Updated: Jan 15
Last week, BCMstrategy, Inc. held a webinar with Interactive Brokers that explored the challenges risk managers and strategists face when confronted with low data environments. The current COVID19 period presents unique challenges for scenario analysis at the tail of the distribution, where investment risks are uniquely exposed to public policy risks in addition to traditional financial risks.
This blog series is based on that webinar series.
We have long observed that markets need data like people need oxygen.
Risk managers, strategists, and analysts need reliable, consistent access to high quality data from which to make risk assessments and trend projections. Until recently, that data has all been structured, quantitative data drawn from markets.
One of the many consequences of the COVID-19 pandemic is that it creates real gaps in structured economic data from which financial and economic risks are measured and scenario analyses are created.
The gaps occur both with respect to sourcing and analytics. Today’s post focuses on sourcing. Tomorrow’s post will focus on analytics issues.
Two Data Deficits
1. Gaps in Traditional Data
Decreased passenger flights due to the pandemic have compromised weather forecasting data collection globally. Since aircraft collect over 250 million atmospheric measurements annually, dramatic dips in passenger flights decrease commensurately the accuracy of weather forecasts. The European Centre for Medium-Range Weather Forecasts estimates that loss of all data from aircraft could reduce forecast accuracy by 15%.
In the financial world, the passenger jet equivalent of meteorological data collection is regulatory reporting. Every day, financial firms across the markets report to various government entities real-time data regarding sales, defaults, margin calls, portfolio values, losses, inventory changes, and various risk metrics.
Aggregated data provides all firms with the opportunity to benchmark their own performance relative to their peers. Aggregated data also provides the foundation for determining possible parameter adjustments and assumptions adjustments within the scenario analysis and risk management function.
The pandemic disrupted these data collection channels in the financial system just as the grounding of passenger jets is compromising meteorological data collection: financial regulators and government agencies globally delayed by 3 months or more regular data collection from financial institution.
At a minimum, financial firms and strategists must make risk assessments based on outdated, delayed information. If additionally the universe of reported data contains gaps as different firms report data at different intervals, the comprehensiveness of the information will create extrapolation challenges.
The impact on risk management is comparable to the impact on weather forecasting: decreased access to timely, accurate, consistent, and reliable data decreases the effectiveness of models that run on that data.
Ironically, the accuracy of risk forecasts and scenario analysis will be under pressure at precisely the moment during which they are most needed.
2. New Data Gaps
The data problem is compounded by the increased prominence that event/public policy risks play in shaping the risk environment. Significant increases in fiscal spending and major expansions in central bank support facilities change dramatically the near-term exposure to liquidity and credit risks by substituting systematically sovereign risk for private sector risk. This substitution effect has helped calm markets and deliver impressive market rebounds.
However, as the time series below from our PolicyScope platform indicates, the mountain of emergency measures supporting the economy means that financial institutions, consumers and companies are all now far more dependent on public policy shifts than in the past.
At some point, the pandemic will recede and governments will begin unwinding the extraordinary support structures. This unwind will impact risk profiles for all financial institutions and intermediaries. But it will not occur in a symmetrical manner internationally.
From mid-February to early April, policymakers across borders successively took similar actions. Dramatic increases in fiscal spending were paired with massive central bank liquidity lines for an expanding range of private sector credit assets a well as foreign exchange swap lines between central banks and widespread relaxation of various financial regulations (regulatory capital, accounting standards regarding expected losses, margin requirements, etc.). The moves may not have been coordinated formally across borders but the moves were all convergent conceptually.
The exit from these policies will be a profoundly national decision, driven by health and economic conditions in the local economy. With sovereign risk now embedded within credit portfolios through government guarantees, financial firms now hold a high concentration of public policy risk on their balance sheets.
However, none of the data relevant to public policy shifts is structured….the data is all unstructured verbal data.
Fortunately, our PolicyScope platform uses patented technology to develop robust data from the language of the public policy process.
Moreover, not all governments and central banks are providing transparency to markets regarding the scale and scope of how private sector firms are using the emergency pandemic support programs. Data regarding the size of central bank balance sheets, aggregate asset purchases (private sector securities, sovereign bonds), and other market activity by some central banks is welcome, of course. But the data provide insufficient detail to generate robust risk assessments, particularly when paired with other data gaps mentioned above.
Ironically, heightened exposure to these public policy risks amid a low data environment is likely to increase the prominence of scenario analysis.
When markets cannot rely on familiar foundations to form their risk forecasts, they must turn to scenario analysis to assess portfolio-level impacts associated with specific shifts in market behavior and public policy.
In order for scenario analysis to provide meaningful insight into risk exposures during and after the pandemic, scenario analysis designers must proactively address the remaining analytical challenges associated with a low data environment.
Tune in tomorrow for our assessment of how to meet the analytical challenges.
PolicyScope data is available through the Bloomberg Enterprise Access Point.
Customized widgets and dashboards are available via API HERE.