June 29, 2020
Extreme market events are evidently more frequent than commonly thought of, therefore their effects on portfolio performance should be diligently risk-adjusted by investors in order to create defensive portfolio re-balancing action scenarios for a wide spectrum of unexpected shocks.
«Think not of what you see, but what it took to produce what you see.»
Benoit Mandelbrot, Mathematician and Creator of Fractal Geometry.
With the exception of portfolio performance, no other investment notion has awash the financial press and the mind of investors as dominantly as that of market volatility. The most common quote for volatility is the statistical dispersion of returns for a security or a market index, represented by the variance or standard deviation.
In February 2020, a prolonged period of equity price appreciation came to an abrupt end. Extremely low levels of stock market volatility, paired with very low interest rates motivated investors to retain high equity exposures and become increasingly complacent with the brewing risks of their portfolios. The lack of short-term market volatility was deciphered as a vivid indication of market stability and investors jumped on the bandwagon in fear of missing out (or FOMO, as now popularly cited).
Between February 19 and March 23, the S&P500 Index dropped from 3,386 to 2,237, a correction of 34%. In the same period, the VIX ‘fear’ Index jumped by 328%. A spreading COVID-19 pandemic had triggered a massive increase in market volatility within a few trading days. The price reversion was so quick and violent that the less attentive investors had no time to react.
Could have investors predicted the imminent market reckoning and had, in advance, reallocated capital into non-affected assets or cash, thus preserving their capital through this turbulence? No, not in a consistent and repetitive fashion. The two defining characteristics of future change are the existence of wild volatility and the impossibility of predicting it. The mission of those involved in Asset Management is not to predict the future but to manage their positions of high conviction within a disciplined risk framework.
Monetary easing and government bailouts employed by the world’s Central Banks may prevent certain businesses from going under (often temporarily), but they also increase the possibility of a system-wide collapse. Artificially suppressing short-term volatility weakens complex systems, encourages excessive risk-taking and creates a false sense of stability, while in reality it only increases long-term risks at the expense of short-term vulnerable market price growth.
Such volatility-reduction policy mechanisms combined with collective market behavioural psychology (false stability) and investor inertia (FOMO) may also cause volatility to cluster, meaning that large volatility bursts tend to happen more infrequently but at successive over-sized amounts. Empirically, this phenomenon can be thought of as a system’s reaction to defuse and mean-revert an artificially suppressed pressure.
What can then investors do in practice to assess and understand their portfolios’ vulnerability to future violent volatility fluctuations? How can one test and simulate the behaviour and loss-tolerance of a multi-asset-class portfolio for the ‘occasional’ 328% increase in stock price volatility?
In addition to creating portfolio stress-test simulations based on past historical crises (https://finvent.com/historical-knowledge-and-decision-quality/), one can adjust such ‘worst case’ scenarios to modern frameworks via customized portfolio stress-testing that allows user-defined changes to the portfolio’s driving risk factors.
The graph below demonstrates an example of a KlarityRisk customized factor-based stress-testing concept at work, for a global multi-asset, multi-currency diversified portfolio. The model portfolio is over-weighted toward US equities, with its remaining balance allocated to Europe, UK and Japan. The analysis simulates a user-defined volatility increase of 20% on its US equity holdings and depicts the VaR changes at portfolio level (the maximum potential portfolio loss over a given period, under a confidence level) and the contribution of each asset class to the total portfolio VaR.
Importantly, when the user increases the US equity volatility by 20% but makes no changes to the volatility of the other asset classes, KlarityRisk utilizes the correlations between the US equities and each other asset class, for the measurement period, to adjust the volatility values of the latter too. The final output thus represents a holistic stress effect of the portfolio to factor changes. KlarityRisk utilizes a wide range of critical risk factors to perform similar stress-test simulation scenarios.
Furthermore, KlarityRisk can produce a pre-test and post-stress risk decomposition of the portfolio for an exhaustive list of categorizations such as asset class, sector, industry, risk country, reference currency and issuer credit rating, thus effectively identifying any imbalances between individual position weights and their associated risks.
We do not need to predict a system’s hidden future vulnerabilities but instead use smart software solutions to dynamically digest the collective information that market volatility provides, in order to make our investment decision-making process more adaptable and longer-term successful.
FINVENT Software Solutions is a trusted provider of advanced financial software applications and custom engineering services. The award-winning KlarityRisk platform specializes in investment risk analytics and fixed income performance attribution reporting and it is offered to financial institutions and family offices in European and African countries. Finvent is the sole SS&C Advent distributor worldwide and its products are natively integrated with those of SS&C Advent. Finvent is a partner of FactSet, the integrated data aggregator platform.