Featured Image

In Risk Management, Fear Is a Step to Investment Wisdom

Risk, Performance, and Reporting

By Boryana Racheva-Iotova  |  December 11, 2018

The observation that fear is the beginning of wisdom is as old as humanity and still holds true. But we, as human beings, dislike the feeling of fear, and thus avoid thinking about our fears or of how to manage and understand them more precisely. Quite often this is  because we do not have a way to transform those fears into wisdom.

Fears and Risk Management

In the domain of risk management, however, technology to aid in this transformation is developing rapidly. Today, the asset management and investor communities are much better equipped with advanced, flexible tools that profile extreme uncertainties and help transform these uncertainties into investment wisdom.

Fears are the manifestations of those uncertainties and risks that we fail to evaluate well and which we cannot monitor or control. Focusing on financial risks, we can broadly differentiate among: (1) market risk that is business-as-usual-risk, (2) extreme events risk, and (3) risk of regime shifts or structural breaks, including market disruptions or disasters.

We do a relatively good job of understanding and managing (1), but fear is associated with (2) and (3), and they require investments and efforts to understand, quantify, and manage (see table below). Moreover, extreme events and pronounced turbulence have become natural phenomena of the modern financial system—a system that is exposed to potential structural breaks that can propagate globally.

For many decades, up until the early signs of market tremor in 2007, financial markets were characterized by extended periods of “normal” behavior, followed by shorter “stressed period” phases exhibiting higher probability of extreme events. This is no longer the case as exhibited in Figure 1.

For most markets, the “normal” periods are a thing of the past. Contributing to that are both factors related to macro-economic, political, and other global developments and others that stem from the irreversible evolutionary developments of the financial markets.

The MSCI US Turbulence Indicator has not gone below 50% post-2008 – even during periods of low volatility – indicating an “unstable” state of the system, i.e., a high probability of extreme events.
Figure 1: Spread between Normal and Fat-Tailed 99% VaR, used as an indicator of turbulence for MSCI US, and which ranges from zero to 100%. The MSCI US Turbulence Indicator has not gone below 50% post-2008 – even during periods of low volatility – indicating an “unstable” state of the system, i.e., a high probability of extreme events.

Asset returns themselves are no longer predominantly fundamentally driven. Depending on the investment horizon, market micro-structure or behavioral factors may play a determinant effect. The market landscape is more and more defined by the behavioral reactions of market participants to news and the resulting re-positioning towards anticipated risks. Political uncertainties, environmental impact, and technological disruptions can cause both short-term turbulence and long-term structural breaks.

Turbulence Drivers

The ever-expanding universe of turbulence-driving factors is linked to the natural evolution of the financial system: technology development, transformation of investment strategies including reliance on machine learning and algorithmic approaches, the volume of passive offerings, and the growth of smart-beta funds, to name a few. Such developments increase the speed of the “market clock” and lead to extreme dependencies, crowding, and risks of short-term liquidity evaporation – all of which create turbulence resulting in a significant probability for large sudden drops in the markets.

The stressed or “turbulent” periods are not necessarily those with the highest volatility. These are the periods when – relative to the local volatility – returns (positive and negative) that are both abrupt and large in magnitude can happen with high probability, even if the present-day volatility is relatively low. These can be called times of “false safety.”

Moreover, certain monetary and economic disbalances are silently mounting pressure across asset classes, leading to (mis-)valuation in a non-obvious manner that at some point may call for valuation rebalancing to correct dislocations.

Such a complex environment necessitates advanced risk monitoring processes and portfolio- and investment-product construction approaches that utilize sophisticated and sound “new-gen” risk modeling techniques. These modern approaches depend on open, flexible technology systems and architectures built to quickly and efficiently (and, often, agnostically) fold in new applications and sources of information. Not only banks and asset managers, but also institutional and individual investors need to be prepared for the not-if-but-when prospects of extreme events, market crises, and disruptions.

For institutional investors, this means acquiring the know-how, systems, and people to understand the facets of risk dynamics, empowering them to define long-term asset allocation approaches that can help them be prepared for difficult-to-predict structural market changes.

This also means that institutional investors ought to work with asset managers that provide the essential building blocks to implement those capabilities, and who help navigate around the turbulence spots by creating products resilient to short-term drawdowns. Putting this in the context of our intro: Institutional investors must acquire the know-how, solutions, and partners that transform fears into wisdom. Individual investors should seek advisors capable of providing transparency, effective communication, efficient ways of delivering professional services, and diversity of products including tail-risk and volatility protection.

Technology is developing rapidly, and today the asset management community is much better equipped with advanced tools that profile risk tolerance characteristics, quantify various risk attributes, and implicitly manage the numerous components of risk. In addition to specific risk domain expertise, a key enabler of these abilities that has emerged in recent years is the growth of collaborative, nimble approaches to building the kinds of multi-vendor, data-rich ecosystems that enable financial institutions to assemble differentiated sets of capabilities to create value for their clients.



Main Risk Measure

Portfolio Construction Approach

Business-as-usual risk: Typical market swings.

Uncertainties related to typical market swings, which, in most cases, lead to diversifiable risks. In general, the system is in a stable (steady) state and these risks are a natural feature of its dynamic behavior.

Standard Deviation, Tracking Error, Correlations, Normal distributions.

Advanced approaches would employ GARCH-type models.

Well-diversified, balanced portfolios. Traditional risk budgeting and Mean-variance optimization approaches to ensure diversification based on correlations achieving risk minimization criteria.

Necessity: Ensure diversified portfolios with target vol/tracking error to minimize the impact of regular market swings.

Tail risk: Extreme events, market crashes.

These events destabilize the system; they can vary from very short-term (e.g., flash-crashes) to prolonged severe market downturns. These always encompass some form of systemic risk, be it on micro-structure or macro-structure level, transmission of risks/losses between market participants, and contamination from one market to another.

Expected Tail Loss (Expected Shortfall)*, Non-gaussian fat-tailed distributions with tail dependence (non-gaussian copula functions).

*ETL is already a requirement for banks' trading books. However, measuring ETL based on Normal distribution does not add value beyond the standard deviation. Measurement and quantification of ETL and related risk metrics must be done based on richer probabilistic models that include “fat-tails” and extreme dependencies

ETL-based risk budgeting and optimization for the construction of hedges and tactical overlays and optimal combination of “regular” products with tail-hedging products.

Necessity: avoid big drawdowns that can cause outflows and liquidity shocks, minimize drawdowns.

Structural breaks: These destabilize the system to such an extent that it enters a new regime with a prolonged period of altered dynamics.

Stress testing including “new-generation” risk factors.

Simulations based on EVT to quantify probability and association of disaster/disruptive events and study portfolio impact.


Long-term multi-step optimization with time-varying objective functions to control St.Dev/Tracking Error, ETL, liquidity profile, and abilities to incorporate stress-scenarios constraints (user-defined or EVT projects); Bayesian approaches to overlay view.

Necessity: Ensure that long-term investment objectives can be reached under diverse market conditions.

Transforming Fears into Wisdom

A new generation of advanced risk-based, human-guided, semi-automated portfolio construction and product structuring approaches is on its way. Financial institutions are working hard to turn fears into wisdom by employing new tools and systems for managing risks and offering to their institutional and retail clients novel solutions with risk-measurement approaches built into the investment decision and product design methods.

As a technology company, FactSet works closely with financial institutions across the spectrum to help them deliver such solutions. To highlight one such relevant example, we collaborated with a leading asset manager to develop an Investment Advisory Portal based on advanced risk analytics. The platform aims at providing investment professionals with a deeper level of insight during portfolio construction, including risk and return drivers and trade-offs, diversification, sensitivity analysis, correlations, and other quantitative factors presented at multiple investment horizons and using a combination of business-as-usual and tail risk metrics – all delivered in an easy-to-use, solutions-oriented workflow. We also built, jointly with clients, portfolio solutions and recommendation reports for both institutional and individual investors, encompassing various aspects of risk analysis and stress tests as described earlier, and suggest model portfolios providing improved risk-return characteristics for different risk and investment horizon mandates.

Behind the scenes, the design of these model portfolios is based on advanced multi-asset-class optimization techniques with complex objective functions that could be a combination of different risk measures (e.g., ETL, tracking error), sensitivity constraints, or max drawdown allowances during specific stress-scenarios, and can incorporate multi-horizon investment objectives.

Another application of advanced tails risk modeling is the building of effective, stress-durable tactical overlays based on optimization approaches extending across asset classes with non-linear payoffs using tail-risk functions. In the Wealth and Retail space, the next step is incorporating behavioral components to the objective function to account for fear and extreme-scenarios biases.

Gaining Efficiency

Factor investing is proliferating, and now the understanding of how factor-tilted positioning would be impacted during market stress and support optimal combination of products is the next step further. Using tail-risk-equipped tools, the design of strategies to weather adverse market conditions, such as risk-parity approaches, is proving more efficient. We also seek out and work with partners to incorporate new risk drivers into our stress test scenarios, such as political and weather risk, and to move innovation forward, reflecting the evolving complexity of the global markets. Our regulatory solutions team's cooperation with clients and the market also supports this trend.

The risk models employed by financial services institutions continue to evolve in line with (and are driven by) the supervisory and regulatory transition to risk-based frameworks which are reflected in updated multi-sector capital requirements and new risk measurement, management, and reporting. We are exploring this connection from a holistic risk-management standpoint, as well as the important connections between risk management and other stages of the portfolio lifecycle, from fundamental research through portfolio construction, trading, and performance measurement and attribution.

Whether a client is a risk-averse pension fund or a tech-savvy, high-risk, mass affluent Millennial investor, building advanced risk technology directly into the portfolio construction process is becoming a preeminent approach to navigating through turbulent periods to achieve long-term investment objectives. The tools and guides to do so are available; without them, efficiently (and profitably) finding our way through the plethora of geopolitical and structural risks, and within the unpredictable dynamics of the modern markets, may prove nearly impossible.

This article originally ran on www.garp.org

New call-to-action

Boryana Racheva-Iotova

Senior Vice President, Senior Director of Research, Risk and Quantitative Analytics

Dr. Boryana Racheva-Iotova is Senior Vice President, Senior Director of Research, Risk and Quantitative Analytics at FactSet. In this role, she leads the strategy for risk and quantitative analytics solutions that includes research, sales, and product development strategies. She is a co-founder of FinAnalytica and the former Global Head of Risk at BISAM and has over 15 years of experience in building risk and quant portfolio management software solutions. Before founding FinAnalytica, Dr. Racheva-Iotova led the implementation of a Monte-Carlo based VaR calculation to meet the Basel II requirements at SGZ Bank, as well as the development of six patented methodologies for FinAnalytica. In 2018, she received the Risk Professional of the Year Award from Waters Technology based on her achievements building risk management software solutions and translating the latest academic advancements in practical applications to meet the needs of financial industry practitioners. Dr. Racheva-Iotova earned a Master of Science in Probability and Statistics at Sofia University and a Doctor of Science from Ludwig Maximilian University of Munich.


The information contained in this article is not investment advice. FactSet does not endorse or recommend any investments and assumes no liability for any consequence relating directly or indirectly to any action or inaction taken based on the information contained in this article.