What Is Risk Mitigation in Finance?

Lesson K: The Systematic and Non-Systematic Breakdown

Todd Mei, PhD
1.2 Labs
5 min readJan 29, 2023

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Image by Gino Crescoli from Pixabay

mit·i·ga·tion

/ˌmidəˈɡāSH(ə)n/

noun

  1. the action of reducing the severity, seriousness, or painfulness of something.

With financial markets, there are generally two categories of risk:

  • Systematic risk
  • Non-systematic or unsystematic risk

If you think of the financial markets as comprising one uniform system, then systematic risks are those that exist internal to the markets as features of operating financially — i.e. trading and investing. Because such risk is inherent to the financial system, it cannot be entirely avoided. However, there are certain steps one can take to dampen or mitigate such risk.

Examples of systematic risk include:

  • Political and regulatory policies affecting markets (think of SEC regulation of crypto)
  • Interest rates (the cost of borrowing goes up, things cost more)
  • Market risk (price volatility)
  • Exchange rate risk (a currency loses its value, which especially hurts import-export and international investments)
  • Counterparty, or default, risk (when someone fails to pay you what is owed)

Non-systematic risks are those that pertain specifically to individual investments. Although the adjective “non-systematic” might suggest risk that is external to the system of financial markets, you have to think of it as a way of trying to understand how a specific investment may present problems regardless of how the system (i.e. the financial market) is performing.

For example, the markets could be experiencing a bull run, but if an investment involves a company whose management structure is unstable (e.g. Twitter), then regardless of how well a sector is performing, that particular investment poses some risks.

A risk mitigation strategy attempts to take into account systematic risks and dampen or even neutralize their effects. Generally speaking, risk mitigation in financial markets entails the diversification of investments in order to take into account specific, potential problems. So, for example, if you have invested in a few tech stocks, you might want to balance your portfolio with long-term bonds. Tech stocks tend to be more volatile than other assets since they involve growth and innovation. Or, you can invest in a mutual fund that already has diversification built into its portfolio.

Specific common strategies of risk mitigation include:

  • Hedging (see, for example, delta neutral positions)
  • Interest rate swapping
  • Reduce the amount of illiquid investments, or investments that cannot be trade easily
  • Risk analysis of geo-political sentiment and conditions
  • Purchasing insurance (e.g. stablecoin de-peg insurance)

Non-systematic risks can be mitigated by not investing in a particular asset whose structural stability might be in question or by diversifying one’s portfolio so as to dilute the effects of a particular investment that might potentially crash.

How to Measure Risk? Value at Risk

Let’s look at one way to determine how much risk exposure a financial portfolio might have.

For anyone who’s interested in a financial portfolio, yet does not want to spend much time following the market trends, the concept of “value at risk” may not necessarily register. This is because the majority of minor retail investors tend to be of the “set and forget” mentality, where one simply invests according to a long-term strategy — small gains that build up over a long period of time. It’s the classic form of the long position.

Yet for advanced retail investors and accredited investors (e.g. wealthy individuals and financial institutions), “value at risk” (VaR, hereafter) is a key metric when deciding to adjust their financial portfolio according to market conditions.

VaR is essentially the amount of risk exposure inherent to a financial portfolio. In general, the more diversified the portfolio is, the more it can mitigate its exposure to risk. Measurement of VaR includes three factors:

  • the amount of potential loss
  • the probability of the loss occurring
  • the time frame in which the loss might occur

VaR uses a normal distribution analysis to get at the probability of the loss occurring. Here’s an example from Investopedia:

“A financial firm, for example, may determine an asset has a 3% one-month VaR of 2%, representing a 3% chance of the asset declining in value by 2% during the one-month time frame. The conversion of the 3% chance of occurrence to a daily ratio places the odds of a 2% loss at one day per month.”

There are some caveats to using VaR.

(1)
Because VaR uses normal distribution, it is subject to misinterpretation of risk probabilities when and where its data tends not to have a normal distribution. Financial markets tend not to have normal distributions.

Chart from Klement on Investing

(2)
VaR works well for a portfolio with a small number of assets. Calculations become more unreliable as a larger number of assets makes statistical measurements more difficult.

(3)
VaR calculations are only as good as the data profile used. For example, if the historical data uses a time slice in which there is low volatility, then the VaR would understate the probability for risk.

How This Can Be Applied

Diversification of a portfolio is something that the everyday retail investor can do for themselves. Determining VaR is much more complicated. In the TradFi space, this is what financial subscription accounts and brokerages are for. In the Wild West of crypto, there are different subscription services that provide information. And, of course, The Art of the Bubble is one such educational service whose charts and signals are based on algorithmic trading.

This article is a part of the Crypto Industry Essentials educational program presented by 1.2 Labs (formerly The Art of the Bubble).

Though this article is credited to me, it contains some written material by Sebastian Purcell, PhD from his 1.2 Labs education series on cryptocurrencies.

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Todd Mei, PhD
1.2 Labs

Director of Research at 1.2 Labs. Former academic philosopher (work, ethics, classical economics).