greeks.live/question/can-a-factor-adjusted-model-be-applied-to-asset-classes-other-than-equities-such-as-futures-or-fx/
