## Risk and data

A while back a group of <a large number of scientists> wrote an open letter to the Prime Minister demanding greater data sharing with them. I must say that the letter is written in academic language and the effort to understand it was too much, but in the interest of fairness I’ll put a screenshot that was posted on twitter here.

I don’t know about this clinical and academic data. However, the holding back of one kind of data, in my opinion, has massively (and negatively) impacted people’s mental health and risk calculations.

This is data on mortality and risk. The kind of questions that I expect government data to have answered was:

1. If I get covid-19 (now in the second wave), what is the likelihood that I will die?
2. If my oxygen level drops to 90 (>= 94 is “normal”), what is the likelihood that I will die?
3. If I go to hospital, what is the likelihood I will die?
4. If I go to ICU what is the likelihood I will die?
5. What is the likelihood of a teenager who contracts the virus (and is otherwise in good health) dying of the virus?

And so on. Simple risk-based questions whose answers can help people calibrate their lives and take calculated enough risks to get on with it without putting themselves and their loved ones at risk.

Instead, what we find from official sources are nothing but aggregates. Total numbers of people infected, dead, recovered and so on. And it is impossible to infer answers to the “risk questions” based no that.

And who fill in the gaps? Media of course.

I must have discussed “spectacularness bias” on this blog several times before. Basically the idea is that for something to be news, it needs to carry information. And an event carries information if it occurs despite having a low prior probability (or not occurring despite a high prior probability). As I put it in my lectures, “‘dog bites man’ is not news. ‘man bits dog’ is news”.

So when we rely on media reports to fill in our gaps in our risk systems, we end up taking all the wrong kinds of lessons. We learn that one seventeen year old boy died of covid despite being otherwise healthy. In the absence of other information, we assume that teenagers are under grave risk from the disease.

Similarly, cases of children looking for ICU beds get forwarded far more than cases of old people looking for ICU beds. In the absence of risk information, we assume that the situation must be grave among children.

Old people dying from covid goes unreported (unless the person was famous in some way or the other), since the information content in that is low. Young people dying gets amplified.

Based on all the reports that we see in the papers and other media (including social media), we get an entirely warped sense of what the risk profile of the disease is. And panic. When we panic, our health gets worse.

Oh, and I haven’t even spoken about bad risk reporting in the media. I saw a report in the Times of India this morning (unable to find a link to it) that said that “young are facing higher mortality in this wave”. Basically the story said that people under 60 account for a far higher proportion of deaths in the second wave than in the first.

Now there are two problems with that story.

1. A large proportion of over 60s in India are vaccinated, so mortality is likely to be lower in this cohort.
2. What we need is the likelihood of a person under 60 dying upon contracting covid. NOT the proportion of deaths accounted for by under 60s. This is the classic “averaging along the wrong axis” that they unleash upon you in the first test of any statistics course.

Anyway, so what kind of data would have helped?

1. Age profile of people testing positive, preferably state wise (any finer will be noise)
2. Age profile of people dying of covid-19, again state wise

I’m sure the government collects this data. Just that they’re not used to releasing this kind fo data, so we’re not getting it. And so we have to rely on the media and its spectacularness bias to get our information. And so we panic.

PS: By no means am I stating that covid-19 is not a risk. All I am stating is that the information we have been given doesn’t help us make good risk decisions

## Monetising volatility

I’m catching up on old newsletters now – a combination of job and taking my email off what is now my daughter’s iPad means I have a considerable backlog – and I found this gem in Matt Levine’s newsletter from two weeks back  (\$; Bloomberg).

“it comes from monetizing volatility, that great yet under-appreciated resource.”

He is talking about equity derivatives, and says that this is “not such a good explanation”. While it may not be such a good explanation when it comes to equity derivatives itself, I think it has tremendous potential outside of finance.

I’m reminded of the first time I was working in the logistics industry (back in 2007). I had what I had thought was a stellar idea, which was basically based on monetising volatility, but given that I was in a company full of logistics and technology and operations research people, and no other derivatives people, I had a hard time convincing anyone of that idea.

My way of “monetising volatility” was rather simple – charge people cancellation fees. In the part of the logistics industry I was working in back then, this was (surprisingly, to me) a particularly novel idea. So how does cancellation fees equate to monetising volatility?

Again it’s due to “unbundling”. Let’s say you purchase a train ticket using advance reservation. You are basically buying two things – the OPTION to travel on that particular day using that particular train, sitting on that particular seat, and the cost of the travel itself.

The genius of the airline industry following the deregulation in the US in the 1980s was that these two costs could be separated. The genius was that charging separately for the travel itself and the option to travel, you can offer the travel itself at a much lower price. Think of the cancellation charge as as the “option premium” for exercising the option to travel.

And you can come up with options with different strike prices, and depending upon the strike price, the value of the option itself changes. Since it is the option to travel, it is like a call option, and so higher the strike price (the price you pay for the travel itself), the lower the price of the option.

This way, you can come up with a repertoire of strike-option combinations – the more you’re willing to pay for cancellation (option premium), the lower the price of the travel itself will be. This is why, for example, the cheapest airline tickets are those that come with close to zero refund on cancellation (though I’ve argued that bringing refunds all the way to zero is not a good idea).

Since there is uncertainty in whether you can travel at all (there are zillions of reasons why you might want to “cancel tickets”), this is basically about monetising this uncertainty or (in finance terms) “monetising volatility”. Rather than the old (regulated) world where cancellation fees were low and travel charges were high (option itself was not monetised), monetising the options (which is basically a price on volatility) meant that airlines could make more money, AND customers could travel cheaper.

It’s like money was being created out of thin air. And that was because we monetised volatility.

I had the same idea for another part of the business, but unfortunately we couldn’t monetise that. My idea was simple – if you charge cancellation fees, our demand will become more predictable (since people won’t chumma book), and this means we will be able to offer a discount. And offering a discount would mean more people would buy this more predictable demand, and in the immortal jargon of Silicon Valley, “a flywheel would be set in motion”.

The idea didn’t fly. Maybe I was too junior. Maybe people were suspicious of my brief background in banking. Maybe most people around me had “too much domain knowledge”. So the idea of charging for cancellation in an industry that traditionally didn’t charge for cancellation didn’t fly at all.

Anyway all of that is history.

Now that I’m back in the industry, it remains to be seen if I can come up with such “brilliant” ideas again.

## Optimal risk sharing

The wife moved to Ann Arbor over the weekend, where she will be spending three months. She took an Air France flight (AF191) in the wee hours of Sunday morning, and then switched to a Delta flight at the legendary Charles de Gaulle. I must mention upfront that she seems to have had a peaceful journey.

Except that people following the same schedule exactly twenty four hours earlier would not have. AF191 that departed from Bangalore i n the wee hours of Saturday morning returned to Bangalore after a bomb scare. The flight was subsequently cancelled.

There are many risks to flying. Schedules nowadays are packed so closely that your flight might be delayed. Occasionally it might be cancelled even, sometimes without a good reason. A delay might sometimes mean that you miss your connecting flight.

The question is who bears the risk on this one. If I’m booked on a flight that gets cancelled or delayed (because of which I miss my connection), whose responsibility is it that I’m transported to my destination? There are three possibilities – the passenger himself, the airline and an external insurer. The question is which of these is most optimal.

The traditional model in aviation as I understand it is that it is the airline’s responsibility. While this makes sense because a large number of delays/cancellations are on account of faults on account of the airline, even when the delay is not due to the airline’s fault, the airline is best placed in terms of mitigating the risk.

Leaving the risk on the passenger has the advantage that he can choose his own risk profile. If you are flexible about your trip, you might choose to go without insurance, and take the hit yourself. If you’re a frequent flyer, then the “insurance cost” thus saved will compensate for the occasional delay. Yet, the problem with this kind of a model is that people tend to underestimate the risks, and will more often than not not insure, and get hit badly when the delay happens.

Which brings us to the final absorber of risk – the insurance company. I’d purchased “travel insurance” for a recent trip, and there was a component on account of delayed or missed flights. If my flight was delayed by a certain amount of time, my insurer would pay me a fixed amount of money.

While this financial hedging is good, it may not adequately represent the costs of making a new booking (including the hassles) when my flight is delayed or cancelled. So this is not a workable solution at scale.

Another solution is for the insurer to guarantee that you will reach your destination by a certain time in case your flight gets delayed or cancelled. This might work out to be more expensive than a fixed cash payout but this removes the cost and hassle of figuring out the next best alternative on the part of the customer. The problem, however, is correlation. Insurance works when people’s risks are uncorrelated or negatively correlated. Here they are positively correlated – all passengers on Saturday’s AF191 to Paris were affected similarly, and this pushes up the cost for the insurer to rebook people.

Unless they tie up with the airline itself! If they reach an agreement with the airline such that the airline commits to transport the stranded passengers, then this “positive correlation” I mentioned earlier will be taken care of. Seems workable, right? Except that what is being insured here is the risk that the airline abandoned in favour of the passenger, who insured against it from an insurer, who reinsured it with the airliner! Can we just cut out the middle men?

From this rather unscientific argument above, it looks like airlines are best placed to insure passengers against disrupted flight schedules. Back in the days of regulated air fares where competition had to be “on service”, airlines would take responsibility. This might have disappeared with the move towards unbundling over the last 2-3 decades. For good reason – insuring a schedule results in an additional (albeit hidden) cost, and getting rid of it can result in cheaper (base) fares.

Yet, given that airlines are best placed to insure schedules, we need a solution. Maybe they can charge a premium for insuring schedules apart from the base fares? Or would they argue that the current “unrestricted fares” are such insured fares (implying the premium is rather high)?

Short of  government mandated regulation, what is the best way for allocating the risk of disrupted flight schedules, and pricing it appropriately?

Tailpiece: A decade ago, our valuation professor (at IIM Bangalore) had told us that “risk cannot be eliminated. It can only be mitigaged by selling it to someone who can handle it better”.

## 27% and building narratives using numbers

Some numbers scare you. Some numbers look so unreasonably large that it seems daunting to you, infeasible even. Other numbers, when wrapped in the right kind of narrative, seem so unreasonably small that they sway you (the Rs. 32 per person per day poverty line comes to mind). Thus, when you are dealing with numbers that intuitively look very large or very small, it is important that you build the right narrative around them. Wrap them well so that it doesn’t scare or haunt people. As the old Mirinda Lime ad used to say, “zor ka jhatka.. dheere se lage..”.

So the number in the headline of this blog post is the proposed rate of the Goods and Service Tax. While it is the revenue-neutral amount that needs to be charge should excise and sales and other taxes go, the number looks stupendously large. The way this number was reported on the front pages of business newspapers this morning, it looks so large and out of whack that people might decide that it is better to not have a GST at all.

I’m not blaming the papers for this – they have reported what they’ve been told. It is a question of building narratives by the government. The government, and the GST sub-panel, has done a lousy job of communicating this number, and guiding how it needs to be reported in the media. It is almost as if the way the number was reported is an attempt to further delay the implementation of the GST.

The GST is too important a piece of legislation to be derailed by bad narratives. The government must make every attempt to build a narrative that shows the GST as being conducive to people and to businesses, to show how the transaction costs it reduces will result in better prices for both consumers and businesses, and why it makes lives better. Reporting numbers that look really large doesn’t help matters.

Also, the quant in me is disappointed to see one precise number being put out as the “revenue neutral rate”. Since different goods and services which are now being taxed at differential rates are going to be brought into this one umbrella rate, the real revenue neutral rate is actually a function of the mix of the contribution of each of these goods and services to the GDP. Given that in a dynamic economy these rates are constantly changing, reporting one revenue neutral rate simply doesn’t make sense. A range would be a better way of going about it.

Related to this, given that the revenue neutral rate is a function of mix of goods and services, and this mix will change over time, the assumptions and forecasts that need to be taken into account in the process of fixing the rate are important. The GST panel would do well to take into account the risk of product-and-service mix changing that can make all calculations go awry!

PS: If only they were to hire me as a consultant to this panel 😛

## Sigma and normal distributions

I’m in my way to the Bangalore airport now, north of hebbal flyover. It’s raining like crazy again today – the second time in a week it’s raining so bad.

I instinctively thought “today is an N sigma day in terms of rain in Bangalore” (where N is a large number). Then I immediately realized that such a statement would make sense only if rainfall in Bangalore were to follow a normal distribution!

When people normally say something is an N sigma event what they’re really trying to convey is that it is a very improbable event and the N is a measure of this improbability. The relationship between N and the improbability implied is given by the shape of the normal curve.

However when a quantity follow a distribution other than normal the relationship between the mean and standard deviation (sigma) and the implied probability breaks down and the number of sigmas will mean something totally different in terms of the implied improbability.

It is good practice, thus, to stop talking in terms of sigma and talk in terms of of odds. It’s better to say “a one in forty event” rather than saying “two sigma event” (I’m assuming a one tailed normal distribution here).

The broader point is that the normal distribution is too ingrained in people’s minds which leads then to assume all quantities follow a normal distribution – which is dangerous and needs to be discouraged strongly.

In this direction any small measure – like talking odds rather than in terms of sigma – will go a long way!

## Volleyball

It’s been over eight years since I last played the game, but if I were to pick one outdoor game in which I’m best at (relative to other games I’ve played) it’s volleyball. And when I say I’m best at that, it’s on a strict relative basis – in undergrad, I struggled to get into my hostel team (let alone college team). It just goes to show how bad I’ve been in other outdoor games! I’m a successful cricket and football-watcher, though!

The thing with volleyball is that my game runs counter to how i play other games, and my life in general. In general, I’m an extremely high-risk person – I’m not into adventure sports, though, but have a Royal Enfield motorcycle – I take chances where possible and go for the spectacular. It is hard for me to be “accurate” and “correct”, and given that I know that I’m prone to making mistakes I try to maximize the outputs from the times when I don’t make mistakes, and thus go on a high risk path.

So I’ve quit my job without something else in hand four times, now freelance as a management consultant, blog about every damn thing – things that have promises of big upsides, but also risks of downsides. It also reflects in how I sometimes talk to people – I sometimes try too hard to make an impression – which can potentially get me big returns, but end up saying something stupid at times, and end up sounding arrogant at other times. Those are risks I willingly take.

And this risky nature has reflected in most games I’ve played, also – again nothing in the recent past. In chess, I get bored of slow technical Carlsen-esque positions, and am prone to go on Morphy-esque attacks that can backfire spectacularly. Playing bridge, I finesse way more than I’m supposed to – making some otherwise unmakeable contracts, but going down in contracts I should have otherwise made.

Back in school, when we played cricket with rubber and tennis balls, I would bowl leg spin, and using a light bat, would try to hit every ball for four or six, rather than trying to bat steadily. And while playing basketball (my “second best” outdoor game, after volleyball) I have a propensity to go for long shots.

What sets volleyball apart is that my game completely runs counter to who I am. In volleyball I’m a solid player – don’t spike too much (can’t jump!!), but can set spikes well, block well and can lead a team well from the back line. In fact, my best volleyball games have been those when the team has had to carry some weak links, and I’ve led from the centre of the back line, lending solidity and helping build up attacks. It definitely doesn’t reflect what I’m like otherwise.

But volleyball has also been the game where I’ve had a large number of spectacular failures. At every level I’ve played, I’ve had some responsibility thrust upon me, and I’ve buckled under the pressure. It’s volleyball that comes to mind every time I let down people’s trust because I do badly a something I’m supposed to be good at.

1. Voyagers versus pioneers, 1999: This was the school inter-house tournament. We go two sets up. They win the next two. Down to the decider. We lead 14-13, and its our turn to serve. Our captain purposely messes up our rotation such that I can serve (I had a big serve – one attacking aspect of my volleyball). The serve clips the net on its way across (back then, a let was a foul serve in volleyball). We lose.

2. NPS Indiranagar versus NPS Rajajinagar, 1999: Then I get selected to represent my school. I’m on the bench, and am subbed in right on time to serve. I decide to warm up with an underarm serve (before I start unleashing my overarm thunders). Hit it into the net. Opponent’s serve comes to me and I receive it badly. Get subbed out.

3. G block versus F block, 2004-05: Semi finals of the IIMB inter-hostel championship. We have two big spikers, two decent lifters and defenders (including me) and two who had never played volleyball in their lives, but were chosen on the basis of their physical fitness alone. Down to third set (best of three). We lead 25-24 (new scoring system). I’m playing right forward. Ball comes across the net. All I need to do is to set it up for a big spike, but I decide to spike it directly myself. And miss. Then I serve on the next match point. Decide to go for a safe serve, gets returned. We lose.

4. Section C versus Section A, 2004-05: Again similar story. I don’t remember the specifics of this, but again it was heartbreak, and I think I missed my serve on match point.

I guess you get the drift..

## Pricing railway safety

Yet another railway accident has happened. As someone on twitter pointed out,

The problem with the Indian Railways is that there is no real measure of safety. How do we know how much safer the trains and tracks are compared to last year? Given the way the Railway finances are put out currently, there is no way to figure this out. Without the railways putting out more disclosures, is there a way to put a number on how safe the Indian Railways are? In other words, is there a way to “price” railway safety?

As you are well aware, and as the above tweet points out, it is standard practice in Indian Railway accidents for the Railway Minister to announce an ex-gratia payment to the families of the dead and the injured in case of any accident. I’m not sure if there is a formula to this but one cannot rule out the arbitrariness of this amount. As I had pointed out in an earlier post on RQ, accident compensation needs to be predictable and automatic. Can we use this to price railway safety?

First of all, we need to point out that the railways follows a cash accounting system, and thus doesn’t need to account for any contingent liabilities such as ex-gratia payment (last weekend I sat through an awesome lecture by Prof. Mukul Asher (councillor to Takshashila) on public finances, and he pointed this out). Hence, it would be prudent on behalf of the Indian Railways to hedge out this contingent liability.

How do you hedge a contingent liability? By buying insurance! What the Indian Railways needs to do is to buy group accident insurance – all the ex-gratia payments will then by paid out by the insurance company, and the railways will only pay a premium to these companies, thus hedging out the risk! And this process will help put a price on railway safety!

How is that? Let us say that given the railways’ bad record in safety, and its continued promises that safety will be improved each year, the railways decides to take up group accident insurance on an annual basis. Let us say that there is a competitive bidding process among general insurers in India (both public and private sector) to provide this insurance (railways is a large organization, and insuring them will be a matter of prestige, so companies will bid for it). The premium as determined by this competitive bidding process is the price of railway safety!

We can do better – instead of buying one overall policy, the Railways can think of insuring different routes separately, or perhaps zones. This will help put a price on the safety of each route or zone! There will be some transaction cost, of course, but price discovery will happen, and we will be able to put a price on risk!

But then, this is all wishful thinking. It is unlikely this will happen because:

1. Given the cash accounting system followed by the railways, there is no incentive to hedge contingent liabilities
2. Buying insurance means increasing scrutiny. The railways will not want to be scrutinized too hard. It is currently an opaque organization and it will want to be that way.
3. Given the railways are wholly government owned and there are government owned general insurers, there might be some collusion which might  result in underpricing the risk.
And so forth…

Nevertheless, the point of this post is that it is possible to put a price on safety!

## Home Equity

I’m looking to purchase a house. However, the amount of cash I have with me will not suffice to completely fund the house. Given that I’m confident of earning that difference amount in the future means that some bank will give me a mortgage, and I will thus finance my house with debt. Question is why I can’t finance the house with equity instead.

Let’s say the house I want to buy costs Rs. 1 Crore and I have with me Rs. 50 lakh. Instead of taking a loan for the balance Rs. 50 lakh, why can’t I sell equity instead? A consortium of investors can be invited to invest the balance Rs. 50 lakh in exchange for a 50% stake in the house. Rather, we set up a company that owns my house of which I own 50%, and every month I pay a rent to this company. As and when I get additional funds I start buying up additional shares in the company that owns my home and soon I’ll own it completely.

So who will be these people that will invest the balance 50% in my house? They are going to be dedicated real estate investment funds and their business will be to invest in minority stakes in properties of different sizes and in different parts of the town and country. This they are going to fund via a bunch of funds that allow ordinary investors to take exposure to real estate.

Currently there is no way I can invest in real estate except for taking on a large mortgage and purchasing a whole house. If I’m saving up money to buy a house some day and want to invest it in a way that will help me partially hedge against increase in real estate prices (something that I’m unable to do today) I simply buy units in one of these real estate funds. On the other hand, if I sense there might be some problems with my property (let’s say it is ripe for acquisition by the government for some road widening purpose, let’s say) I can sell some part of it to some of these real estate firms, thus reducing my risk of ownership.

These real estate funds can offer a variety of funds that invest in different kinds of properties in different proportions (like you can have a fund that invests 50% of its money in housing, 30% in commercial real estate and 10% in farmland, say). This allows ordinary investors to get exposure to real estate without any large down payments or mortgages. And reduce the risk of owning property in a particular place (let’s say I’m concerned that property prices in Bangalore might fall while those in tier 2 cities might go up. I will simply sell stock in my Bangalore house and invest the money in a fund that invests in houses in tier 2 cities, thus hedging myself).

Why is such a structure not popular already? In fact, I don’t think you have such structures anywhere in the world. One problem in India is the massive transaction taxes on real estate which makes the market illiquid. If that goes, is there anything that prevents us into getting into a culture of home equity?

## Corporate Culture

In good times, when you like the core aspects of your job, you don’t really care about your “organizational culture”. You don’t care so much about how they treat you, about how they make you feel. All you care about is that you are enjoying your time there, that you think there’s some value that the job is adding to your life, and you are happy receiving your salary.

When your organization’s “culture” starts mattering is when things aren’t going all that well in your job. It’s when you stop liking the core aspects of your job, and start wondering why you’re doing what you’re doing. That’s the time when all the “cultural” and “feel good” things about your job that come to the fore. That’s the time when any problems that you have with the organizational culture get highlighted, and you start focusing more on that and less on your work (after all, you’re trying to think whether there’s a reason apart from your core work for you to stay in the job).

As an employer, the risk with not paying attention to your organization’s culture is that when one of your employees doesn’t feel that good about his/her job (and this is bound to happen; irrespective of how much one loves his job, one is bound to go through these cycles), if he realizes that he doesn’t like the culture of your organization, it is that much more easier for him to get extremely disgruntled, and think of deserting ship. By maintaining a great organizational culture, on the other hand, even when someone is going through the troughs (in terms of core work), there is value that they see in sticking on to job, and living to see another day in the job, when (hopefully) the cycle would’ve been reversed.

As a prospective employee, if you see a high degree of attrition in a prospective employer, think twice before joining even if the core nature of work really appeals to you. For, the attrition indicates something is possibly wrong with the culture of the place, and that sooner or later that is bound to bite you.

## S&P’s Responsibilities

Reading through some of the reactions from “experts” to the S&P’s downgrade of US debt, I see words such as “irresponsible”, “misguided” and “inappropriate” being bandied around. These experts seem to be of the view that in view of all that the US is already going through (given the debt crisis et al) it was not correct for the S&P to push it further down into the abyss by downgrading its debt.

Now, the S&P is a rating agency. Its job is to rate debt, categorizing it in terms of how likely an issuer is to honour the debt it issues. It is a privately held firm and it is not the job of the S&P to prevent global crises and save the world. In this case, the S&P has just done its job. And having been following the crisis for a while I’m of the opinion that it’s done the right thing (check Felix Salmon’s article on this; he says the downgrade is more due to the risk of the US’s willingness to not default, rather than its ability; given that there is no permanent solution yet to the debt ceiling and it issues all debt in its native currency).

If a simple move like this by a private company is going to bring down the world, it is because of screwed up regulations (read Basel 2 and Basel 3) that ended up giving way too much importance to firms such as this. And I’m sure the US had adequate representation at that meeting in Basel where the accord was adopted, so it can be partially held responsible for the enormous power that rating agencies currently wield.

The bottom line is that excessive regulations based on dodgy parameters have been responsible for a lot of the mess that we see today. #thatzwhy we need strong regulations.