Who where the leaders in the ABS market?The US securitization market experienced an amazing growth rate of almost 19% on an annual basis since its inception in 1985 until 2007, reaching a peak of about USD $1.
2 trillion in issuance.
Figure 1 shows the growth of the securitization market and its relationship with interest rates.
Figure 1: The availability of a growing supply of fixed income instruments backed by uncorrelated asset classes helped bring interest rates down.
In this chart, I choose to display some interesting milestones in the market, including the ones where I personally worked, in bold.
Source: SAGA Capital, LLC.
The securitization technology developed by US quants at investment banks allowed producers to obtain cheap financing for operations & expansion.
In fact, from 1985 to 2007, the average spread of a 5- year “AAA” rated ABS dropped from 120bps (1.
20%) over LIBOR to about 20bps (0.
20%) over LIBOR.
Figure 2: Fueling the purchase of risky pieces of Asset Backed Securities (and other securities) was the staggering amount of assets under management by Hedge Funds, which grew from 350 Bn USD in 1996 to 1.
4 Tr USD in 2006 (almost 3% of the world’s GDP that year).
Source: SAGA Capital, LLC.
This explosion of cheap financing was supported by an incredible expansion in the assets under management of hedge funds, from about USD $350 billion in 1997 to its peak of over USD $1.
4 trillion in 2006–2007 (Figure 2).
Hedge funds contributed to the massive infusion of cheap capital to the world’s markets, since a non trivial number of these hedge funds supported ABS issuance by acquiring the riskier tranches, generally the equity pieces, rated below investment grade.
At the same time asset under management for hedge fund peaked, securitization reached peak volumes in 2006, with over 1 trillion USD/year in issuance.
Citi, Lehman Brothers, Bank of America, JP Morgan and a few other American banks responsible for over 1/3 of all the world’s flow (Figure 3).
Figure 3: The numbers of dominant banks with the know how and resources to create Asset Backed Securities grew from less than a dozen in 1996, to more than 50 in 2006.
Still the largest players remained the ones shown in the chart, with Lehman Brothers among the leaders in Mortgage Backed Securities & ABS in Emerging Markets.
Source: SAGA Capital, LLC.
What was the impact of ABS in the US and Global Economy?From a top-down approach, the securitization market contributed to the generation of new assets for monetization, and indirectly helped increase employment rates in the US and the rest of the world, as well as the expansion of domestic & global economies.
At the same time the US securitization market was experiencing growth and innovation, the average US citizen dramatically increased his/her debt load, from 50.
4% of Personal Disposable Income (PDI) in 1958 to over 100% of PDI during the George W.
Bush administration.
In that same period, US citizens reduced their personal savings rate — in favor of higher consumer spending — from a high of 11.
20% of PDI during the Ronald Reagan administration, to a low of 0.
25% during the George W.
Bush administration.
The importance of US consumer spending in the context of the world economy and its relationship to US GDP is not well understood.
The US economy in 2008 was by far the largest economy in the world, and US consumer spending represented a staggering 70% of the US GDP.
To have an idea of how important it was, the chart in Figure 4 show US Consumer Spending vs GDP of several countriesFigure 4: Top GPD’s (2007), in billions of USD vs just one of the components of US GDP, consumer spending.
Source: SAGA Capital, LLC.
In the 15 years previous to the crisis, the easy credit markets experienced in the US were fueling most of the GDP growth experienced in the US and the rest of the world, in a sort of cycle, as shown in figure 5.
This worldwide industrial growth, fueled by US consumer spending, had a direct impact on the corporate profits of the world’s markets, as evidenced by the growth of market capitalization of virtually every stock exchange in existence.
For the data scientists aspiring to become data strategists (and even for many senior data scientists and data strategists working in financial institution and without a finance or economics background), the chart in Figure 5 shows a high level view of how securitization fueled the world’s economy.
Figure 5: US Consumer Spending & Securitization fueling the world’s economic growth.
Source: SAGA Capital, LLC.
Easy, readily available credit to US consumers via credit card or access to instantaneous, internet approved home equity loans encouraged consumers to save less, consume more & improve their quality of life by purchasing new homes, cars, get better education and acquire other goods and servicesIn mid 2007, one type of asset class that had been growing dramatically within the securitization market started to show signs of problems.
The asset class was the sub-prime mortgage, which had become very popular among some investment banks, pension funds, and hedge funds, due to the supposed low risk and attractive returns of some of its tranches in a securitization.
Figure 6: ABS Outstanding as of 2009.
Source: Federal Reserve Bank of ChicagoFigure 6 shows the growth of ABS outstanding from 1995 to late 2008.
We can clearly see that “Other” and “Home Equity” lines of credit represented more than 2/3 of the total market.
The “Other” category included “derivatives” instruments, such as ABS of ABS, CDOs, CDOs squared, etc.
Once you had problems in a particular asset class, the ABS that included that asset class had problem, and well as the “ABS of ABS” that contained that asset.
Figure 7 below shows the spread for a typical 3- year, Credit Card “AAA” rated securitization increased from about 0.
2% over LIBOR in early 2007, to almost 6% over LIBOR in December 2009.
Similarly, the volume of securitizations in the market decreased from over USD $1.
2 trillion in 2006, to less than USD $30 billion in the first quarter of 2009, a 98% drop with respect to its peak.
Problems in two US financial institutions contributed to the increase of spreads in ABS: 1) Bears Stearns, the fifth largest US bank, and 2) Lehman Brothers, the 4th largest bank.
Both had proprietary capital in ABS, in addition to being heavily involved in the creation of all types of securitization products worldwideThis drastic decrease in the available funds for financing, and its associated increase in the cost of financing created a global financial “gridlock” that needed to be handled by the world’s authorities.
Figure 7: Spreads for AAA rated Credit Card ABS (as well as all other ABS) skyrocketed in late 2008 and peaked as Barack Obama became the President of the US.
Issuance of Credit Cards ABS were fueling the US and the World’s economy.
Source: SAGA Capital, LLCIn order to jump start the US economy and try to reverse the pervasive trends it was experiencing, two new plans were put into place: the TARP program, implemented in the last days of the Bush administration, and the TALF program, announced by the Obama administration a few days after Barack Obama took office.
The Federal Reserve Jumpstarts the EconomyOn December 15, 2008, Barack Obama was declared President of the US.
A few weeks later, on February 2, 2009 the US Federal Reserve announced the TALF’s Master Loan and Security Agreement, indicating the rules of the program.
Despite the massive injection of capital into the US banking system in the last days of the Bush administration, there was not an increase in consumer lending or spending.
The US Federal Reserve and the Department of the Treasury under the Barack Obama administration recognized that reviving the ABS market was critical to restoring the flow of credit to consumers.
Accordingly, the Fed and the Treasury launched the TALF program — a USD $1 trillion program designed to entice investors into buying legacy and newly issued AAA- rated securitizations backed by specific asset classes.
By providing most of the financing for investors’ purchases, the Fed and Treasury hoped that renewed demand for ABS would drive down credit spreads for the issuing banks and finance companies and encourage investors to buy, promoting further securitization of loans.
In turn, this should make more loans available to more consumers at lower rates.
The TALF program provides up to 95% of the money necessary to buy a qualified bond, at rates (fixed or floating) of LIBOR plus 1% over 1-3 years, with a minimum loan amount of USD $10 million.
The TALF program substantially improved the risk- reward profile for investors by providing true non- recourse loans.
As a result, substantial leverage was possible and thus raising potential returns significantly.
Losses were not leveraged, as these were true non-recourse loans that did not require posting of additional collateral or forced redemption of collateral.
To initiate the process, a US investment company (either a newly formed or existing one) that meets the US government requirements had to open an account with a Primary Dealer and identify eligible bonds that the investor wanted to buy.
TALF purchase walk-troughTo understand the logistic of the program back then, let’s walk through a hypothetical purchase.
First, let’s assume that we are back in the first week of the announcement of the TALF program back in early 2009.
Let’s assume that a hypothetical fixed income Portfolio Manager at Goldman Sachs checks his Bloomberg screen and sees something like the table below, under the first page of thousands of TALF eligible securities:A few of TALF eligible securities.
Source: SAGA CapitalThe Goldman Sachs Portfolio Manager decides that the CCCIT 2009-A1 A1 “AAA” securitization of auto loans, issued by Citigroup in the amount of 3 billion USD could be a good bond to hold.
Checking LIBOR and spreads at that time he might have seen:ABS Spreads.
Source: SAGA Capital.
So for the purposes of this exercise and under our assumptions, the Goldman Sachs PM could have bought, let’s say $10MM USD worth of that bond, expecting to make approximately 4.
79% (278bps + 201bps ) annual return for 3 years on his 10MM investment.
Not bad for a Fixed Income portfolio manager.
What about a hedge fund manager?.4.
79% annual return for 3 years is not the kind of returns that a hedge fund is looking for, specially when hurdle rates greater than 5% are not uncommon.
However, TALF leverage completely changed the distribution of risk, and made the boring, low yield AAA ABS security the best game in town.
After checking all the documentation, requirements, etc.
the expected annual returns under no loss scenario when buying TALF eligible securities could be summarized in the above formulaContinuing with our hypothetical Goldman Sachs PM, he asks his Jr.
Analyst to get a current list of TALF leverage and finds out that for a 3 year Auto “AAA” ABS, the Fed provides 94 cents for every 6 cents you commit, at an interests rate of LIBOR — 100 basis points, with a minimum investment of $10MM USD.
Actual TALF leverage for eligible securitiesApplying the formula above, the PM sees that the expected annual return of acquiring that bond (in a zero default scenario) changed from 4.
79% to 32.
67% for the next 3 years!((((2.
01% + 2.
78%)*0.
06) + ((2.
78%-1.
00%)*(0.
94)))/0.
06) = 32.
67%The best part of the TALF program for investment managers was that, again, the leveraged capital was non-recourse loan, meaning that in case of default, the loan from the Federal Reserve did not have to be paid back.
But few understood the opportunities or were frozen in panic.
If you had survived the crisis, you did not want to risk you job and suggest investing in what was perceived as risky securities.
Some of those who understood the risk/returns could not buy the securities; for example, our hypothetical PM from Goldman Sachs.
Since Goldman Sachs as well as most of the banking sector had been bailed out directly or indirectly by Bush’s TARP program, they were precluded from participating in Obama’s TALF program.
The bail out program of Goldman Sachs and others, created as a consequence of Lehman’s bankruptcy and to stop the weakening of the US banking sector, was masterminded by the then Secretary of the US Treasury, Henry Paulson and former CEO of Goldman Sachs.
So effectively, this opportunity was initially taken advantage of by a few high net worth individuals, a small number of few hedge funds that understood ABS, and eve a smaller number of pools of quants (or data scientists, although they were not called like that back then, but “special opportunities quants” or “risk finance quants”) that understood the models, had domain expertise, and who pooled their resources to meet the investment requirements.
My particular exposure was part of this case.
Expected returns of a systematic TALF purchase program— Monte Carlo Simulation using Python, Pandas, Numpy, SciPy & Statsmodels Time Series AnalysisSo we are back in 2009 and let’s assume that we are a special opportunities quant working at a family office that has never invested in low yield ABS.
We want to analyze if investing $100MM in TALF qualified securities is a good idea.
As a quant/data scientist, you want to know the answer to the following questions in order to present your idea to the risk committee:What is the distribution of expected annual returns if we commit $100MM to program in 4 weeks?.Let’s say, $40MM the first week, $30MM the 2nd week, $20MM the 3rd week, and $10MM by the 4th week.
What is the probability of losing money if we commit to buy every bond we can with our capital, regardless of the spread at purchase time?How many Fallen Angels can we expect in TALF elegible securities?What is the probability of making more than a 5% hurdle rate, if we discriminate what bonds to buy based on their spreads and leverage at the moment of purchase?What percentages of downgrades can we expect in the quality of the portfolio in that strategy?What is the median no loss expected annual return of that strategy?What are the expected defaults in our portfolio in basis points?What kind of average leverage we can expect?What is the average life of the portfolio?These are questions we should have asked as of Feb 2009, before committing to the purchases.
There are some last question I will answer in this exercise that refer of the performance of the model in real-life:How well did our interest rate model fit reality?How well did the modeled correlations behaved?Was it feasible to obtain the size of loans we modeled for our purchase program in weeks 1, 2, 3, and 4 of the TALF’s program life?Beforehand, I will say that the model behaved as expected and the returns were realized, as you will learn with the rest of this post.
This was not an academic exercise but a real trade, and yes, hundreds of billions of USD were available for 3 years at LIBOR minus 1%.
ModellingTo keep thing short and in the conservative side of modeling, let’s assume that 1 and 2 year LIBOR rates are the same as the 3 year LIBOR rate, and we will base all our calculations in the modeled 3 year LIBOR rate (conservative assumption, since the 3 year rate is generally more expensive than the 1 and 2 year rate).
The one year ABS spread will be the 3 year ABS spread minus the average historical difference (up to 2009–02–18) between those 2 terms.
The two year ABS spread will be halfway that difference.
These are the steps for the whole analysis:Simulate N paths of correlated 3-year “AAA” spreads for Credit Card, Student Loans, Home Equity Line of Credit (HELOC), Car Loans and LIBOR sampled weekly, starting at the date of the announcement of the TALF Master Loan Agreement.
Recreate the 1 and 2 year spreads from the 3 year spreads.
For every week, simulate purchase of one of the 4 types of assets, with probabilities given by the distribution of ABS outstanding as of 1/2009.
Apply FED haircuts to corresponding asset, and use spread for that asset class calculated in 1 and 2 to estimate the no loss expected return.
Simulate migration of the bond from its initial AAA rating to other ratings as bond default, and keep track of its migration over the life of the bond.
Bond is not traded, but held to maturity or bond default.
Show relevant distributions to answer questions.
Train, Test, SplitThe 2000–2010 time series looks like thisI split the data in 2 segments: before the TALF announcement (for training) and after TALF announcement.
The crisis period is the one below.
The period for testing starts in the last black dotted line, shortly after the spreads started to come down from their peak, when Barack Obama became President of the USA.
Milestones of the financial crisisMean reversion and auto-correlationMean reversion is the assumption that an asset’s price will tend to move to some long term average.
This might happen with interest rates, commodities, currencies, etc.
Deviations from the average levels are expected to revert to the average.
Auto-correlation or serial correlation is the correlation of a signal with itself as a function of a time lag.
It is often used in signal processing for analyzing functions or series of values, such as time domain signals.
Below are the auto correlation plots for LIBOR, and 3-years Auto Loan Credit Card, Student Loans, and HELC spreads.
Auto correlation for LIBOR rateBy inspecting the lags for the LIBOR rate, we can see that there are several lags that are statistically significant with positive and negative auto correlation.
All of them are good candidates to model future rates.
We could predict a future LIBOR rate based on many lagged parameters of itself.
In order words, today’s value is equal to some mean value, plus a fraction of yesterday’s value (phi), plus some noise.
For a process to be stationary, the phi values needs to be between -1 and +1.
If the process is just White Noise, phi=0, and if it is a random walk, phi=1.
If phi is negative, the process shows mean reversion.
If phi is positive, the process shows momentum.
In the chart below, Rt is a time series of interest rates.
Using the auto-correlation chart of the LIBOR rate above, we could define futures rates as a function of its lags.
The first one is AR1, the second one is AR2, etc.
Below are the auto correlation charts for ABS spreads.
Auto-correlation for Auto Loans and Student Loans time seriesAuto-correlation for HELC and Credit Cards time seriesThe code to generate the graphs above is in the gist below:Auto-correlation and significant lags for time seriesBy inspecting the lags for ABS and LIBOR rates, we see that there are many lags we could use, but how do we avoid over fitting or under fitting our time series model?Order of the auto-regressive modelAlthough we could use all valid lags for all assets, we will probably be over fitting our models.
A solution is fitting many models (AR1, AR2, AR3, etc.
), and measure the Akaike Information Criterion and/or the Bayesian Information Criterion for each model and determine what number of parameters gives us the lowest BIC or AIC.
BIC for LIBOR and selected AAA ABS spreadsAccording to the AIC or BIC, models higher than AR1 might contribute to over fitting our model, therefore, I will simulate rates using AR1 models for all assets.
The code to determine the optimal parameters is below:Once I determined the optimal lags to use, I created a dictionary of AR parameters for all the ABS spreads and the LIBOR rate.
CorrelationsThe MC simulation needs to generate simulated rates that keep the properties of their historical correlations.
As mentioned before and for simplicity’s sake, I calculated the correlation among the different asset classes at the same maturity, and assumed that the correlation of, for example, the 3 years AUTO ABS AAA spread and the 1 year AUTO AAA ABS spread was 100% (which is close to the behavior in real life most of the time).
For the multivariate random number generator, I initially used the scipy function here, but found that using a Cholesky decomposition was computationally more effective.
I will also generate a date_index that will contain all the future dates of our analysis and the potential longest maturity of the simulated bonds we will buy.
The code is below:Probabilities of AAA assets being downgraded or defaulting in the holding period.
To estimate this, I created a function that defines a transition matrix based on generic historical transitions probabilities for AAA ABS in one year, excluding mortgages.
This 1 year transition matrix can be used to simulate transitions from any initial state (“AAA”, “AA”, “A”, “BBB”, “BB”, “B”, “CCC”) to any other state plus “D” using a one state Markov process.
We can then estimate the probabilities of transition over a given number of years by adding the 1-year transition matrix to a recursive function as a function of time.
We can derive many transition vectors running the function in a simulation.
This will hep us estimate probabilities.
pyabs, credit migration estimationSimulated leveraged purchases on a single interest rate scenarioThe process here is a follows: With one of the simulated interest rate scenarios, we simulate the purchase of any of the 4 eligible assets classes in week one, at whatever prevalent market rates we have, and with the leverage provided by the Federal Reserve.
TALF leverage per type of ABS as a function of term (f_risk_capital), probabilities of 1, 2, and 3 year issuance (p_term), and probabilities of acquiring each of the asset classes (p_issuance)With those market conditions, we can estimate the no loss return, and decide if we buy or not.
However, we will not know if we would incur losses in any of those purchases.
To know that, we need to model defaults; but we will not use that knowledge for our decision to buy.
We will only use expected annual return and hurdle rate.
In the simulated scenario above, in week 3 (index 2) on 2009–03–06 we could have bought USD $166 million of a “AAA” auto loan ABS maturing on 2012–03–02, committing only USD $20MM of our capital and USD $146MM from a loan from the Fed.
With those prevalent market conditions, our expected annual return on that purchase, (no loss scenario) is 7.
82%.
Assuming a hurdle rate of 10%, we would have not bought that specific bond.
Additionally, our simulated 3-year Markov transition shows that that bond remained as AAA during its life.
So, on this particular scenario, we would have purchased bonds at index 0, 3, and 4.
Those simulated market conditions would have allowed us to lock USD $250MM @ 25.
42% for 3 years, USD $166MM @ 63.
09% for 1 year, and USD $100MM @ 20.
46% for 1 year.
That turns out to be USD $516MM in investments with only USD $60MM of capital, and the simulated defaults showed that none of the investments defaulted.
This particular simulation shows that the first bond transitioned from AAA to AA, which could have slightly affected its market price; but that is of no concern to us, since TALF purchases had to be held to maturity, and market-to-market pricing was not an issue.
The code below generates a dictionary of pandas df that you can pass to a solutions data frame to answer all your questions.
ResultsDistribution of returns and their percentiles:What is the distribution of expected annual returns if we commit USD $100MM to program in 4 weeks?What is the probability of losing money if we commit to buy every bond we can with our capital, regardless of the spread at purchase time?:len(solution[solution['exp_annual_r']<0])/(sims*purchase_weeks)0.
049836How many Fallen Angels can we expect in TALF elegible securities?solution.
groupby(['final_rating'])['final_rating'].
count()/(sims*purchase_weeks)final_ratingA 0.
011288AA 0.
101102AAA 0.
884480B 0.
000418BB 0.
000852BBB 0.
001516CCC 0.
000214D 0.
000130What is the probability of making more than a 5% hurdle rate, if we discriminate what bonds to buy based on their spreads and leverage at the moment of purchase?strategy_df = solution[solution['exp_annual_r']>0.
05]len(strategy_df)/(sims*purchase_weeks)0.
881436What percentages of downgrades can I expect in the quality of the portfolio in that strategy?len(strategy_df[strategy_df['final_rating'] !='AAA'])/(sims*purchase_weeks)0.
101908What is the median no loss annual return of that strategy?strategy_df['exp_annual_r'].
median()0.
37166140387288993What are the expected defaults in basis points?(strategy_df[strategy_df['final_rating'] == 'D']['total_purchase'].
sum()/strategy_df.
total_purchase.
sum())*100001.
2697864542783506What is the average fraction of capital we expect to commit for every $1 of purchases?strategy_df['risk_capital'].
sum()/strategy_df['total_purchase'].
sum()0.
0854416650602756What is the average life of the portfolio?sum(strategy_df['term']*strategy_df['total_purchase'])/sum(strategy_df['total_purchase'])2.
241573279710343With all these results, the recommendation to a risk committee of an investment firm back in February 2009 was to commit capital to the TALF program, expecting a 39.
79% annual return for 2 to 3 years, in a no loss scenario, and with a basic strategy of only investing if market conditions (spreads, leverages, and maturities), allowed us to earn an annual return greater than 5%.
The interest rates stayed within the confident intervals of our model.
We could have expected to realize returns close to the ones in the median scenario.
Expected defaults were modeled at about 40 bps, and we could have expected to receive ~91 cents of leverage for every 9 cents we committed to a diversified portfolio of TALF qualified ABS.
How well did the our interest rate model fit reality?.How well did the simulated correlations behave?Below you can see the actual 3 yr credit card AAA spreads in orange, plotted against many simulated paths; and dotted lines representing the median, and 5th and 95th percentiles.
Although our purchase strategy was concentrated in the first 4 weeks of TALF, the graphs shows 10 weeks of results.
Realized rates stayed within expectation.
Simulated spreads for 3 years AAA Student LoansAll other realized rates stayed very close to the model’s approximation and within expectations.
Simulated LIBOR rateAnother way to look at the graph above, is to plot the distribution of spreads at the last week of our purchase program, and compare the actual values with the median values expected from our AR1 simulation model.
LIBORstudent loans, credit card, helc, autoA better visualization, an animation of the shifting distributions of forecasted values for the LIBOR rate:As we move forward in time in our forecast, we start seeing fatter tails in the shape of the distributionsAs we can saw before, the median values of the simulation did a good job estimating the actual realized values.
Simulated correlation also stayed within expectations.
Below is a chart showing correlations for one of the many simulated scenarios chosen randomly, vs realized correlations.
Simulated vs realized correlationsDefaults and transitions stayed within modeled expectations.
Loans were available for about 3 years; however the juiciest returns were made investing in the first year of the programs; when spreads were high relative to later years.
To my knowledge, no investor experienced losses.
Outstanding TALF loans from inception of program to end.
Source: Federal Reserve Statistical ReleaseIn retrospect, was the TALF program good?A paper published a while ago by the Federal Reserve Bank of Chicago concluded the TALF program prevented the US economy from sinking deeper than it did.
The paper references a study done by some academics where they found a strong link between financing conditions and the sale of vehicles when using both household level data and aggregate data.
Specifically, they found that 38% of the decline in vehicle sales between 2007 and 2009 could be attributed to increases in the interest rates on new vehicle loans and households’ perception that credit conditions were unfavorable.
The purchases of households that were likely to face borrowing constraints were extremely sensitive to changes in credit conditions, but were not sensitive to expected changes in income.
The study found that aggregate vehicle sales fell 130,000 units for every 1 standard deviation increase to the interest rate.
The study suggested that (directly or indirectly) by making credit more accessible and affordable to consumers, TALF supported vehicle sales and the economy as a whole.
Are there other TALF like opportunities out there?.How can we find them?There are always opportunities associated to a financial crisis; but as far as I am concerned, domain expertise, fast prototyping/coding, and fast decision making is the only way to spot and take advantage of those opportunities in finance.
There is not an all purpose AI (yet) that will point out investments in special opportunities around credit risk, commodities, etc.
All those “AIs for Credit Risk” out there are developed by startups are just repurposed Scikit-Learn and/or Google’s TensorFlow code mostly stitched together by machine learning engineers or Jr.
data scientists or data science enthusiasts (as some of them call themselves) with very little or zero domain expertise in finance.
Granted, most of them are built around pretty GUIs, with tons of bells and whistles that give the illusion of a finsied product.
In many cases, the developers do not even know that some of their creations have built in bugs.
For example, in his book “Advances in Financial Machine Learning”, Dr.
Marcos López de Prado documents a bug in Scikit-Learn’s cross-validation and that piece of code is built in many of the AI’s for credit risk I have seen.
You can check out the issue below:Scoring functions don't know classes_ · Issue #6231 · scikit-learn/scikit-learnMoving the discussion with @amueller from pydata/patsy#77 (comment).
Proposing to: add an optional 'labels' argument to…github.
comBut of course, there are some exceptions and a few products out there are truly unique.
You know who you are.
TALF opportunity analyzed by my advisory firm back in 2009About unique investment opportunities, yes, there are a few interesting ones that I will write about in future posts and after the opportunity has been arbitraged out, since it is not wise to write about specific opportunities while they are live, for obvious reasons.
As a matter of fact, practically all the analysis and modeling you learned about here was done by me over 10 years ago, when the TALF was announced.
I was actively trying to raise capital to take advantage of the opportunity, and the background information plus all the simulation you learned about here where part of my presentation to interested parties.
Today, I am writing proprietary code for my private repos and analyzing several opportunities as exciting as the TALF was back them.
But if you dont have domain expertise, you probably don’t know how to structure something around the events going on around the world.
I will give you a hint about a potential interesting deal in the link here.
I hope this data science exercise and and its background information helped you understand one of the best investment opportunities derived from policies in Obama administration.
If you are curious about how the financial crisis affected me personally as a Senior Quant in the Fixed Income trading desk of Lehman Brothers in NY (ground zero of the crisis), please check out the video with the interview that the Wall Street Journal did for me and a few of my colleagues.
The interview was done in September 2018, at the 10-yr anniversary of the largest bankruptcy in US history and which triggered the investment opportunities presented here.
You can find all the code in my Github repo ,as well as a Jupyter notebook here with examples of use.
I hope you enjoyed this post, and please, leave your comments below.
Sources:The US Government Creates Opportunities for Investors in Fixed Income Instruments, Fixed Income Research — Special Report.
SAGA Capital, February, 2009.
The asset-backed securities markets, the crisis, and TALF.
Sumit Agarwal, Jacqueline Barrett, Crystal Cun, and Mariacristina De Nardi.
Federal Reserve Bank of Chicago.
Term Asset-Backed Securities Loan Facility: Frequently Asked Questions.
Federal Reserve Bank of New York, 2009.
Federal Reserve Statistical Release H.
4.
1, “Factors AffectingReserve Balances”, Table 1.
.