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On Uncertainty, Instability Of Financial Markets

Yannis Sardis, 13 July 2020

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What can investors do to assess and understand their portfolios’ vulnerability to future violent volatility fluctuations?

There are different ways to view investments and risks. Technology firms that serve wealth managers by giving them and their clients a way to view portfolio risk are in an interesting place to comment. One such organisation is Finvent Software Solutions. Here Yannis Sardis, director, talks about the uncertainties and volatilities in markets. (To understand the KlarityRisk references below, in 2018, Finvent, regional distributor and service provider of SS&C Advent Software, entered into a majority shareholding position with KlarityRisk, a multi-asset class solution for market risk, portfolio construction, limits monitoring and investment decision analytics for the buy-side.)

The editors are glad to share these views and invite readers to jump into the conversation. The usual editorial disclaimers apply. Email tom.burroughes@wealthbriefing.com and jackie.bennion@clearviewpublishing.com

Extreme market events are evidently more frequent than commonly thought, therefore their effects on portfolio performance should be diligently risk-adjusted by investors in order to create defensive portfolio re-balancing action 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, said.

With the exception of portfolio performance, no other investment notion has flooded the financial press and the minds 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 19 February and 23 March, the S&P 500 Index dropped from 3,386 to 2,237, a correction of 34 per cent. In the same period, the VIX “fear” Index jumped by 328 per cent. 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 investors have predicted the imminent market reckoning in advance and 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 then can investors do 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 per cent increase in stock price volatility?

In addition to creating portfolio stress-test simulations based on past historical crises (https://www.fwreport.com/article.php?id=187395#.XwRY120zaUm), one can adjust such “worst case” scenarios to modern frameworks via customised portfolio stress-testing which allow user-defined changes to the portfolio’s driving risk factors.

The graph below demonstrates an example of a KlarityRisk customised factor-based stress-testing concept at work, for a global multi-asset, multi-currency diversified portfolio. The model portfolio is over-weighted towards US equities, with its remaining balance allocated to Europe, the UK and Japan. The analysis simulates a user-defined volatility increase of 20 per cent 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 per cent but makes no changes to the volatility of the other asset classes, KlarityRisk uses the correlations between the US equities and each other asset class, for the measurement period, to adjust the volatility values of the latter. The final output thus represents a holistic stress effect of the portfolio to factor changes. KlarityRisk utilises 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 categorisations 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.

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