Tag Archives: myths

10 Python Myths with PayPal Security

10 Python Myths with PayPal Security

Eye popping facts with suggested Python packages. If PayPal chooses this as a secure way to do financial transactions, it needs to be secure for automated trading if you follow these you follow these recommendations

10 Myths of Enterprise Python

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Fintech 5 myths vs an investment bank

Fintech 5 myths vs an investment bank

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5 fintech fears that are pure myth

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The three myths of modern risk management in quant analytics part 2

The three myths of modern risk management in quant analytics part 2

 

Myth 1: Volatility is a measue of risk – Volatility is indeed a “byproduct” of Risk, which means that it manifests itself only after the risk has already occured. Therefore, Volatiliy, like most other quantitative methods based on time series analysis, is a “Measure of Past Risk”.

Myth 2: Correlation is a measure of diversification – Up to a point. Correlation has some serious limitations as a measure of diversification. Correlation is a measure of how much returns of different assets move more or less in the same direction. However, it does NOT imply that highly correlated asset prices move together and vice-versa. Furthermore, one has to be very careful of which time period is used. There are cases where daily returns exhibit low correlations but monthly returns exhibit high correlation. That is why concepts like “co-integration” and “co-movement” have been introduced.

Myth 3: The underlying asset does not matter – This is a true Myth which comes mostly from CAPM. The theory behind CAPM is borderline absurd. It states that the Risk of a stock is a purely statistical function of the stock’s Beta and the Market risk. It totally ignores any reality of the underlying business. Furthermore, measures like Beta are totally unreliable and have a lot of statistical noise, plus they structurally change over time. Example: IBM in the 80s was a very different company than IBM in the 2000s. The idea that IBM was “born” with its own Beta is nonsensical. But then, since Beta changes with time, it is hard if not impossible to measure.

The key point is that Risk and Value are not simple results of some statistical analysis. They require true understanding of economic and business funamentals. The idea that investing can be reduced to running a piece of quantiative software is overly simplistic and certainly over-rated.

This is not to say that good quantitative analysis cannot be useful.

As Ivo says, the rub is in the intuition and art required to chosing the right inputs.

It’s fun to speculate, if Ben Graham were alive and working today, how he might have adapted his ideas about intrinsic value and margin of safety for individual holdings so that they could be used to generate probabilistic real (after inflation) outcomes at long-term (rather than just short-term) horizons for a portfolio combining many individual holdings? And how would he have allowed for individual risk tolerance, or indifference between the risk free asset used to determine the margin of safety and uncertain risky returns? If he couldn’t, would he get published today?

Though he would have unquestionably used his judgement and knowledge of history to try to solve the problem overall, would he have advocated quant techniques to deal with the scale and complexity and required rigour and consistency when continuously generating return probabilities and constructing and rebalancing portfolios or would he have suggested it should all, always, be a matter of judgement?

I would consider Ben Graham approach quite “quantitative”. He just used indicators of risk other than the statistical definitions advocated by CAPM. He also defined “risk tolerance” as the change of making a bad investment, not as the amount of volatility to be experienced.

On the other end, it is also true that CAPM and MPT techniques still require much more “judgement” than commonly believed. In particular, chosing the input assumptions for these models is anything but simple. For example, it is well known that portfolio optimization techniques based on expected returns and correlationas are VERY unstable and sensitive to errors in the inputs [Something that Richard Michaud has explored extensively]. If one considers the challenges in chosing the inputs, CAPM and MPT only offer the “illusion” of rigor, something that is easy to forget until one tries to use these methods in practice.

To be sure, I think that Ben Graham’s approach also has some severe limitations because it tends to focus solely on the bottom-up view.

It is inveitable that one needs to exercise considerable judgement to assess when a certain method, theory and tools may or may not work at any given time.

The problem is that common quant tools and softwares simply assume that the most recent past can be used as input to estimate future probabilities. This approach is effectively blind to changes in market cycles because academics say that in an efficient markets there cannot be cycles or bubbles.

Just a comment on volatility. Historical Vol. is a measure of “past” risk which not necessary gives us any clue of future risk. Implied Vol. is a better measure of “future/market” risk on a particular asset. The relationship of Historical and Implied is a great indicator of what kind of risk the market is pricing on a particular asset. Very seldom HV/IV is close to one. Not only that you can pick IV for different times: week, month, year….
Again we are facing sentiment indicators here, what Mr. Market is Expecting on this particular asset. Usually this is a contrarian indicator, i. e. when HV/IV is too high there is too much complacency; the reverse is also true.
As a personal note, I always run my numbers based on IV because HV alone does not tell me much.

, when I refer to volatility as a concept, I refer to the whole set of
indicators including implied and realized vol. That also includes the whole
vol surface whether estimated (including all mainstream methodologies) or
market-derived. Unfortunately, the whole set of these indicators move in
tandem with a few days gap (and difference in scale). While for trading
purposes it is crucial to follow the vol surface and look for mispricing,
in a large institutional setting implied volatilities are as misleading as
realized vols. In fact, all analysis I carry out is based on the VIX index,
which is an implied volatility measure across all strikes.

Not necessarily true and not necessarily that bad either. There are statistical models and software packages that allow you to incorporate your views into the analysis. The problem is that it is usually very difficult to formulate your views in a manner consistent with the model used. It requires experience with the model and skill.

The funny bit is that your own views about the future are based on past information. This is how our brains work. Your views about the future are based on your past experience and (hopefully) on the experience of others. A mathematical model does exactly the same – it uses past data to “learn”. Its forecasting power depends on the condition that the future is similar to the past and on its ability to quickly “learn” as new information becomes available. The past is never exactly the same as the future but on average it quite frequently is. Statistics is about averages. By the way, the VIX also relies on past data and models to come up with a measure of expected volatility.

I agree with you that volatility is a bad measure of risk, but I have a slightly different argument in addition to the already mentioned. Volatility makes sense as a measure of risk only when put in a probabilistic concept. Using volatility as a risk measure is equivalent to assuming that markets follow the normal distribution, which they don’t do very often. This also means that VaR and volatility are very different concepts.

Correlations are an average measure of codependency. It is weird that people put so much trust in a single number without even considering that this is an estimate, with usually quite wide confidence bounds, prone to outliers, changing over time, etc… And yes being an average it does not tell you much about any particular daily outcome. BTW it also assumes the normal distribution…

An interesting thread…

Yes there are problems with the current methods of risk management, however, until anything else comes along its all we have!

Risk practitioners and investment managers reading too much into the information is the problem, VaR seemed to get very bad press saying it didn’t work, but even saying this highlights that it was already being wrongly utilised. If you were a fundamental analyst would be buy a stock solely based on the PE or P/bk, of course not, so why would you manage risk based on a single number either. One can read far too much into a multitude of numbers, which then asked the questions, how information is this used?

I remember the low vol days before the crisis, we used to run stress/scenario analysis to determine possible large drawdown’s, did that stop managers running portfolios how they wanted… no; did that prior notice of possible meltdown allow managers to position themselves differently… no… we were running those numbers for a long time before the problems.

The main battle of risk management is ensuring effective portfolio construction throughout a full market cycle. Covariance based analysis works well under normal conditions, and inherently most of the time it is normal and risk can effectively budgeted, it’s the times of extreme market conditions that cause issue. Trending using multiple time horizons and models may offer some indicators to impending problematic markets and I believe there is a good link here to using technical indicators.

Practical lessons were learnt, if we want to manage the risk of portfolios we need to get involved; risk management is to constantly challenge about the process of portfolio construction. Lets remind ourselves it was excessive use of leverage that caused the problems, not the misuse of linear regressions

the usefulness of a metric can only be considered in the context of
its application. I agree with you, neither VaR nor variance can be blamed
for anything, since those are abstract statistical constructs and there may
be many harmless ways they can be used. However, in your comment you
highlighted eloquently the crux of the context-driven problem:

“I remember the low vol days before the crisis, we used to run
stress/scenario analysis to determine possible large drawdown’s, did that
stop managers running portfolios how they wanted… no; did that prior
notice of possible meltdown allow managers to position themselves
differently… no… we were running those numbers for a long time before
the problems.”

I suggest that as a portfolio manager I could not have reacted otherwise,
since one could come up with a large drawdown scenario at any point in
time, the question is did you have any indication from the risk tools that
specifically then (in a low vol environment) the large drawdown was more
likely to happen? And when the vol started to climb, was not this
accompanied by the first losses (particularly in the quant funds), i.e. it
was too late! This is not a criticism of linear regressions or VaR as a
statistical measure. What we have is a hazardous application of
inappropriate statistical techniques.

“Yes there are problems with the current methods of risk management,
however, until anything else comes along its all we have!”

There are several alternatives, some of them already deployed in a large
scale with very good results

 

 

 

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Quant opinion: The three myths of modern risk management part 1

Quant opinion: The three myths of modern risk management part 1

Financial institutions in the post-crisis era have found themselves in two camps: those who have already spent an exorbitant amount of cash on state-of-the-art risk systems and those that are going to spend on the systems some time soon. And yet, investors and investment risk professionals have little confidence in the outcome of these efforts. The reason is simple: professionals with over a decade of experience have an intuitive and acute awareness of the three myths of modern risk management.

Myth 1: volatility is a measure of risk. There is plenty of evidence to suggest that our treatment of volatility as a measure of risk was in part responsible for the excessive risk taking in the period of 2005-2007. While the markets continued to appreciate in value, investors kept pouring more money into the same limited set of investments. In practice this meant that buying S&P 500 at 1100 was riskier than buying the same index at 1400. In fact, many institutions did not follow blindly their risk systems in that period, preferring instead to rely on common sense.

Myth 2: correlation is a measure of diversification. Most investors by now have acknowledged that low correlations in calm market conditions exaggerate the benefits of diversification. The reason for this lies in the economics of systemic crises.
Consider a supply chain of automotive companies. Steel makers supply steel to car makers and in a normal economic environment the returns of steel stocks and automotive stocks may diverge based on relative negotiating power between the two groups of companies. Lower steel prices would be good news for the car makers. An investor holding both set of stocks would observe low return correlations. In a crisis environment, however, the whole supply chain struggles with lower volumes and all companies perform equally poorly. So just when the investor needs the benefits of diversification, the portfolio is faced with a single risky exposure.

Myth 3: the underlying asset does not matter. By buying a stock, an investor becomes a shareholder of a company. The investor runs the risk of mismanagement, adverse market for the company’s product, cost overruns, failed product introductions and a multitude of other quantifiable risks. The resulting volatility of the stock price is a mere reflection of true risks after the event. The risk of a major draw down, above all, depends on the price paid for an investment in the first place. All of the above, although intuitive from an experienced investor’s point of view, is ignored by the modern risk management practice.

It is quite understandable then that despite all ongoing efforts to beef up the risk management function, experienced professionals are skeptical. All mainstream risk management systems are based on the three myths. They are useful in evaluating investment risks if everything goes well. And if not, then the experienced professionals will yet again be blamed for inadequate monitoring.

We have been successfully applying an alternative risk management framework which does not use volatilities and correlations as an input. Graham Risk is named after Benjamin Graham, who first suggested a measure of risk based on the difference between the price and the intrinsic value of an asset. The full article is available on http://waronvar.com/?p=156. More about Graham Risk is available on www.waronvar.com

In general, people want to simplify things, it make life easier if a few (one will even better) number can summarize the current situation for us. So we have

 

Point taken – one big issue is oversimplification. But whereas stock
indices are in general a reasonable gauge of what the daily moves of the
stock markets are, volatility indices are not even close in terms of
describing the underlying level of ex ante risk.

 

• I am fully on this side of the war. The general myth is that modern quantitative finanacial analysis, which in the end is 99% about volatilities and correlations, is an effective tool for risk management because it “looks scientific”. The over-quantification of finance has made everyone blind to the “common sense” risks of asset bubbles and improper asset values (in the G-D sense). After all, the academics of Modern Portfolio Theory “deny” that an investor like Graham and Dodd coul even exist!

Volatility and VaR are stictly “backward looking”, so they work as long as the past is what is going to happen in the future. But during bubbles this is a naive assumption. Note that volatility and VaR are typically low during periods of over-optimism just before a bubble bursts. Totally useless.

The one thing to remember is that even market aggregates can become overvalued, so the G-D value principle applies to the market in aggregate as well as for individual companies, a fact missed by some value investors.

I have written a white paper on the subject here (note also my comments on risk and volatility):
http://www.pionline.com/assets/docs/CO72842217.PDF

I am fully supportive of the war against VaR and other nonsensical measures.

Simple is good. Simplistic is not.

 

A good example of volatility not equalling risk is a strategy which involves systematically selling low delta out-of-the-money options. The equity curve will show a smooth move higher month after month, thus volatility is “low”. But clearly this low vol (or high Sharpe) in no way reflects the risk being taken.

I’m with all of you on the limits of modern portfolio and G-D methods. In my experience they work in “normal” markets (1 sigma). In heavy trending markets or bubbles they can cost dearly. If you do not add technicals and sentiment indicators you might be sitting in a one or two legged stool. Remember markets can be irrational longer than you can remain liquid. As examples look at Silver or the Dollar today. Even with this four legged stool you have to be very careful in those heavy trending or bubble markets (lessons of 2008 crash and 2009 rally)

 

The CEO of Deutsche Bank, Josef Ackermann, once made the astute comment that financial analysts overused linear regression at their own risk. A statistics text proved the same point by using linear regression on Galileo’s data from dropping different weights from different heights, getting precise but meaningless results because gravity takes the form of constant acceleration, not constant velocity. Volatility and correlation methods (more precisely, covariance analysis) are overused in finance and fail because i) market probabilities are time dependent and ii) the relationship between standard deviations and percentiles quickly becomes impossible to measure “down the tail” or in the presence of sufficient leverage. The saying “market risk will hurt you but liquidity risk will kill you” has come to supplant the old Keynes: “markets will remain irrational longer than you can remain solvent.”

 

“Overused” for sure and I would add “abused” and “over-hyped”. As soon as I entered the financial industry I spent months and months trying to make sense of returns based risk analysis. In the end, I quickly convinced myself that it was highly useless and potentially misleading. Unfortunately, it is also ubiquitous and pervasive because it provides the illusiong of scientific-looking risk management.

That being said, data can be very helpful to understand what is driving the markets at any given time. Personally, I have found it very useful to combine elements of fundamental as well as technical analysis depending on the situation and especially with a great deal of common sense.

 

Market prices are good indicators of rationally evaluated economic value
Securitised credit has improved allocative efficiency and financial stability
Mathematical analysis can deliver robust quantitative measures of trading risk
Market discipline can be used as an effective tool in constraining harmful risk taking
Financial innovation can be assumed to be beneficial

http://www.fsa.gov.uk/pages/Library/Communication/Speeches/2009/0618_vr.shtml

“Market prices are good indicators of rationally evaluated economic value”

After spending over a decade of hands-on asset valuation experience I tend
to avoid a concept such as rationally evaluated economic value.

“Securitised credit has improved allocative efficiency and financial
stability”

Trying to be unbiased, not sure if there is evidence either way

“Mathematical analysis can deliver robust quantitative measures of trading
risk”

Agree, with a caveat that mathematics is a fantastic tool, but it requires
correct application and the right set of data.

“Market discipline can be used as an effective tool in constraining harmful
risk taking”

Agree

“Financial innovation can be assumed to be beneficial”

Agree. Contrary to the general media bias, I do not hold financial
innovation as such responsible for the crisis. The problem is not that
there is too much innovation. The problem is there is not sufficient
innovation to fully understand and implement the new tools, financial
instruments and systems.

Most basic myth of risk management is that they (financial firm most important owners, their C-level representatives and insiders, installed regulators and politicians/representatives) give a damn about it.—

• I agree with almost all of that. Correlation as a measure of dependence isn’t that bad, but models that suppose correlation is constant under all circumstances are very obvious culprits. For a long time I thought more general modeling of dependence structures (copulas etc.) would be a promising line of research but sooner or later you run into the same problem: you make an assumption about a parameter that governs dependence being constant. Enter massive risk underestimation from stage left.
Linear regression…don’t get me started. I am an econometrician, and a good econometrician spends most of his or her time being extremely paranoid about linear regressions. Sadly, modern software allows everyone to “estimate” linear regression models, without having the slightest understanding of what the results mean or how much they can be trusted. The Galileo example is a classic but there are many other. I vividly remember an article (in the refereed (!) journal “Economics Letters”) which regressed a movie’s box office total on a set of dummies indicating which oscars that movie had won. Surpisingly, winning an oscar for best actress had a NEGATIVE effect on box office total….if you believed the regression result was not fatally flawed because the regressor had failed to do any diagnostic testing.
In the end, linear regression models are little idiot machines that can be very good at extracting the maximum from simple datasets..if the practitioner knows what he or she is doing.

The following are the replies ::-

Myth 1: Volatility is a measure of risk – According to me Volatility is not the measure of Risk infact Volatility is the byproduct of Risk . Whenever we are having Risk in the Financial markets then Volatility originates but the same Financial world have created few models like VAR to compute the impact of the volatility but the real truth is ” Volatility is byproduct of Risk ”

Myth 2 : Correlation is a measure of diversification :- I agree with the fact that low the level of correlation the better the diversification you are having . Correlation is something that matches your returns with an Index but at the same time if you are having 100% correlated than there is low level of Diversification you are having in your portfolio.

Myth 3 : The underlying asset does not matter :- I may agree because Underlying matter and at the same time you have to have backup plan as well…., like in the case of Options underlying matters but we are having backup as well …., My answer can change in case i am having more scenarios.

I hope we can have more discussions on the same topic … ,

 

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