Onboarding new asset types: a case for flexible risk model integration

Thursday, September 3, 2020 | By Patrick Braun, Buy Side Pre-Sales

Onboarding new asset types: a case for flexible risk model integration

For wealth managers and institutional asset allocation specialists exploring long-term investment opportunities, the onboarding of new financial instruments presents both data and risk modeling challenges. This investigation journey can be illustrated using a hypothetical cryptocurrency fund example as part of a broadly invested financial portfolio. This example also advocates the need for analytics to enable upstream data investigation, flexible risk model mapping and support for a broad range of simulation techniques within a cost-controlled environment.

The twisty road to excess returns

In their quest for diversified excess returns, analysts and investors in crypto-assets have often debated if these ‘tech vehicles’ now constitute a formal asset class in their own right. Do these assets exhibit high, stable return correlations amongst themselves? Do they show low return correlations towards other members of the traditional investment universe (equities, bonds or commodities)? While answers may vary based on the selected names and sectors, most individuals will agree that crypto assets have offered investors a bumpy ride. As shown in Fig.1, the example of Bitcoin daily price data reveals a return time series with high, time-varying volatility. On closer investigation, negative skewness and fat tails (kurtosis in excess of 10) put some shadow over the positive average historical return figures. Such patterns can be observed in some other asset classes’ time series too.

Graph shows daily log returns of BTCAbout investment horizons

For risk managers, model portfolio owners and asset allocators considering these instrument types, the need to capture fat-tailed distributional behaviors is key. This requirement comes with the question: what is the investment horizon? With limited data, the computation of risk measures based on direct historical time series and sampling techniques would be acceptable at 1-day, 1-week and maybe 1-month horizons. These approaches capture well the asymmetry observed in the original single-instrument returns, including spikes from Q1-2020. At longer time horizons considered for asset allocation decisions though, analysts may prefer stochastic techniques with multi-step simulations. The required Monte Carlo framework would need to provide users with consistent modeling choices for all asset types under consideration. It means diffusion models to test, scenario density to amend and co-dependence structures to explore. This process shall include the new investment candidates too. As shown in Fig. 2, the asset allocation jigsaw includes numerous moving parts. In this hypothetical case, these parts range from equities, fixed income products—plain and derivatives—to commodity exposures, gold and a range of crypto assets. Real-life allocation cases can, of course, be much broader.

Image shows model portfolio viewA journey into the “new normal”

The questions of risk model selection, suitable calibration for the retained time horizons, and cross-functional alignment are nothing new to finance professionals. These choices reflect front office, risk management and asset allocation goals. In essence, they define the investment process of an asset or wealth management organization. The COVID-19 crisis and 2020 financial market reactions have just reminded us of the regular need to adjust these established processes to integrate the “new normal.” For example, while exposures to crypto assets may not be part of the conventional asset mix to date, risk analytics platform users shall have the option to test such investment ideas at various horizons if they so wish. How would these fat-tailed instruments impact their broader model portfolio in risk/return and diversification terms? As featured in Fig. 3, the answers will be unique to each investment team, or client. These depend on modeling assumptions, data points, horizons, co-dependencies and more.

Image shows stochastic vs historic return distribution examplesFor asset allocation specialists, investments into new instrument types is a real risk management conundrum. The exercise is data heavy. It requires various analytics in one place, generous simulation capabilities and easy model amendments. In an investment world where cost controls play an important role, these capabilities most often leverage Cloud infrastructure and managed services. For wealth managers, model portfolio owners and asset allocators, the journey towards capturing (potential) excess returns from new asset classes remains challenging. Fast-evolving analytics platforms should make this granular investigation process lighter to perform.

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Asset Management, Risk Management

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