Value at Risk 101
In any financial meltdown, you tend to hear the term "value at risk" a lot in the aftermath of the destruction. "But our value at risk models said..." becomes a common refrain.
So what is Value at Risk and how does it work?
Here's Value at Risk 101!
👇👇👇
In any financial meltdown, you tend to hear the term "value at risk" a lot in the aftermath of the destruction. "But our value at risk models said..." becomes a common refrain.
So what is Value at Risk and how does it work?
Here's Value at Risk 101!
👇👇👇
1/ First, a few definitions.
Value at Risk, or "VAR" for short, is a statistic that aims to quantify the level of financial risk within a firm, portfolio, or position in a specific time interval.
It is comprised of a time period, a confidence level, and a loss amount.
Value at Risk, or "VAR" for short, is a statistic that aims to quantify the level of financial risk within a firm, portfolio, or position in a specific time interval.
It is comprised of a time period, a confidence level, and a loss amount.
2/ Its intended use is in managing risk. It provides a single metric to "bound" the potential losses of a portfolio or position.
Commercial banks, investment banks, and institutional investors are frequent users of VAR.
Let's look at how it is calculated and where it fails.
Commercial banks, investment banks, and institutional investors are frequent users of VAR.
Let's look at how it is calculated and where it fails.
3/ There are three primary ways VAR is calculated:
(1) Historical - uses historical outcomes to predict future volatility.
(2) Variance-Covariance - uses a normal distribution to predict future returns
(3) Monte Carlo - uses a Monte Carlo simulation model to predict outcomes.
(1) Historical - uses historical outcomes to predict future volatility.
(2) Variance-Covariance - uses a normal distribution to predict future returns
(3) Monte Carlo - uses a Monte Carlo simulation model to predict outcomes.
4/ There are also real deficiencies with each:
(1) Historical - assumes past performance is an indication of future performance.
(2) Variance-Covariance - assumes future returns are normally distributed.
(3) Monte Carlo - assumes accuracy derived from brute force modeling.
(1) Historical - assumes past performance is an indication of future performance.
(2) Variance-Covariance - assumes future returns are normally distributed.
(3) Monte Carlo - assumes accuracy derived from brute force modeling.
5/ The ultimate output of each of these calculation methodologies is to make the following statement:
"I am [X%] confident that our portfolio/position will not lose more than [Y%] during [set period of time]."
As a risk manager, this talking point will keep your bosses happy.
"I am [X%] confident that our portfolio/position will not lose more than [Y%] during [set period of time]."
As a risk manager, this talking point will keep your bosses happy.
6/ While VAR may provide risk managers with a nifty, single metric for quantifying risk, it has serious drawbacks with meaningful consequences.
First, methodologies using historical returns can be easily manipulated by cherry-picking historical periods.
This is manageable.
First, methodologies using historical returns can be easily manipulated by cherry-picking historical periods.
This is manageable.
7/ The bigger issue, which @nntaleb is clear in pointing out in several of his famous books, is VAR misses the mark on accurately predicting the likelihood and impact of tail-risk events.
We systematically underestimate them. Events are unprecedented, until they aren't.
We systematically underestimate them. Events are unprecedented, until they aren't.
8/ In 2008, we saw this deficiency in action.
The VAR calculations at major banks failed to capture the true risks of the portfolios of subprime mortgages held by many financial institutions.
This led to the near-collapse of the global financial system. amzn.to/36AMBbR
The VAR calculations at major banks failed to capture the true risks of the portfolios of subprime mortgages held by many financial institutions.
This led to the near-collapse of the global financial system. amzn.to/36AMBbR
9/ Long Term Capital Management, a hedge fund managed by geniuses (seriously, they had two Nobel Prize winners), collapsed in 1998 when events outside the bounds of their VAR modeling crashed their fund.
It nearly took down the financial system with it. amzn.to/2ESIs7y
It nearly took down the financial system with it. amzn.to/2ESIs7y
10/ So while the idea of a quantitative measure of risk is not a bad one, in practice, VAR has real flaws that may diminish its effectiveness.
As VAR has been used in the past to justify risk-taking that had negative cascade effects through the system, it may require a rethink.
As VAR has been used in the past to justify risk-taking that had negative cascade effects through the system, it may require a rethink.
11/ For more on the topic of VAR, its pitfalls, and the role of randomness in life, I highly recommend reading The Black Swan and Fooled by Randomness by @nntaleb. Honestly, just read anything by him! Foundational classics.
amzn.to/36nd501
amzn.to/2SiiTQe
amzn.to/36nd501
amzn.to/2SiiTQe
12/ And for more educational threads on money, finance, and economics, check out my meta-thread below!
https://twitter.com/SahilBloom/status/1284583099775324161
• • •
Missing some Tweet in this thread? You can try to
force a refresh
