Risk model used which has no lag (Figure 24.2). The alpha model hence includes the return from 13 months ago, but the risk model does not. As a conclusion when opti-mizing, the optimizer sees return but no factor risk in month 13 and places a large bet. On the other hand, the risk model includes.
- Axioma Risk Model Handbook
- Axioma Risk Model Handbook Pdf
- Axioma Robust Risk Model Version 4 Handbook
- Axioma Ventures
NEW YORK, July 27, 2020 /PRNewswire/ -- Qontigo, aninvestment intelligence leader and provider of best-of-breedanalytics and world-class indices, has announced the integration ofthe Axioma Factor-based Fixed Income Risk Model in their enterpriseportfolio risk management system, Axioma Risk. The cross-sectionalstyle-based model, which is also available in a flat file format,is powered by proprietary methodology and allows intuitive risk andperformance attribution analysis, as well as robust portfolioconstruction aligned with both passive and active strategies.
- By offering all three model types, Axioma helps front and middle office functions at buy-side firms align their view of risk in a constantly evolving and increasingly complex environment. Axioma incorporated client feedback in the development of the new US equity models, which include the following updates.
- Axioma’s latest US Risk Model suite, US4, for analysis. Risk Differences Fig. 1 shows a time series plot of the predicted active risk using Axioma’s US4 Fundamental Medium Horizon.
Axioma Risk offers broad pricing and flexible analyticscapabilities across multi-asset class portfolios with the abilityto run linear or full repricing on a single, cloud-basedplatform.
Style-oriented fixed income factor investing has grown inpopularity and according to Greenwich Associates, 70% of investmentmanagers see large opportunities for fixed income factor investingover the next few years. However, owing to the lack of quality andconsistency of fixed income data, bringing factor investing tofixed income has previously been a challenge, resulting in modelswith thin rules-based sector factors, strong sensitivity to ratingsmigration and volatility estimates dominated by noisy data.
Axioma Risk Model Handbook
'Fixed income data has historically been frustrating, especiallyfor investors that have more experience in equities. A big part ofthe problem is that data must be transformed in order to proveuseful in support of a style-based approach. The advancements infixed income trading support tools have made managing andtransforming fixed income data more viable and as a result there'sfinally an opportunity to meet the growing demand to develop asystematic analog to equity factor investing in bond markets,' saidKevin McPartland, Managing Directorat Greenwich Associates. 'Enabling sophisticated, flexible – andconsistent – risk analysis is key for developing innovativestrategies and rigorous portfoliomanagement.'
The Axioma Factor-based Fixed Income Risk Model uses advancedmodeling techniques to reliably capture systematic risk in a numberof ways, including a more accurate issuer classification system andthe estimation of bond specific risk from both issuer andissue-specific spread risk. The model has been designed to supporta broad cross-section of the credit universe from investment grade,through to high yield and emerging markets.
Axioma Risk Model Handbook Pdf
'The Axioma Factor-based Fixed Income Risk Model enhances analready market-leading multi-asset risk platform and delivers aninnovative solution to enable both a new style of fixed incomefactor risk management and factor-focused portfolio construction,'said Ian Lumb, Qontigo's ManagingDirector and Head of Multi-Asset Solutions, EMEA andAPAC. 'Integrating the model in the cloud-based Axioma Riskplatform as well as making it available in a portable flat fileformat for use in Qontigo's leading optimizer or third partyinvestment tools really is the best of both worlds for fixed incomeinvestors.'
The complete Axioma Fixed Income Solutions Suite includes:
- Axioma Fixed Income Spread Curves
- Axioma Granular Fixed Income Risk Model
- Axioma Factor-based Fixed Income Risk Model
*Bringing factor to fixed income, Greenwich Associates 2019
About Qontigo
Qontigo is a financial intelligence innovator and a leader inthe modernization of investment management, from risk to return.The combination of the group's world-class indices andbest-of-breed analytics, with its technological expertise andcustomer-driven innovation, enables its clients to achievecompetitive advantage in a rapidly changing marketplace. Qontigo'sglobal client base includes the world's largest financial productsissuers, capital owners and asset managers. Created in 2019 throughthe combination of Axioma, DAX and STOXX, Qontigo is part ofDeutsche Börse Group, headquartered in Eschborn with key locationsin New York, Zug and London.
www.qontigo.com
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SOURCE Qontigo
Last week in the series, we discussed how lack of excess return on actively managed portfolios is not purely a function of stock selection. Regardless of strategy employed, however, intelligent portfolio construction can help improve performance, and through the processes discussed in this series, can aid in the transition from pure research to portfolio construction.
We’ve already walked through our process for modeling alpha estimates across global equities. Here, we’ll discuss mean-variance optimization techniques, and how to translate our findings into an investable portfolio.
Related: Moving from Research to Construction: Stock Selection
Measuring Our Signal
In order to measure the efficacy of our signal, we back-tested our results using FactSet Alpha Testing. We grouped our universe into quintiles and rebalanced monthly for 10 years. The first quintile represents the top 20% of securities based on high alpha estimates, and the fifth quintile is comprised of the 20% of securities with the worst alpha estimates. The chart below highlights the cumulative equal weighted return of each quintile relative to the benchmark.
As we can see, there is a clear monotonic relationship between our alpha estimate and forward returns, with the top quintile producing 268% of outperformance over ten years. The following graphs illustrate the Information Coefficient and Spread (Long Q1 / Short Q5) Returns across GICS Sectors, Regions, and Size buckets, as well as the risk return tradeoff between investing in the various quintile portfolios.
We can then conclude that our stock selection framework does what it’s supposed to do: pick good stocks. When weighting equally, ignoring liquidity, transaction costs, and other real-world investment constraints, the Q1 portfolio does exceptionally well over the 10-year period analyzed. Our next task is to transition from signal to portfolio while hopefully achieving similar performance results.
Constructing Portfolios
How can we build an investable portfolio, through time, that harnesses the strength of our stock selection model? Our portfolios are constructed under the Markowitz mean-variance optimization framework, which seeks to maximize a utility function while adhering to portfolio constraints. We also attempt to minimize factor alignment problems through the use of the Alpha Alignment Factor approach presented by Saxena and Stubbs in 2010. We utilize the Axioma Portfolio Optimizer and Risk Models integrated within FactSet, as well as FactSet’s Portfolio Simulation utility to historically back-test the portfolios over 10 years. Our base case optimization is formulated as follows:
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Model Inputs
- Initial Holdings - $1B USD
- Trade Universe / Benchmark - MSCI AC World Index
- Monthly Rebalance
- Risk Model - Axioma World-Wide Fundamental MH
- ITG Global Cost Curves
Objective
- Maximize Total Return:
Constraints
- Fully Invested - 0% Cash
- Max Asset Weight - 8%
- Threshold Position - 35 bps
- Liquidity - 10% of Security’s 1M Avg. Dollars Traded
- Turnover - 20% Buy Side
- Active Risk - 7.5%
In addition to our base case portfolio, we augment our strategy using the following methods.
First, we employ a statistically based risk model to control active risk in the portfolio. Statistical risk models are mathematical constructs responsible for observed correlations in asset returns. Unlike fundamental risk factors, statistical factors have no descriptive value and are often referred to as “Blind Factors.” While essentially useless for attributing volatility, statistical risk factors are not confined to the definitions of fundamental factors and can thus dynamically pick up systematic effects in the market as they evolve and/or are beyond the scope of pre-defined factors. Statistically-based risk models have been shown to improve ex-post results in optimized portfolios in research conducted by Guerard, Markowitz, and Xu in 2013. See the 2010 version of the Axioma Risk Model Handbook for further detail regarding Axioma Statistical Risk Models.
Face swap software for mac. Our second approach is to budget risk at the security level. Risk budgeting involves limiting the contribution to absolute or active risk from a set of assets or group of assets in the portfolio. Our “Risk Budget” portfolio incorporates a statistical risk model and limits any single security from contributing more than 8% of the portfolio’s total tracking error. Our last improvement to the base case is to incorporate transaction cost management into the utility function of the optimization model. Our new utility function considers the tradeoff between expected return and transaction costs.
Axioma Robust Risk Model Version 4 Handbook
- = Portfolio Utility
- = Weight of Alpha | = Security Expected Return
- = Weight of Transaction Costs | = Estimated Transaction Costs
Our final portfolio incorporates a statistical risk model, risk budget constraint, and transaction cost variable within our utility function.
Next week in the final installment of this series, we’ll review ex-post performance results across all of our portfolios, and we’ll leave you with some thoughts regarding further enhancements to the portfolio construction process through the use of exogenous factors and shifting market regimes.
Axioma Ventures
Read part one of the series: Moving from Research to Construction: Stock Selection