# Value at risk (VAR): Would you buy a car with a faulty airbag!

Published: June 27, 2015 at 10:07 am

Last Updated on

Value At Risk (VAR) is a risk measure used to determine the probability of a certain percentage loss (or gain!) in the value of a security based on historical data.  Although widely used by risk managers to determine worst case scenarios and handle the risk a firm can take (in a bid to earn more!), it is deeply flawed.

Not because it uses historical data to calculate future risk, but because it assumes that the historical returns (daily. monthly etc.) fall under a normal distribution or bell curve – the one used to fix employee appraisals (another flawed application!).

It is widely documented that VAR models is one of the reasons for why, and the manner in which the 2008 crash  occurred. Watch the movie margin call for a non-technical account or get hold of the book, Alchemists of Loss: How Modern Finance and Government Intervention crashed the financial system!

The title of this post (written by a student of the subject) is a reference to US hedge fund manager, David Einhorn’s quote that VAR is “an airbag that works all the time, except when you have a car accident.”

Technically, it does not work at any time.

I do not wish to get into the details of the VAR model only to later show that it is flawed. Instead, using a simple measure called the SKEW, will demonstrate why using the VAR is incorrect. Alternatives to the VAR are not intuitive but that is no excuse for using a faulty model.

This is a how a textbook handles this issue:

“The most questionable assumption, however, is that of normality because evidence shows that most securities prices are not normally distributed. Despite this, the assumption that continuously compounded returns are normally distributed is, in fact, a standard assumption in finance”.

Terrific! The ‘standards’were set by the alchemists of loss!

The x-axis will be monthly or daily stock returns and the y-axis the corresponding probability of occurrence.  So VAR can be used to calculate the probability associated with a range of returns.

(1) The peak corresponds to the average return. Notice that the data is distributed symmetrically on either side of the peak. That is the SKEW of a normal distribution is zero

(2) Also, notice how smoothly and rapidly the probability falls off from the peak. Extreme events are extremely rare! Meaning, a Harshad Mehta scam which saw the Sensex shoot up by 270% or the 2008 crash would have such a low probability of occurring that you can safely discount them. Yet as B Mandelbrot writes in his book – the misbehaviour of markets – such events are common enough.

These two are the hallmarks of a normal distribution.  Stock price returns look nothing like this!

Mandelbrot showed the even over a period of several decades, price movements do not follow a bell curve. That his contributions were largely ignored and he was sidelined by the proponents of the modern financial theory*  is another story!

(*) also depends on the normal distribution which makes mutual fund star ratings questionable!

Here is the skew observed in the daily and monthly returns of a few stocks (15 year history).  For an ideal normal distribution, the value should be zero. Even small departures from zero can result in bad predictions.

It would be foolhardy to bet that these distributions can be approximated as normal and use the VAR model.

Have a look at the monthly returns distribution of Manali Petro Chem.

Notice that asymmetry (non-zero skew) and the so-called ‘fat tail’ regions, marked by arrows.

Normal distributions do not behave like that!

As Ramesh Mangal once told me, why choose a model that is exactly wrong? In order to assess volatility, we need to use models that do not depend on the normal distribution assumption.

The upside/downside calculator and geometric information ratio calculator for mutual funds are some of my baby steps in that direction.

What do you think? Would you buy a car with a faulty airbag because it looks elegant?

M. Pattabiraman(PhD) is the founder, managing editor and primary author of freefincal. He is an associate professor at the Indian Institute of Technology, Madras. since Aug 2006. Connect with him via Twitter or Linkedin Pattabiraman has co-authored two print-books, You can be rich too with goal-based investing (CNBC TV18) and Gamechanger and seven other free e-books on various topics of money management. He is a patron and co-founder of “Fee-only India” an organisation to promote unbiased, commission-free investment advice.
He conducts free money management sessions for corporates and associations on the basis of money management. Previous engagements include World Bank, RBI, BHEL, Asian Paints, Cognizant, Madras Atomic Power Station, Honeywell, Tamil Nadu Investors Association. For speaking engagements write to pattu [at] freefincal [dot] com

#### About freefincal & its content policy

Freefincal is a News Media Organization dedicated to providing original analysis, reports, reviews and insights on developments in mutual funds, stocks, investing, retirement and personal finance. We do so without conflict of interest and bias. We operate in a non-profit manner. All revenue is used only for expenses and for the future growth of the site. Follow us on Google News
Freefincal serves more than one million readers a year (2.5 million page views) with articles based only on factual information and detailed analysis by its authors. All statements made will be verified from credible and knowledgeable sources before publication.Freefincal does not publish any kind of paid articles, promotions or PR, satire or opinions without data. All opinions presented will only be inferences backed by verifiable, reproducible evidence/data. Contact information: letters {at} freefincal {dot} com (sponsored posts or paid collaborations will not be entertained)

### Our Publications

#### You Can Be Rich Too with Goal-Based Investing

This book is meant to help you ask the right questions, seek the right answers and since it comes with nine online calculators, you can also create custom solutions for your lifestyle! Get it now. It is also available in Kindle format.

#### Gamechanger: Forget Startups, Join Corporate & Still Live the Rich Life You Want

This book is meant for young earners to get their basics right from day one! It will also help you travel to exotic places at low cost! Get it or gift it to a young earner

#### Your Ultimate Guide to Travel

This is a deep dive analysis into vacation planning, finding cheap flights, budget accommodation, what to do when traveling, how traveling slowly is better financially and psychologically with links to the web pages and hand-holding at every step. Get the pdf for Rs 199 (instant download)

### Comment Policy

Your thoughts are the driving force behind our work. We welcome criticism and differing opinions.Please do not include hyperlinks or email ids in the comment body. Such comments will be moderated and I reserve the right to delete the entire comment or remove the links before approving them.

1. Shan Rajasekaran says:

Excellent article Pattu Sir.

As George E. P. Box said “Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful.” Various probability distributions – like random, normal, exponential – all have their use. But when people use it without understanding their predictive abilities and limitations, they cause much damage. In that sense the normal distribution must be one of the most abused mechanised models of thinking.