RK's Musings

Montecarlo and Trading

Simulator should be ONE of the tools , a trader uses regularly, especially a newbie.Why ?

Here is a nice argument FOR it - The Talent Code Revisted

Concise Intro to Econometrics : Summary

The only reason I chose to read this book on a weekend was to verify the null hypothesis of the author, " an intuitive guide" :) Well, intuition and econometrics are kind of animals that don't sleep together that well in the SAME book. Either econometrics books are very math/probability oriented OR they are "stats for dummies" types. I had tons of other things to do on this weekend, but took a peek in to this ~100 page double line spacing book,just out of curiosity.

Points that grabbed my attention, not becoz of the math , but becoz of the nice angrezi that was used :)

• Model building means moving from a unconditional expectation to conditional expectation.
• Expected value of a Unbiased estimator equals the parameter to be estimated, irrespective of the number of trials
• While unbiased is difficult to verify in most situations, Consistent estimator is what is generally needed
• Examining the asymptotic behavior of the estimate is a clue to its consistency
• OLS regression method essentially minimizes the variance of the error term by choosing the appropriate parameters for the linear model
• Don't ask whether random walk fits the data or not ? Instead ask , "How can I beat the random walk ?"
• "Are mid-sized cars equally expensive in Japan and in the US, after correction for the exchange rate between these two countries ?" IS FAR BETTER A QUESTION than "Does the purchasing power parity hold good for Japan and USA? "
• Economic theories deal with aggregated quantities..Empirical work needs fine grained data
• 7 case studies to illustrate various models
  1. convergence of countries attributes - a Convergence model with no stochastic behavior
  2. Direct mailing problem - probit model for selecting customers for direct mail (a truncated regression model )
  3. Does automated trading improve trading efficiency - use multiple equation regression model for futures and spot
  4. Unemployment and recession linkage -- auto regressive model with latent variable.... With all this jazz could anyone have predicted the 10% unemployment rate that the US is facing now !! Sometimes it makes me feel that all macroeconomic variable prediction is just some jazz and has no practical value beyond keeping a few economists/econometricians in a job!
  5. Brand loyalty of the customers -- Multinomial probit
  6. Do people make up their minds before elections -- artificial neural network problem ..Basically I look at it as a non-linear regression with fancy functions!
  7. Temperature forecast uncertainty -- Use GARCH to judge the correlation structure of the forecast errors

My takeaway : Considering my interest is modeling variables in finance, I think that it would be naive to depend on some linear parameters of an equation with gaussian assumptions in it. Even though a flavor of models are presented in this book, I don't think models of such type are going to be useful anymore. THE GAME OF GAUSSIAN ERROR TERM is over.

Scientific Illusion

Here is a nice paper that I have stumbled on, "The Scientific Illusion in Empirical Macroeconomics" by Lawrence Summers.

Basic argument of the paper is :

Formal econometric work where elaborate technique is used to either apply theory to data or to isolate the direction of causal relationships where they are not obvious a priori virtually always fails. If this argument is accepted it suggest that in evaluating empirical work we should begin by asking different questions than the ones usually posed.Instead of considering the methods employed, we should ask whether the fact reported is an interesting one that affects our view of how the economy operates. Does it affect our belief about a substantive question?

Good empirical evidence tells its story regardless of the precise way in which it is analyzed. In large part, it is its simplicity that makes it persuasive. Physicists do not compete to find more and more elaborate ways to observe falling apples.Instead they have made so much progress because theory has sought inspiration from a wide range of empirical phenomena.

Macroeconomics could progress in the same way. But progress is unlikely as long as macroeconomists require the armor of a stochastic pseudo-world before doing battle with evidence from the real one.

Books such as freakonomics , frequels, undercover economist etc are a hit in the non-fiction world, precisely because there is a dearth of literature in "doing and articulating empirical research work". Deep structural parameters, structural models give a sense of scientific jazz to economics...but at the end of the day, one must realize that economics / finance is SOCIAL science.

Lesson :)

Duration Mismatch

A nice illustration about the current US situation

Panic : Summary

Michael Lewis, needs no introduction . His previous works have been best-sellers. When he writes a book about subprime crisis, I was expecting a dot-com esque narration of subprime mess with all the ingredients of a thriller movie ride.It turned out to be exactly what I expected.This book is essentially a collection of articles, essays by various people before panics, be it the 1987 crash | asian currency crisis | dot-com bust | the recent sub-prime bust. Reading this book is like bringing old newspaper articles, magazines from your attic to go over what people said about various crisis situations.

The book talks about four panics

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Oct 20, 1987 Panic - Black Monday

The more I read about Oct 1987 crash in various books, the more I think that nobody knew why it happened. Even though 6Billion USD of the total sales of 40 Billion USD (Stocks + Futures) on the day of crash was represented with portfolio insurance selling, it could be hypothesized that portfolio insurance was the effect rather than the cause. Michael Lewis in the first part of the book presents various articles appearing in NYTimes, WSJ ,research reports to take the reader back to 1987 environment. There are articles which blame Program trading in the form of index arb or portfolio insurance for the crisis. Some feel that synthetic option creation itself was a flawed concept. If you want to manufacture an option on the market, you cannot do that by selling and buying the market as the market will not allow you to do it.

Facts are : Index arb saw that the futures were going through a free fall and stock market was trading at a huge premium. Most of the trades knew that the stock index was calculated using Friday's closing prices but still chose to believe that they were updated. They shorted the index and waited for the futures to fall to close the other leg of the arb. Meanwhile large fund houses who could not short the stock, shorted futures instead. However when the stocks finally opened on Monday, the cash futures spread was not juicy and index arb ran amuck trying to close the other leg of the transaction thus buying futures for a brief period of time. Meanwhile stocks were dumped by everyone and thus futures continued the free fall.

After the fact analysis :

• There should have been circuit breakers
• Futures trading is dangerous
• Portfolio Insurance is crap
• Dynamic option manufacturing is a sham
• Coordination problems between Futures and Spot market regulators
• One thing to fear is "FEAR"
• Margin requirement should be drastically increased in futures market
• Uptick rule should be applied to futures market
• Shorting restriction in futures market

As any after the fact analysis appears, they appear correct in hindsight but nobody said anything before crash. ! Is it age-old willingness to suspend one's critical judgement when lots of money is made ?

A gem of a statement in this part of the book is :
Best animal behavioral experts don't pretend to know why antelopes panic precisely when they do.

Can we know why humans panic? Very unlikely

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Asian Currency Panic

Well, the gory details of the currency crisis is known to all. Foreigners lend large amounts of money as short term capital and South east asian countries lap them up. However the capital was short term and it was naturally difficult to fund long term projects, irrespective of whether the countries intended. The currency panic is combined with LTCM failure. One thing I feel about reading these panics is that it gives some numbers about the sheer volume of mess that was created. Here are a few numbers

• 175 Billion of Short term debt was lapped by South east asian countries
• Russian Treasury Bills were offering 50% interest rate
• LTCM blew away 4 Billion in 6 weeks
• LTCM lost $120M in one day and lost $500 M on one trade, selling put options on index
• Partners put in $1.9B of the total $4.4 B fund

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Dot.com Panic

A classic recount of dot com crisis. Again numbers are something which hits me , every time I read about dot com mania. Here are some numbers and quotes from the book

• Dumbest Dot-com , All Advantage raise 175 Million USD and spent 40 Million USD in the first quarter . Their model , pay users to surf. It was valued at 700 Million USD by the venture capitalist !
• Theglobe stock rose 600% on the first day of its IPO. Their model, replicate society on the net !
• Computer.com spent 60% of its initial funding on 90 second Superbowl Ad.
• In 2000, 371 Internet companies were value at $1.3 Trillion , 8% of the entire US Stock market
• NASDAQ peaked on March 10th 2000 and 10 days later, an article in Barrons, "Burning Up", changed the world of dotcom
• Dotcom era : Instead of stock price reflecting the fundamentals of the company, it started affecting the fundamentals of the company
• Investors doled out $78 Billion to the employees of 100 start up companies
• Bill Gross 1000 page article on GE Capital financing made the stock tank by 25%
• A boom with out crooks is like a dog with out fleas. It does not happen.
• When some one talks badly about a boon, turn back to him and ask him,"Where were you during the boom time" ?.

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Subprime Panic

Any numbers reported for this panic will be incorrect as we are still living through that crisis. I remember reading a book titled " Trillion Dollar Meltdown". With in a few months, the same author came up with another book . "Two Trillion Dollar Meltdown". By Feb 08, there were numbers being quoted as high as 7 Trillion Dollars as the global impact. Is this really a panic ? When I hear the word panic, I have a mental image of an event happening very quickly. Subprime mess keeps telling us that there is more to come.

In any case, the good part of the book is that you don't have to read it sequentially. Since it has articles and independent views from various people, one can start reading from any page....So, I guess, the book can be a good subway read/ office commute read..

Buffet on Long dated options

Via Berkshire Hathaway 2008 Report :

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The Black-Scholes formula has approached the status of holy writ in finance, and we use it when valuing our equity put options for financial statement purposes. Key inputs to the calculation include a contract’s maturity and strike price, as well as the analyst’s expectations for volatility, interest rates and dividends.If the formula is applied to extended time periods, however, it can produce absurd results. In fairness,Black and Scholes almost certainly understood this point well. But their devoted followers may be ignoring whatever caveats the two men attached when they first unveiled the formula.

It’s often useful in testing a theory to push it to extremes. So let’s postulate that we sell a 100- year $1 billion put option on the S&P 500 at a strike price of 903 (the index’s level on 12/31/08). Using the implied volatility assumption for long-dated contracts that we do, and combining that with appropriate interest and dividend assumptions, we would find the “proper” Black-Scholes premium for this contract to be $2.5 million.

To judge the rationality of that premium, we need to assess whether the S&P will be valued a century from now at less than today. Certainly the dollar will then be worth a small fraction of its present value (at only 2% inflation it will be worth roughly 14¢). So that will be a factor pushing the stated value of the index higher. Far more important, however, is that one hundred years of retained earnings will hugely increase the value of most of the companies in the index. In the 20th Century, the Dow-Jones Industrial Average increased by about 175-fold, mainly because of this retained-earnings factor.

Considering everything, I believe the probability of a decline in the index over a one-hundred-year period to be far less than 1%. But let’s use that figure and also assume that the most likely decline – should one occur – is 50%. Under these assumptions, the mathematical expectation of loss on our contract would be $5 million ($1 billion X 1% X 50%).

But if we had received our theoretical premium of $2.5 million up front, we would have only had to invest it at 0.7% compounded annually to cover this loss expectancy. Everything earned above that would have been profit. Would you like to borrow money for 100 years at a 0.7% rate?

Let’s look at my example from a worst-case standpoint. Remember that 99% of the time we would pay nothing if my assumptions are correct. But even in the worst case among the remaining 1% of possibilities – that is, one assuming a total loss of $1 billion – our borrowing cost would come to only 6.2%. Clearly, either my assumptions are crazy or the formula is inappropriate.

The ridiculous premium that Black-Scholes dictates in my extreme example is caused by the inclusion of volatility in the formula and by the fact that volatility is determined by how much stocks have moved around in some past period of days, months or years. This metric is simply irrelevant in estimating the probability weighted range of values of American business 100 years from now. (Imagine, if you will, getting a quote every day on a farm from a manic-depressive neighbor and then using the volatility calculated from these changing quotes as an important ingredient in an equation that predicts a probability-weighted range of values for the farm a century from now.) Though historical volatility is a useful – but far from foolproof – concept in valuing short-term options, ts utility diminishes rapidly as the duration of the option lengthens. In my opinion, the valuations that the Black-Scholes formula now place on our long-term put options overstate our liability, though the overstatement will diminish as the contracts approach maturity.

Even so, we will continue to use Black-Scholes when we are estimating our financial-statement liability for long-term equity puts. The formula represents conventional wisdom and any substitute that I might offer would engender extreme skepticism. That would be perfectly understandable: CEOs who have concoctedEven so, we will continue to use Black-Scholes when we are estimating our financial-statement liability for long-term equity puts. The formula represents conventional wisdom and any substitute that I might their own valuations for esoteric financial instruments have seldom erred on the side of conservatism. That club of optimists is one that Charlie and I have no desire to join.

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Here is a more option-math oriented explanation for the above content :

Warren Buffett, Black-Scholes and Long Dated Options

Galleon Group - Fraud ?

One of my research colleagues ( a Phd in Math) wanted to get in to industry. However her desire came at a rather disturbing economic time( 2008 ) . She interviewed at various fund houses but the job situation looked grim. However she managed to get 2 offers. One from her home land , a plum treasury related role and other a job at Galleon hedge fund, NYC. She joined Galleon.The job responsibility at Galleon was exciting as it was high frequency options trading strategies and she was kicked about it.  I felt good about the whole stuff as she got in to a role which she wanted to do . The fact she lived in my neighborhood at NJ made me generally enquire about her work etc as she had moved from a hard core research oriented role to a role which involved taking daily trading calls. She seemed happy doing intra-day trading.

However today, I feel very sad to know that a fund which is very well known in NYC and looked upon as a great fund , is actually a FRAUD. I am sure my former research colleague is equally disturbed with the events surrounding Galleon. If one scans through the attached document, a 34 page accusation, it has all the drama for a hollywood story. "Inside information to make money"!!

I just don't understand...Have the markets become so efficient that you can't trade on public knowledge, your own fundamental/technical/quant analysis ?? Or is it the same old story of GREED taking over the center stage?

The fact that it has a few indians involved , makes it even more a sad event.

Galleon Fund Notice DocumenT-34 pages with all the gory details

Media Link : NYT: Hedge Fund exec charged

Beauty(Returns) is in the eye of beholder(Investor)

Take any asset , Every investor has a specific idea of return as it is based on the expected holding period, ability to short sell, turn over of the asset etc. If one were talk about returns, it is very difficult to give a generic metric. There are umpteen ways to calculate returns as one can imagine. For example,

• One could take daily returns and multiply by 252, daily standard deviation and multiply by sqrt(252) to get a risk return profile
• One could take weekly returns and multiply by 52, daily standard deviation and multiply by sqrt(52) to get a risk return profile
• One could take monthly returns and multiply by 12, daily standard deviation and multiply by sqrt(12) to get a risk return profile
• One could just take a mean of calendar year returns and sd of calendar year returns.

In Indian markets, there is no doubt that NIFTY, NIFTY Jnr index assets figure amongst the sought after assets. Also with Gold witnessing a dramatic rise year after year , it is attracting hordes of investors too. Lets look at these assets from a risk return perspective and understand the importance of the scale for returns calculation

Let's look at these assets over the last 5 years

Takeaways

1. Weekly annualized scale for Gold gives a completely different picture as compared to Daily annualized and Monthly annualized. The gold returns are about 5%. These numbers are important as one sees different numbers quoted in the media under different circumstances.
2. NIFTY Jnr is more risky than NIFTY. It is pretty obvious point as the mid cap is going to be more volatile than large cap.
3. NIFTY and NIFTY Jnr daily returns annualized have almost the same risk return whereas monthly annualization shows that NIFTY has a better sharpe ratio than Jnr.
4. Weekly scale - It is important to realize that distributions under weekly scale look so so different. Aggregation at this scale and metrics resulting from it paint a different picture

Let's look at the same graph after removal of 10% of outliers

Takeaways

1. Daily Scale return profile changes completely for Nifty and Nifty Jnr.
2. Weekly returns annualized for Nifty and Nifty Jnr starts making sense as the numbers are somewhat comparable to daily annualized and monthly annualized
3. One thing that stands out is that Gold gives about 15-20% returns whichever way you slice and dice daily and monthly data.

However this is just the beginning of looking at scales. For a realtime investors who want to stay put for an year, these metrics do not make sense as they would be interested in looking at returns between t1 and t2 where t1 could be any day of the year and t2 is accordingly 252 trading days away from t1. These are not rolled returns as rolled annualized returns have a different calculation. For lack of better terminology , I will use rolled returns to mean that returns between sets of ordered pairs (t1,t2) in the sample where t2-t1 is 252 days. Now amongst such ordered pairs, there are again three ways to calculate returns.

• One can take daily returns between t1 and t2 , add up , call it annualized returns , roll it on each day, , form a sample and compute stats
• One can take weekly returns between t1 and t2, add up , call it annualized returns, roll it for each week , form a sample and compute stats
• One can take monthly returns between t1 and t2, add up , call it annualized returns, roll it each month , form a sample and compute stats
• One can take point in time value returns between t1 and t2, call it annualized returns, roll it on each day, form a sample and compute stats

If one computes metrics as described above for the period 2005 to 2009 , here is how returns look

Takeaways

1. Weekly scale still paints a different picture
2. Daily returns and weekly returns look similar though the returns for each of the asset is shifted downwards by a specific amount
3. The last graph in the above illustration is probably useful as it uses point returns between ordered pairs(t1,t2) for every data point . If one thinks about it, this is what an investor with an expected holding period of 1 year would get .

There are umpteen other scales at which the data can be aggregated.

So, sharpe ratio of the assets reported in the media with out mentioning the proper definition of the scale used , is very dicey to interpret. Depending on what one wants to say, one can combine risk return metric at the appropriate scale and can use to further an opinion about the asset.

Few points on Technical Analysis

One of my friend's brother works for the stock exchange and I happened to chat up with him about technical analysis. I have been told that a user interface would be available soon on the web site of the stock exchange. He asked me my thoughts on the some of the TA graphs and the type of charts I would be interested in looking at. This post is a summary of my thoughts on the same.

Firstly, I do not believe the practices of current technical analysts who spew out words like support , resistance, heads and shoulders, neck lines, triple tops etc and urge YOU to take positions based on the pattern. The BIGGEST problem with this approach that it is not statistically back tested. Infact one academician has written a great book on statistical analysis of technical indicators in which he mentions that about 500 charting patterns on S&P 500 were found to be statistically insignificant. He calls the technical analysis in the current state, a sham and calls for a completely revamp of the way technical analysis needs to be carried out.

So, the obvious question is , Why do investors needs charting / graphical interface to price and volume information? It is well known that our eyes deceive us and mistake randomness to pattern. 
Colors, lines are all illusions. Look at this video if you still believe that you can trust your eyes.

However, we are humans after all. We thrive on stories, we try to rationalize everything in this world using story telling.Technical analysis is a form of story telling which has no statistical basis, but very powerful  and viral in communicating analysis.  Nearly every investor tool / site/ stock market site has a tool to chart various indicators. WHY? I have asked this question myself umpteen number of times and have  not reached a coherent argument for completely disbanding the technical analysis approach aka charting approach to financial analysis. The only logical reason for knowing about charts and drawing charts on a site is , it gives an additional comfort feeling  feeling  if you are like me, who uses rigorous statistical back testing for taking a trading position.

If I were to create a technical analysis tool , here is what I would consider

1. Any series chart should have price and volume and price movement backed by less volume is different from price movement backed by high volume
2. An option to create logarithmic charts of prices. I believe looking at raw prices is useless as they do not magnify the difference. A price movement from 50 Rs to 100 Rs is same 1000 Rs to 1050 Rs
3. Stock data needs to be standardized. Or else there it is a chart crap. There are numerous sites which do not give dividend and split adjusted series and hence the trend lines etc which are drawn become totally useless.  All the data needs to be split and dividend adjusted so that any technical charting analysis can be done
4. If cash futures trader wants to use a futures chart, the dynamics are completely different. I have got to chain various futures prices and then show it in continuum. For example, a basis spread trader might want to see cash and futures charts . I have to see to it that futures are chained continuously and the volume chart needs to have volume of cash and volume of futures shown together.
5. Point 4 is a completely different category of charts as you are seeing two assets movements. Hence it would be better to bifurcate my graphical window in to single stock GUI and multiple stock GUI
6. It is important to have more than once instances of the chart at one point in time, meaning ability of the trader to use the same scrip and generate many windows on the same data and each window is a separate pop up
7. Include charts like Max Gainers/ Losers/ % Gainers/ % Losers / Most Active on that specific day.
8. Drawing options charts is a completely different ball game as there are so many aspects to it like implied vol near month, mid month, bid-offer spread overlay , open interest overlay, underlying security comparison etc . A short term intraday trader would look at a specific type of options chart whereas a guy trading near expiry would want to look at a different chart.
9. Some of the signals I would track are :

• N-day moving average ( N could be 5/30/100/200 day MA days etc).  I think this is THE MOST powerful tool from a statistical point of view. Ideally a slow moving average, medium MA and fast MA  would be superb to see in one plot
• Bollinger bands is a logical extension to the above point
• Exponential Moving average... Trader can choose a smoothing value.
• AR bands..Basic Autoregressive bands would do. Not many people track this, but I think it is damn useful
• Ability to combine AR and MA(x) is probably I would love to see dynamically generated real time
• I don’t believe in channel indicators as they have no statistical basis AT ALL. But if you want to, you can incorporate PSAR (Parabolic Stop and Reversal)
• RSI –  God knows whether this works in the real world , but some of the folks in the hedge fund industry track... One of the main reasons for its popularity is it gives buy and sell signals very fast and hence make a trader do something  about it.
• MACD lines to show bullishness or bearishness  - This is another way of overlaying of Moving Average plots. I think this plot gives Okish signals, atleast better than RSI
• Momentum:  Indian market has a lot of traders who live on momentum. So, such a plot would be helpful for the trader community as such.
• Giving user an ability to draw trend lines
• One of the things which I really like is the daily returns plot and daily returns square plot. At one shot it gives an idea about the return and volatility of the stock

Sometimes I do feel the single most reason for knowing about these patterns is that everybody uses them and stock market is a place where its the majority that wins. If majority are bullish, then stock rises irrespective of what you and I think. However I find it rather painful to read about trendlines , necklines etc as they have no statistical basis whatsoever. Recently I asked a technical expert who had spent about 15 years in this field about the % of calls that went right in his entire career, he said , it was about 60%. Somehow I think that if you use robust statistics to find out trading patterns, you will be better off than trusting your eyes. I firmly believe that EYES always deceive you ..On a lighter vein, I don't believe in love at first sight :)

Miles to go

Why "portfolio optimization" as a discipline , needs to develop a LOT ?

Link : 1/N , an LBS paper reports that equally weighted portfolio is good enough!

Efficient Asset Management : Summary

Everyone who goes through the BSchool /CFA rut knows about mean variance optimization . Some of them might know that it does not work in the practical world. So, what if noble prizes have been awarded!! It doesn't work in practice...Like all models, however, it does give a framework to think about and nothing more than that....So, What's the alternative ? If you are given a couple of assets and are asked to create an asset mgmt plan, how would you go about it ? Pick up any random Bschool student and ask this question and I am certain that only a few of them would be able to answer coherently..Why ? Finance education is stuck in gaussian world!! anyways, the point of this post is to summarize this book which suggests alternatives. I came across a reference to this book in a paper and was curious to know more about it, almost a year ago. Was intending to use Bayesian estimates if ever I had to solve a portfolio allocation framework. This weekend , I had decided to take a look at this book out for sheer curiosity. What I was found in this book was a fascinating critique of classic MV solution and more than that, a statistically solid approach to the MV optimization problem.

If one doesn't want to ponder on this, one can follow the usual procedure of computing covariance matrix of the returns , leading to efficient frontier which spews out specific portfolio compositions for a level of risk....Amidst all this jargon, one quickly realizes that this is good in paper, this is good for software vendors who make plugins for mean variance optimization, good for hiding the complexities that actually take place in the real world. But if you want to really manage assets using MV approach, one needs to think about its limitations and work around it.

First, something about this book. 2 methods mentioned in the book , Resampled Efficient Frontier and Portfolio Rebalancing, Analysis and Monitoring have been patented in US and are awaiting patents in other countries. Well, patenting is only one part of story. The view of the book is from a statistician perspective which makes a case for a healthy understanding , than taking a pure numerical approach / analytically efficient approach.

Molehill of garbage in and Mountain of garbage out , is the way the authors describe MV optimization. To begin with, the limitations of MV approach are the following:

• Returns are assumed gaussian
• Investor's Utility function is not considered
• How to use it in Multi period context ?

Typically one uses the mean and variance of the historical data to construct the frontier. However the estimation errors gives rise to instability in the portfolios. Also there is an ambiguity in testing the differences between a portfolio on efficient frontier and outside of it. Authors of this book urge the investment community to follow approaches based on statistical grounding to grapple with the instability and ambiguity issues of portfolio optimization.

The books starts off with a basic introduction to classic mean variance approach which comprises applying quadratic programming technique which comprises an objective function(portfolio variance) with a set of equalities and inequalities like expected return of the portfolio, sector allocation rules, budget constraint rules, sector allocation rules , non negative weights for portfolio weights etc. The basic technique is minimization search algo that finds the best allocation across the assets in the portfolio. Concept of parametric quadratic programming is also touched upon where corner portfolios and its importance are discussed in the Markowitz framework.

For a detailed understanding of corner portfolios, refer to RiskLatte and Sharpe . Sharpe's exposition is more leisurely.

Author then moves on to giving a gist of alternative approaches that have emerged :
1. Alternative measures of risk instead of second moment
2. Utility function Optimization
3. Multi period investment horizons
4. Monte carlo techniques for asset -liability flow simulations
5. Linear programming optimization

Each of the above though touted as alternatives to Mean variance approach has in itself another set of limitations!

The book then explores Unbounded MV frontier and Linear Constrained MV Frontier. The first approach limitation can be seen by simply bootstrapping a sample from the historical returns and seeing that sharpe ratio of the sampled data is very different from the actual data. Thus the unbounded method clearly is of no investment value for a portfolio manager. One needs to give a great importance to estimation errors as it will turn out a different MV portfolio. Thus it looks like Unbounded MV solution, though analytically elegant is basically useless for an investment manager.

In most of the cases, the portfolio manager puts some constraints on the unbounded mv problem like sector specificity or sum of weights = 1 etc and the bounded one seems to be doing ok for a few repeated sampling. The book points out the reason being that in the unbounded case, the higher returns are usually over represented, which means the higher estimation errors are over represented and vice versa. In the bounded, the role of constraints is just to minimize that aspect. Apart from that Linear Constrained MV is also not good. Here the author explores a intensive bootstrap experiment to introduce the concept of Efficient Frontier Variance

The above 2 graphs summarize the concept of frontier variance. Efficient frontier, for the same set of data, under bootstrapping gives rise to variability in the frontier. Also efficient portfolios for a set of risk level( figure on the right) also span a specific area. If an investment manager has to use MV , then the statistical nature of MV has to be taken in to consideration. The book mentions the following approaches , each of which taken individually improve the MV portfolios, but when taken together can substantially improve the investment management procedure.

1. Resampled Efficient Frontier Optimization
2. Resampled Efficiency rebalancing, monitoring, analysis
3. Stein Estimation
4. Bayesian Estimation
5. Avoiding Optimization design errors

Resampled Efficient Frontier (REF) Optimization

"If you are 100% confident of the estimates, use Classic MV ", is what the author states!, reason being that MV is extremely sensitive to estimation errors. Book goes on to make a strong case for REF by saying that REF are average portfolios and provide safe bets on assets under the realistic case of estimation errors in the input. There is also a simulation based proof of the usefulness of REF. What is the look-back period for resampling ? This gives the flexibility which is missing in the rigid classic MV. A value based manager can take a longer history while a growth based manager might be willing to look at short term horizons. Another related concept is the Maximum return point, which is not present in classic MV approach. This Maximum return point gives higher return for a lesser sigma as compared to some assets which give lower returns with higher sigma!! Paradoxical it might seem to a person who is only thinking in terms of Classic MV !! However, considering the variability of the input estimates , this is very much a situation where there are inferior assets present in the asset universe included for optimization. A quick illustration would help one see this point.

One of the best things in this book is the clear illustration of REF Vs Classic MV approach using a story of a person trying to find his pet

Let me summarize this story , for this is a beautiful analogy to look at REF vs Classic MV approach.
The story is about Robert who has a pet hedgehog . One day he notices that it has escaped from the cage and is somewhere in the fence. The fence is sturdy and hedgehog escape proof. Fortunately, Robert had fitted a GPS device. He gets signals about the location of his pet.
Now if one were living in unbounded MV world , depending upon what the signal shows, Robert would go and search the location. For example , if A were the location displayed on GPS, he would go to A. However he knows that there is a fence that his pet cannot go out. Thus he ignores A. This is like living in a bounded MV world. If he believes that GPS is 100% accurate in its estimates , then he would go to whichever location that GPS points out with in the boundary.

However he knows that GPS has always some estimation error with it. So, he accumulates a set of points and then depending on the cluster properties , ventures out to search for his pet. This is similar to REF where you resample the input so that you are aware of estimation error and the portfolio you construct is more appropriate. So, instead of going to M which the GPS currently points out, he would probably go out and venture location R.

Portfolio Rebalancing, Analysis and Monitoring
Once a portfolio has been selected, it needs to be obviously rebalanced according to market conditions. Most of the present rules are adhoc , to say the least. Rebalancing is done quarterly, monthly,annually....for god knows why ? if asset weights exceed a specific percentage level, they are rebalanced ? why ? Most adhoc rules like technical analysis that is prevalent in todays world is completely arbitrary. There is no statistical significance attached to the decisions. This chapter makes a case for brining in statistical mindset to this process. By formulating a distance measure between the current portfolio and the previous selected portfolio, the author makes a case for portfolio rebalancing.

At this stage of the book, the book introduces meta-sampling method....I could not understand this method well enough to write about. Hope to understand this concept in the days to come.

Input Estimation and Stein Estimators
This is an important chapter of the book where there is a discussion on the sample means and the way they are used every where for multivariate case. Charles Stein(1955) was the first person to prove that sample means are not an admissable statistic for a multivariate population mean. Since then there have been a lot of work in this area, though finance community has been wary of adopting them, for no reason i guess.
The basic funda behind these estimators are that it starts off with a prior and then based on the sample means and their variances, population mean is calculated. James Stein estimator, Frost-Savarino estimator, Stein covariance estimation and Ledoit Covariance estimation are discussed in the book. Preliminary results of applying these estimators are provided, though the author admits that it is in the interest of finance community , industry to be specific, to focus / prod academic research in this area.

The last couple of chapters talk about benchmark mean variance optimization, bayes fundas and general aspects that need to be kept in mind while optimization.

My take on this book : It is a terrific book which will make you think about a lot of aspects about mean variance optimization. One of the highlights of this book is that most of the stuff is explained intuitively and through diagrams. This is a must read for anybody who is connected to asset management work !.

> rnorm(15) Thoughts

A Random sample of my thoughts

1. If Nifty falls / Nifty rises , does the implied vol move in the same direction of the underlying or does it move the opposite direction. This becomes a very important question in the context of choosing a local volatility idea or chucking such an idea ? Local vol models assume that underlying and smiles move in different directions!!
2. As there is a short sale restriction on stocks, what exactly is a tradable underlying for Nifty option, for valuation purposes ? Yes you do hedge a long call option with whatever is a cheap, a future/ETF. But then is it sensible to treat a long call option as option on futures and put option as an option on index, the reason being that that they are the respective hedges for the options available
3. Does vol of nifty determine the atm imp vol level for nifty options? If so, how does it move ?
4. Is there a curvature for vol across strikes ? If so, does the curvature remain quasi-stable?
5. What is the functional/deterministic/stochastic relation between the comovement of nifty futures and nifty options in a 20,100 seconds(9:55AM - 3:30PM) trading day ?
6. What is the average trade volume per moneyness in a trading day ? This is easy...got to crunch a few numbers to get an idea
7. World-over the realized vol is explained by atm variance largely(80%)? Is it the case in Indian markets ? Are skew and convexity merely jazz in Indian markets which one need not worry too much ?
8. If you look at an option trade that happens at time t1, what would be the corresponding future price that you would consider for valuation? the instantaneous futures price, average of future prices in the interval (t1-delta, t1)? Can you choose delta with some rationale, meaning, some metric which justifies choosing last 5 min / last 10 min / whatever that one thinks is meaningful ?
9. Parameters for Vol of Vol for Nifty - Are they stable and more importantly what is their impact on Imp vols
10. Is imp vol in Indian markets a completely random animal ? Is Indian market completely a punter's market , meaning every one is a momentum player ?and analyzing numbers beyond a point is a useless activity ? ( I hope not )
11. Are circuit breakers, fancy as they might sound, properly designed? Shouldn't financial markets be there for the price discovery to happen? How can the third busiest exchange in the world trade for 30 seconds (Monday,after election verdict) and let the order flow go to other stock exchanges ?
12. Are Indian investors sensible ? Nobody wants to buy when nifty was 2600 level and now when it has reached 4300 , every one wants to buy? Shouldn't it be the other way round ?
13. Who makes money in the entire game ? Are the only people who make money are Custodians, Fund account management fees, brokerage houses, financial advisors? Is "extracting decent alpha consistently", a fantasy and there is survivorship bias amongst the successful fund managers that we see around ?
14. How do people trade reliance options or for that matter any of the American options on the Indian market? Are there good pricing and hedging tools for these options available with traders ?
15. Gap up and Gap down!!! , the biggest problem in the Indian markets -- How do you sleep well when you know that you have got to deal with such an issue the next day morning ?

................................May be I will find clues to the above stuff, someday!

Covestor : Purple cow

Via TP:

Caters to a nice gap and it is touted as a service which democratizes hedgefunds - http://www.covestor.com/how/

Will this make money ?

VIX Trading in India

Via FE:

With a rising number of market participants closely tracking India Vix, the volatility index that indicates markets’ expectation of volatility in the near term , the National Stock Exchange (NSE) would like to start trading in the index. An NSE official said the exchange would soon approach the Securities & Exchange Board of India (Sebi), the market regulator, for its approval.

Introduction of Vix Futures and Vix Options would start an interesting phase in Indian markets.

More than you know : Summary - Part 2

Continuing from my last post where I had left the juicy part of the book for this post, here I go :

The last topic contains ideas which I find fascinating. Markets are complex adaptive systems and one can understand it better if you have systems thinking. Peter Senge's Book "The Fifth discipline" had a great impact on my way of thinking and each essay in the last part of the book is a suggestion towards developing such a kind of thinking.

The author points out to the need for diversity in thinking. Reading about how stats is used in baseball to improve a team's performance can teach you a lot than trying to look for cases in your own domain. A healthy discussion with a ecologist can give you more insights about speculation in financial markets than trying to look at indicators from the same field. Google's search engine and the way it used eigen value decomposition can provide you with a far more insight in to the usual factor analysis of stock returns. Research evidence from study of ants, bee-hives can give clues about how markets aggregate information.

Decision markets, and readings like "Wisdom of Crowds", " Wikinomics" can give you insight in to taking positions on a stock or a derivative.
Study of fractals is more meaningful than studying and working on the same old academic finance theories which are based on normal and log normal distributions. Power laws and the ways Power laws are used in fields like economics, psephology and like can help one understand markets better.

The entire point of the essays under this section is to urge you to keep your mind active and open to things happening in fields which might not seem related at the outset. Markets as a system can be understood better by applying ideas and thoughts from other fields.

Take any branch of finance in today's date..It is crying for contributions from other fields..It is being flooded by sociologists, ecologists , psychologists, who know that finance as a discipline can grow only by fusion of ideas from various fields. Case in point , Mandelbrot's fractal finance. Finance literature has virtually nothing on fractal finance as compared to the other kinds of literature. I had a chance meeting with the Mr.Barone Adesi , the person behind closed form solution for american option. I asked him whether he would consider research in fractal finance a viable option for a budding researcher. He said, he had himself worked on it for 2 years and was hopeful that research will pick up in this area.

I remember another faculty giving an analogy of ants in research literature. If you place something that attracts ants and you see to it that there are only 2 possible ways, inevitable one of the ways gets used by ants and the whole path is jam packed, even though an alternate path is available. This is similar to academic literature where it is only by getting references and citations from similar research initiatives , can one publish papers. Coming back to the analogy of Ants, once in a while , a few ants move from the pack and explore new paths and that is how ants as a community survive. Finance has reached a stage where it needs such diverse thinking and one can only hope a few people from academia or a few practitioners will actively stray from the usual path and help us answer some of our questions on

• How do markets aggregate information ?
• What does aggregation mean from market efficiency ?
• Concept of risk remains incomplete
• We don't know how our brains work ?.........................and many more such questions that need to be looked at.
  
  This book reminds me of a set of essays I read a few years ago, "Essays on Derivatives" by Don Chance. Each of the essays revolved around a simple but powerful idea. I get the same feeling after reading this book.This book provides a lot of food for thought. If you want to think on the areas where finance concepts have to be worked on, read this book..Out of 30 ideas, I am certain that atleast a few will interest you.
  
  

More than you know : Summary - 1

This book contains about 30 essays , meaning 30 ideas which can be read, ruminated and possibly applied in real life situations wherever they are applicable. Though the book is about finance, it draws considerable amount of research evidence from Biology, Math, Sociology,etc . At a 10,000 ft view , actually each discipline is nothing but an effort to understand the world using a set of constructs.

I would term Finance as a soft science rather than hard science where the discipline needs to be understood from various eyes . For a number cruncher , it becomes all the more important to realize that numbers,statistics is after all ONLY one part of the story in markets. One must consider market as a complex adaptive animal where numbers is ONLY one part of understanding. The more the mental models one has, the better one is equipped to understand what's going on.

I will try to summarize the key idea from each of the 30 essays from the book.

Part I

1. Process is more important than Outcome in Investing
   Always consider success as a lucky realization unless you backtest it. Just because you make money using some trading rule does not mean that you are going to make it again. There is an undoubtedly a strong relation between good process used to make a decision and a good outcome. Think about it, this applies not only to investing but also to most of the things we do in life. Lets assume that you think that C# is the best language to create something that is needed, but somehow your skills are rusty and you would rather do it with VBA and build the same screens etc. Well, it might be probably good for short term, but can anyone use the tool apart from you? Is it Scalable ? Does it have a wow! element ? Is it extensible ?
   
2. Constant see-saw between Investing considered as Business or Profession
   This is the same point which Daniel Kahneman makes about the scale struggle. Money managers consider Investing as a business and work for the immediate goals and immediate returns (quarterly/yearly) but Business operates on a longer scale. I think instead of thinking of it as a Business or Profession, it is best if one can think of it as a Business and Profession. There will always be some assets which can be shorted at a professional scale and always some assets which can be longed at a business scale.
3. Babe Ruth Effect
   Well, this one is pretty well known. Focusing on frequencies and not on the Expected value can kill a risk manager. The same idea as Taleb's Pi Lambda. In Mediocristan, Frequencies are important because payoff is pretty much symmetrical. But in Extremistan, it is important to understand the payoffs are not symmetrical. Infact they are very skewed and following frequentist approach is equivalent to being a sucker.
4. Proper Categorization is the Key (Context based)
   Most often than not, we categorize things and do not check the stationarity of the categories. My best ever read on categorization is an essay by Clay Shirky (Ontology is Overrated). Categorization as a subject has always fascinated me. In this book however the author says theories based on static categorization are not good. Well, we all know it, but how many of us actually keep that in mind when we go about theorizing, categorizing world events.
5. Learn to translate uncertainty to Probability.
   Degrees of Disbelief, Propensities and Frequencies are three ways to get to probability . Academic world is orgasmic about Frequentist world.Successful traders love propensities and uninformed trades live in the world of degrees of disbelief. For a trader who wants to trade by numbers, it is very important that he follows all the three ways depending on the context. For a plain vanilla options, frequentist is not a bad path to take. Well, you can rise over the usual theories known , do your own analysis on whether to follow mixture models, poisson+ gamma , whatever. However propensity based path needs to be taken too based on the context
   
6. There is a Hot Hand in Investing, if not in baseball
   Long streaks of success typically indicate luck imposed on great skill
7. Equity Premium Puzzle
   Loss aversion and Myopia can explain the equity premium puzzle. Somehow I felt that is is the same time scale difference that is being brought out by the author
   
   
   
   Part II : Psychology of Investing
   
8. Link between Stress and Suboptimal portfolio Management
   Essay beats the same point. Money managers operate on Shortening horizons, leading to stress to leading to suboptimal portfolio performance
9. Persuasion
   Robert Caldini's 30 year work is wonderfully summarized in this essay. The construct that there are 6 tendencies that spur a behavioral change : Reciprocation, Consistency , Social Validation, Looking, Authority and Scarcity . Each of these 6 tendencies are important when you need to think about the trading behavior change that can be observed in the market or trading rooms
10. Emotion and Intuition in Decision making
    
11. Guppy Love : Role of Imitation in Markets
    Asymmetric information, Agency costs and Preference for conformity are three things which force traders to confirm. But this also leads to Herding.
12. Beware of Behavioral Finance
    This chapter was like a breath of fresh air in this book. Behavioral Finance follows the reasoning, Individuals are irrational and an aggregation of irrational individuals must lead to an irrational market. However the essay says that " Markets can still be rational when investors are individually irrational". Meaning there is a law of large numbers at play. The very fact that players operate at different time scales, investment styles and investment approaches means that diversity will always ensure collective rationality.. Which means whenever there are imbalances the market corrects itself. Well, Do I agree with this view ? I don't think I am knowledgeable enough to form a view. But as such as a basic premise,I do believe in collective rationality and individual irrationality. But again, it is just a belief with no evidence to back it up
    
13. Essay on Keynes Expectation theory
    Comparison of Markets to "Beauty Contest" and "El Farol Bar". The idea that is conveyed in this essay is about deductive Vs inductive reasoning. Inductive is how we operate and it brings itself with a variety of bias. Deductive is how economics tries to operate , applying general principles to specific. In the case of markets, it ought to have been deductive but looking at market players, it appears inductive.This debate of deductive and inductive reasoning is brought out clearly.
    
    
    
    
    Part III: Innovation and Competitive Strategy
    
    
    
    
    I had a very strong tendency to skip over these essays for the simple reason that I have grown indifferent to the words strategy, competition, innovation etc. These are words, I remember , back in B school days, I was so fond of. It seemed as though using these words and thinking on these lines was leading to productive thinking. Over a period of time, I have realized that these are just fillers in conversations when you don't have clear thinking. In fact all the buzz words that I learnt at B school, I have consciously tried to avoid over the past few years. These words look good in case studies and HBR articles ... that's all there is to it. If you have to do anything productive, I think it is better to ignore these words and think in simple terms.
    With this kind of strong aversion towards such words, I was in no mood to go over these essays on strategy. But then I gave in, thinking that I might get at least some simple & meaningful thoughts from the next 7 essays. What did I find ?
14. blah-blah-blah-blah
15. blah-blah-blah-blah
16. blah-blah-blah-blah
17. blah-blah-blah-blah
18. blah-blah-blah-blah
19. blah-blah-blah-blah
20. blah-blah-blah-blah
21. blah-blah-blah-blah
    
    
    As expected the entire part on strategy was like reading some old Bschool case..It bored me to death!! The only thing that I found was somewhat educative was about fitness landscapes. Again you can term it as old wine in new bottle. The other possible idea which deserves a mention is the analogy of chess players and traders
    -- Don't look too far ahead
    -- Develop options and continuously revise them based on the changing conditions
    -- Know your competition
    -- Seek Small advantages
    Again the above points are cliched, but the essay is a little better as it brings about nice chess analogies using these points.
    
    
    Part IV: Complexity Theory
    
    
    
    This part of the book rocks!
    
    Will summarize the last part of the book in my next post
    

Taylor effect

Many people around the world, after the subprime crisis more so , equate correlation with charlatanism. May be , but should that stop anyone from understanding data, not really. Knowing whatever metric about the unknown is much better than just having an intuitive feeling of things. Sometimes having a wrong map while exploring is OK, atleast it gives one confidence to venture and try things out.

Taylor effect is one such aspect about correlation. If you look at a series of returns and consider various metrics, like abs(ret), (ret^2), abs(ret)^2.5 and so on, one can think of this question :

What is the autocorrelation for various lags (let's say the first 6 lags) for these metrics? This question becomes important when you think about volatility measures ? Should one always go with returns^2 as a metric for vol? what's wrong with abs(ret), or abs(ret)^1.5 . or why not abs(ret)^2.3434 , whatever!.. What makes ret^2 such a wide spread metric for vol ? Actually with the availability of intraday data, one needs to seriously consider what is a good risk metric ? Anyways, these are questions that will take some time and effort to think about and understand, based on what data/market one is working on.

Coming back to the title of the post, Taylor effect says that correlation of absolute value of returns with its kth lag is always going to be greater than correlation of absolute value of some power of returns with its kth lag.

Some basic data crunching of NIFTY returns validates this effect to some extent.

6 lags plotted for various values of d

One thing to note is that the auto correlation of squared returns for lag =1 is more than the autocorrelation of absolute returns for lag = 1 ( the top most curve in the above figure). Usually it is the otherway round , atleast according to academic literature for US markets.

Why to bother looking at powers like squares cubes of abs(returns) ? Becoz after all volatility is an estimate of what you think captures risk.
What metric to use for volatility is a big question!!!

25% Market Vanishes

NSE in its new circular has dropped about 25% of the stocks from Futures and Options Segment.

Well before the number surprises you, the actual numbers are not that terrifying considering that the whole equity F&O market itself is small as compared to developed countries.

There were only 252 stocks that were allowed to be in F&O segment at the beginning of the year. 68 stocks have been dropped in this year citing liquidity reasons, stock manipulation etc. Hope these 184 scripts remain and provide liquidity to the market.

U Shaped Spreads

Bid ask spread movement is an interesting metric to track in a day. It has been observed in developed markets that it follows a U shaped pattern
More details in this paper :Limit orders and Bid-Ask Spread

In Indian markets case, came across a 3 pager in Economics and Political weekly that says that the bid ask spreads do follow the same U pattern.
Link : Are you getting a good deal?

It would be interesting to see how the option spreads behave amongst the NIFTY options in India !. Understanding Indian markets is anyways going to be interesting as it is an intense 9:55 AM - 3:30 PM game where all the instruments get traded.

GJR GARCH for Nifty Vol

Academic literature on GARCH and flavors of GARCH runs in to tons of papers by various researchers. The idea behind GARCH and all its flavors is that there is a conditional variance equation governing the evolution of usual returns, the residuals of which were otherwise assumed homoscedastic. There are a lot of variations of this conditional variance equation which gives rise to various flavors of GARCH.

Some of the markets have seen EGARCH performing better than other flavors. In Indian markets though , some of the literature suggests that GARCH(1,1) outperforms ARCH and other flavors. Some researchers have also tried to improve GARCH(1,1) forecasts by incorporating Implied vol of the options as one of the predictors!!.However the data used in all these models is before the great crash in Indian markets where vix has seen crazy levels. Markets in 2008-2009 has seen Indian Market capitalization shrink from ~1T USD to 0.6T USD . A quick look at the Nifty log returns gives a good indication of the crazy vol levels that Indian markets is seeing. BTW, India VIX as of today is above 50. So, it was natural for me to think about Asymmetric GARCH in these markets and there might be a case for AGARCH models

Nifty Log returns

Crunched some data on Symmetric and Asymmetric GARCH and found that it is indeed GJR GARCH, a version of asymmetric GARCH which has higher statistical power and forecasts better than Symmetric GARCH. Here is an illustration of the out of sample test results and the bands created by GJR GARCH do a good job of forecasting Vols(atleast a one month forecast seems ok)

I am running short of time on checking other fancy models..like arma+GJR GARCH, arma + T GARCH etc ...But I would bet my money on Regime switching GARCH , considering that pre election & govt formation regime volatility would be very different from post government formation regime in India. It would be wise to take this effect in to consideration. It is very likely that Regime switching GARCH would capture the vol better than the current GJR GARCH that seems to be working well..Anyways , this forecast should be fine for now!

Why should one care about a decent historical vol forecast at all ?
Well, the reason being , if you want to know anything about Implied vol of NIFTY options, you got to get a hold on the underlying NIFTY volatility...Or else, "Omitted variable" bias will make your Implied vol model weak/useless :) ..

Aspects to think about : How to incorporate this historical vol in the implied vol model ? Should one just forget about historical vol and just fit a semi parametric model ? Hoping to get some clarity on these things in the days to come.

Salih Neftci

Salih Neftci-- Any student in the quant world would have come across this professor while reading either of his wonderful books on option pricing. He was a very popular professor in the NYC area and more so at CUNY. Some of my friends who attended his classes were all gaga about the way he brought things to perspective.

Came to know from my director that Prof Neftci passed away last night in Switzerland, after a one-year battle with cancer. Student community will miss him dearly.

Its more like V

Smiles & Smirks are what one would get to see about Vols in US Markets. However a quick crunching of NIFTY options show that implied volatility is more V Shaped. Infact Puts are tilted towards right and Calls towards left. Here is an illustration between moneyness and implied vol.

Something to think about :How do you model this quasi V shaped market phenomenon ? and more importantly How do you make money ?

Gaussian Copula : Formula that killed Wall St.

Came across a wired article which says the following copula formula by David X.Li was widely used for securitization models. Results are known to everyone now!

 

Link :Wired

Make Hay while the Sun (read mkts) Sets!

Via TradersMag:

"And anybody who has been using pairs with options is making a fortune right now with the volatility, said Eric Goldberg, chief executive of the EMS maker Portware. "The last [12] months have just been a dream for these guys."

Pairs and arb traders have accounted for some of the strongest growth in Portware's client base of late, Goldberg said. Originally, Portware's EMS was built as an index-arb system, he added.

Also, the changing landscape for market participants has played a role in the increase in arbitrage strategy use, experts say. One important factor was how proprietary trading desks at the big banks-formerly large players in the relative value, and risk and merger arbitrage spaces-have been quiet for the past several months.

"There aren't a great deal of prop desks currently trading," Herriot said. "Flow from the major desks has been decimated over the past 12 months."In the past, prop traders have helped close the gaps in prices with their arbitrage trading. But their absence has opened up opportunity for others.Very few are brave enough to call the direction of the market; clients prefer to be hedged and market-neutral," Herriot said. "Pairs trading is probably the most accessible and best way of doing that."

In the meantime, arb players continue to fill in the pricing gaps and add liquidity. "More market participants realizing potential arbitrage opportunities provide greater liquidity," said Court Crane, an executive director with Morgan Stanley in electronic trading."

Root of the Problem

Michael Lewis : Since the beginning of the crisis I’ve wondered why the government has found neither the will nor the way to attack the root of the problem -- the people who borrowed money to buy homes they shouldn’t have bought.

Link : Bloomberg

Taleb & Daniel Kahneman Thoughts

Key takeaways from the discussion :

• Taleb - Discredit Business Schools, CFA's, math-fin programs which teach crap portfolio theory / gaussian theories
• Taleb - 2 tiered structure , Banks should be made utility companies . High risk taking firms such as hedge funds should be left alone with a clear signal of NO BAILOUT under any circumstances.
• Taleb - Semi government intervention will not work..NATIONALIZATION is the only solution
• Daniel - Useless map is better than no Map, as it gives confidence for people to go out and venture. Useless models could be useful in the short term. They might be useful to predict daily weather but not hurricanes.
• Daniel - Time scale problem : Individuals deal with short term problems, Corporations exist for long term.
• Taleb- Doing nothing is far more useful than doing something in certain situations . Analogy of ancient medicine which killed more people than it healed.

This discussion reinforces my belief :  Being a fox is better than being a Hedgehog!

Word for the day

Brazilian Straddle :

When traders know that they are technically bankrupt, there is a tendency to take a huge speculative position. If price changes favorably they are saved. Otherwise, the outcome was same as before-"doomed"

A trader who uses this strategy is said to hold a "Brazilian Straddle". It consists of a large market position held against a one way airline ticket to Brazil in the breast pocket. If the market position proves profitable, the trader sells the ticket and comes back to trade tomorrow. If the trader continues to lose, he runs off to Brazil and leaves his clearing member to clean up the resulting mess.

2 Tiered Structure

Via Bloomberg

Taleb says:

• We have had worst form of banking structure.. Capitalism when banks made profits, Socialism when banks showed losses!
• 2 Tiered structure is the way to go , where the First tier comprises banks that are completely nationalized and are operated as utility companies. Second tier comprises companies that are free to take any risk they want to take,but with the rider that they will never ever be bailed out!!
• I don't understand Options , being a option trader for 10 years. How will the regulators understand ? Nothing complicated than vanilla options should be traded.

Where's the end ?

Via Bloomberg

The IMF report released yesterday , Jan 27, signaled that write downs and losses at banks totaling $1.1 TRILLION so far are only half of what’s to come. Losses on that scale would leave banks needing at least $500 billion in fresh capital to restore confidence in their balance sheets.

Here is how fast things are changing...A couple of months back, I read a book called "The Trillion Dollar Meltdown" by Charles R Morris. My review for the book is here . Now there is a new book by the same author titled " The Two Trillion Dollar Meltdown"

Going by the current trend , I think he is currently writing a third book and the title is easy to guess :)