How Do I Sign Illinois Banking Resignation Letter

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[Music] this is frm part 1 book 1 foundations of risk management and the chapter on financial disasters now we've been talking a lot in the previous chapters about the importance of identifying risk quantifying risk and managing that risk and this chapter which is called financial disasters could be called hey let's talk about some examples of the violations of not identifying risk not quantifying risk and not managing risk and so we're going to take a look at some examples in which all of the principles of financial risk management that we've discussed have been at least either ignored in part or in full that results in these financial disasters and so what you probably want to do is know the details of each one of these disasters but probably more likely be able to apply it to some fictional disaster that might show up on an exam now the learning objectives are fairly straightforward we're gonna look at some of these case studies and so this is all going to be descriptive in nature there are no calculations there are no equations there are no formulas inside of this chapter and so we'll take a look at each one of these over the next couple of slides and there there's a good number of slides in this slide deck so let's let's go ahead and get to it let's go ahead and begin our introduction describing this concept of interest rate rich risk which we have talked about in the past couple of components to interest rate risks but the primary one that we'll talk about here and in these cases is the simple fact that when interest rates change they influence the value of a financial asset of course the great example is that when yield to maturity on a bond goes up the bond price goes down this called this is called interest rate risk now remember with a financial institution we have assets on one side we have liabilities on the other so a change in interest rates is going to affect the assets over on the left-hand side differently and in the opposite direction as it will affect the liabilities over on the right-hand side of the balance sheet and this can be illustrated with this savings and loan crisis that we had back in the 1980s the text the chapter reminds us that these SNL's were created by the u.s. federal government back in the 1930s and noticed the objective there to facilitate home ownership so lots of time governments and government bodies have really really good intentions but the application and the execution of though and those intentions sometimes has consequences which which we'll see here today actually SNL's go way way back maybe even a hundred years before the 1930s all the way back when states would allow some of their financial institutions to form as a Savings and Loan Association but the idea you know even in the 1800's but clearly after 1930s the idea was to create these institutions so that individuals could go to a particular financial institution to secure a loan for their home right the idea is that home ownership provides stability inside of the home at the micro level and then that expands out to the macro level of the economy if there's people who are comfortable and satisfied and secure in their home ownership then they probably have good jobs and that probably translates into some good macroeconomic variables and an expanding macroeconomic economy now of course when the government gets involved there's going to be regulation and these SNL's were heavily regulated so they were not able to make commercial loans I mean they specialized in residential mortgage loans there was an interest rate margin remember that financial institutions essentially they borrow in the very short-term promising let's just call it a low interest rate and then they lend that capital out and the longer term let's say at a higher interest rate and that margin that spread was specified by the government regulations now what that meant in addition was that these SNL's had limited ability and and hardly any freedom to invest any excess deposits or profits now the cool thing about this from a historical perspective is that these regulations they work fairly well you know in the 1940s you know let's figure about let's figure after World War Two in 1950s and 1960s and so what happened was that they were providing these mortgage loans to residential homeowners but then we hit the 1970s and we had some recessions followed by higher interest rates and then dramatic spike in inflation this is my memory of my high school days under the Jimmy Carter years of high rates of inflation and my mother waiting in line to get gas on a every other day or so it seemed basis so what happened was that the interest rates are rising but these SNL's were required to pay this kind of a fixed rate now of course when you take a look at the other side of the balance sheet these higher interest costs or wiping out that interest rate spreads so they were from the profitability was shrinking and then when interest rates are high of course I tell my students this all the time how could we possibly afford an interest rate that say 18 or 19 percent that was prevalent back in 1979 let's say oh my gosh you know we currently live in a house that's this big we'd probably have to live in a house that's this big and of course when interest rates go up the house mortgages lost value alright so there's a series of let's call them supply and demand or macroeconomic events that were couched inside of this regulation and the regulatory environment that caused these SNL's to suffer and so of course the US government stepped in and they said you know what how about if we relaxed some of these regulations and how about if we increase the deposit insurance and how about and here's the cool thing how about if we allow SNL's to pay interest continuously and this is what I remember learning in my Master of Science days from a really really awesome money and banking professor that the SNL's were allowed to pay interest continuously in commercial banks or not for a relatively short period of time now you guys know from simple mathematics and let's do a quick example here if you start with a hundred and the interest rate is ten percent a year and interest is compounded annually annually you're gonna get one hundred and ten dollars at the end of the year but if interest is compounded let's say semi-annually or quarterly or monthly etc etc you're gonna get a slightly higher number and so SNL's took advantage of this they said something like look we're gonna compound interest every instant you know every instant of every day imagine I mean they made it sound like you would have two hundred dollars today and you'd have a million dollars by the end of the week and of course that's not true the maximum compounding continuously is that hundred dollars can grow into about a hundred and ten dollars and 51 or 52 cents depending on how you round I can trust that you guys can do that in your calculator but look at that bottom block point instead of these changes helping the S&L grow out of their problems they ended up making it worse there's a good word exacerbating now of course when there's an increase in this Deposit Insurance it led the SNL managers to pursue even riskier projects right riskier lending and invest investment activities because they knew there was a backstop this is called moral hazard and so these losses were about a hundred and sixty billion dollars and of course 160 billion dollars today is a lot more than what it sounded like all the way back in in you know 1987 or whatever year this was alright so what are these lessons that we can learn oh my gosh regulation is good I told my students this all the time of course of course all sorts of markets need rules and regulations but we need efficient rules and regulations so notice that first lesson over-regulation can be dangerous how about this one without strict monitoring of course Deposit Insurance can have unintended consequences it can result in increases in market indiscipline and then we have to worry about the decoupling of the value of the assets and the value of the liabilities you know this is why we spend so much time on asset liability management because boy if there was a perfectly one-to-one correlation between those two and we wouldn't have to worry about any of this stuff occurring but of course that's not the case and in fact those correlations can be imagined in the extreme case they could be zero maybe even negative under some some cases but as a good financial risk manager notice that last that last point down there those banks assets should be highly correlated with its liabilities so that when interest rates go up then those things move in similar fashions let's move on to funding liquidity risk this refers to the likelihood that a bank is going to be unable to settle its obligations with immediacy that's important let's take a look at just a simple financial institution let's suppose I have Jim's Bank and I take in lots and lots of deposits today and I make lots and lots of and I have I have ten dollars in cash let's just suppose that's my reserve requirement and during the course of that day I have three or four customers walk in and they they each withdraw a dollar and I have three or four of my creditors come in and say hey Jim you owe me a dollar in interest so what did I have I have ten dollars in cash so I can paid my deposits and I can make my interest payments with immediacy right so that sounds like very little funding liquidity risk but suppose instead of three or four of them coming in and three or four interest payments do suppose it's six or seven so that I have ten dollars in cash and I have fourteen dollars in a liability so with immediacy I don't have I mean ten dollars can't meet $14 but what I can do is go over to the short-term markets and I can issue some commercial paper I can talk to the central banker I can talk to my neighbor bankers I can do all sorts of things to access cash so that I can pay that extra what did I say three or four dollars so funding liquidity risk combines you know the cash that's on hand the cash flow generated by the assets of the financial institution and and and this might be even more important the ability to go out and borrow that money in the short term so roll over that short term credit so what I just described there are those two sources external market conditions and structural balance sheet risks let's take a specific example here of the Lehman Brothers I'm guessing that even some of you younger individuals out there have heard of Lehman Brothers even though they haven't been around for about a decade now but Henry layman was a guy who opened up the layman Brothers store I don't know 150 years ago is actually some kind of like a general store but as his children and grandchildren and great-grandchildren evolved over the years they moved into the financial arena and going back to probably the 1980s Lehman Brothers was not really the first darkness but they really really piled it on with this model that's known as originated to distribute and so the idea is just to make a loan and make a loan and make a loan and then package those loans into some pool of securities and then sell them to recoup all of those assets right and so the idea kind of works well if those mortgage-backed securities are funded by the deposits of the financial institution but but laymen relied heavily on long term debt you know loans and bond issues to fund its operations and so Lehman Brothers as you can imagine made substantial cash flows during the decades of like the nineteen 1990s and even the early 2000s but once that real estate market starting started to surge in that you know kind of the mid 2000s boy hey we're really writing lots and lots of these mortgage-backed securities and selling them essentially all over the world and look at that circle that first circle point I have an asset to equity ratio of about 31 to one by mid 2007 and then of course if you take the other side of the balance sheet or you do the inverse whatever you know that's a extremely high debt to equity ratio now of course when you have all of that debt one of the ways to manage that debt is to go into the repo market and you know the repo market is in its simplest scenario works like this let's suppose that I'm Jim and I'm just a financial institution and I have a Treasury bill that's worth let's say ninety five dollars but I don't have any cash so I agreed to sell it to you today and you pay me ninety five dollars so now I can take that ninety five dollars and I can do something with it I can do something with it overnight or over the next week or over the next month but the idea is that I'm gonna agree to repurchase that Treasury security from you at a fixed price so maybe I'll agree to repurchase that from you for let's say $97 in a week's time all right so you know who are you you're somebody who has lots of cash and you want to generate a short-term rate of return all right so what happens over the next couple of days I take that $95 and I want to try to turn it into let's say $98 so I take the $98 and then I take 97 of that and repay you I have my Treasury Security back plus I plus I have a dollar but essentially that repo market is really a bet on interest rates I mean what am i betting I'm betting I'm betting that interest rates fall and I suppose they fall substantially over these next three days and I buy that security back from you which is now worth let's say a hundred right it's a it's a discount security I call it a Treasury bill let's just suppose it goes up to its par value for some strange reason it goes up to 100 and I buy it back from you for 97 now I have something I'll I made though I made the $1.00 in an interest and then I and then I can generate a capital gain as well and so what Lehman Brothers was doing was constantly constantly rolling over their debt in this repurchase market now of course when the housing market burst then those mortgage applications decline of course there was a constriction in the market and those mortgage-backed securities those values they they fell and they were unable to go back into the repo market or any other market and they were forced into bankruptcy there's the date September 15th 2008 one interesting note is that the government officials allowed Lehman Brothers to file for bankruptcy so they no longer exists but other financial institutions were bailed out or taken over let's go back to when I was in college this Contin Illinois National Bank and Trust Company and so this is really the father of the idea of the too-big-to-fail one of the things that happen back in 1984 is that cotton oi made arrangements and business partner ventures with lots of other banks throughout the world and which they would say something like look if you make these loans we'll go ahead and buy them from you this was a way of continental illinois' to you know maybe kind of diversify their portfolio to reach markets that they were unable to reach before so look at that second circle point there in the middle of the page through this arrangement with the Penn Square Bank cut now continental Illinois purchased a billion dollars in what are now known as speculative energy related loans speculative because they relied on the price of oil and we know from our previous discussions that the price of oil goes like this right it goes like this oh you know over extended time periods that's why oil futures contracts are so popular with companies like you know Exxon Mobil and Southwest Airlines but what happened is that in 1982 and then leading into 1983 there was a big drop in the price of oil and I know this to be true because I was a big fan of that TV show Dallas and jr. Ewing those of you are old enough to remember that Ewing oil lost lots and lots of money when that price of oil fell well well so did Penn Square Bank and continental Illinois now one of the interesting things about this case is that it wasn't like the Lehman Brothers we're okay we've lost all this money and it's time to declare bankruptcy that happened in a relatively short time period this did not I mean the funding dried up and continental Illinois talked to the regulators and they you know they talked to lots and lots of different pe ple when they tried they tried to bring their position back up to being profitable but it took all the way until the early part of May of 1984 for this insolvency to become a widespread event and so look what I have at the bottom there and one week's time ten point eight billion dollars with was withdrawn so imagine let's go back to my bank I'm Jim's bank I have ten dollars in cash right and there's ten billion dollars where the people trying to withdraw their their cash what am I gonna do I just throw up my hands and so continental Illinois was rescued by you know a host of other people one one of one of the entities that it did lots and lots of negotiations with was the Fed but the Fed didn't actually bail him out it was more the FDIC and some other banks and some other regulators how about if we look at an example that's not a United States base so let's go over to the UK Northern Rock and this is a pretty standard one the banks investment profile looked a lot like layman large-scale sale of mortgage-backed securities but what it did is that instead of financing with customer deposits or something relatively low risk it borrowed heavily in the international money markets and capital markets particularly with this interbank interbank market and so when the bubble burst all of their funding channels dried up the UK authorities came in tried to assure but this was not gonna work and so there was a huge capital injection and then then there was public ownership of Northern Rock so here's probably a good slide to memorize about lessons from funding liquidity risk and identifying and quantifying and managing that risk yeah reliance on short-term loans is probably not a good strategy too big to fail can have unintended consequences as we're seeing today we saw this in 2007 and we're seeing it even up to the late 2000 and teens right rumors alone can bring down even the biggest banks of course this is what we're talking about here in all these chapters in the frm program what we're trying to do is figure out what kind of stress testing can we do so that financial institutions are able to handle these low probability events that have high potential losses notice in the bullet point that's that's bolded there avoid high leverage why I tell my students all the times about the beauty of leverage and the ugliness of leverage now of course the duration of our assets and the duration of our liabilities you know we do this whole thing about the recent duration matching and one of the easiest ways to duration matches to just have short-term maturity on their assets and then it's always good to have an emergency liquidity cushion this is one thing that I do in my family and I try to teach my children that boy let's have some emergency cash available for whatever it is that we need and that's can be applied to what goes on in financial institutions let's take a look at some hedging strategies you know so this is what we're doing right we've identified the risk we've quantified and now we're in the point of managing the risk so how do we manage the risk we did this in in another chapter where we said something like well we can just accept the risk or we can try to transfer the risk so that's what we're doing with hedging here but in order to hedge efficiently and effectively the hedgers have to have access to all of relevant information and tools like financial reports market data analytical software you guys know that I've mentioned those Bloomberg terminals that we have access to in our school have to make a choice between static hedging and dynamic hedging remember static hedging is just an example in which we know what this risk is today and then we take a position and then we just wait until the end and we figure it's going to work out in the end but that sometimes works but a lot of times it doesn't so we need to be aware of all the possibilities of dynamic head and that's why things like the black-scholes mert mert an option pricing model with its computation of delta r so extremely helpful to execute these dynamic hedges and then of course we need to decide are we hedging for one day or one week or one month keeping in mind that there are there are implications for the financial statements and there are implications for our tax liabilities now here's an example of a hedging strategy this German firm metal gesellschaft this firm goes back mil by 120 years 130 years and it was really just you know kind of a mining company but over the years you know whenever you have a commodity what did I do with the price of oil you know price of oil goes up and down you know so the price of commodities do that and so any good manager in a commodity firm is going to be aware of the value of derivative securities and so what this what this company did you know they're known as mg throughout the world what they did was they tried to insulate from the volatility associated with these commodities in in particular the price of petroleum and this is what mg would do it would agree to sell at a fixed price in some certain year you know 1991-1992 whenever that was a certain amount of petroleum every month for a long term period all right so these are forward contracts now what it did to manage that risk was to use the near term futures contract to go ahead and make certain that they were able to handle and honor their original contract and so as long as the futures price was above the spot price we call that backwardation in futures markets then there was a profit to be made an mg made a profit look at that bottom Circle point oh my gosh $3 and $5 for every contract this often this sounds an awful lot like free money right but what did we what did we just hear what did we just hear about the price of petroleum for previous examples boy we know that it fell sometimes it during the early 90s and so this is exactly what happened with mg what it was doing was this stack and rule hedge that it would now remember it's doing this for its it's writing these forward contracts for let's say a ten year period and it's it's hedging them with short-term futures contracts so maybe this is a month out or maybe it's two months or three months out so what it would do is would take a position in those let's say a three month contract and then what it would do is it would roll that over but it would purchase fewer and fewer number of contracts so what you should be thinking is that you know Jim this sounds an awful lot like there's going to be some timing problems right we're making this we're making this long-term commitment and we're doing it by stacking these futures contracts in the relatively short time period now of course over the life of this hedge these cash flows would have probably cancelled out but but if you're losing lots and lots of money today in the short term in the futures contracts and remember these contracts are marked to market they're going to be forced out of their positions that's what I have in the bottom and the bottom block point forced to cash out its positions incurring a loss of 1.5 billion in the process oh sorry about that and so as long as as long as the futures price was above so what does that mean the futures price means that is that somebody's willing to pay for storage right that cash-and-carry model in the futures market but if the futures price goes below that spot price so to speak in this case well then there's that's called contango and then there's a big problem and that's what really caused the mg's losses I learned about model risk back when I was in the stages of writing my dissertation when I collected data for about oh I don't know fifteen or eighteen hundred firms what I did was I use some good data and I estimated a stock's beta and I did it over several different time periods and I went to my professor and I said you know if I use a six month window or a three year window I get different results and he said welcome to the world of model risk right you know so what do you have their model risk in red so look at that top left inaccurate parameters inaccurate procedure that probably wasn't that probably wasn't my problem wrong assumptions that might have been my problem you know back when I was in graduate school and let's say 1990 you know the Chicago Theory was prevalent among all institutions that hey you know capital asset pricing model this was the holy grail that beta was able to capture all that kinds of risk normal distributions all that kind of stuff erroneous interpretation I probably didn't I probably didn't suffer from that but let's go ahead and combine all those under model risks so I would I would remember all these inaccurate parameters erroneous interpretation now of course with all the training that you have reading these trapped chapters you're not probably not going to have that erroneous interpretation procedure is probably going to be okay we need to worry about that as well wrong assumptions but as you'll see here in some of these examples all these combined whether it's just one or two or three here all four of them can really result in some problems how about this neater Hoffer put option model risk example so this guy Viktor he has successful hedge fund in the 1990s and what he would do is he would write uncovered which means he didn't own the share of stock out of the money put options on the S&P 500 index all right so let's just suppose the S&P 500 index is let's say 3,000 and let's suppose that we write this out of the money put option with an exercise price of I don't know 2800 or 2900 and what are we going to do since it's an out of the money maybe even a deep out of the money put option that that premium that we're going to raise is not very substantial right but what we're going to do is we write lots and lots of these so that we can accumulate and we can raise some incomes so that we can invest that somewhere else all right look at that first circle point and we talked about this in other chapters with you know value at risk and expected shortfall and all that other good stuff that we have talked about the overriding assumption underlying the strategy is that a one-day market decline of more than 5% would be extremely rare so look at the date of course this happens of course October Sept 27th 1997 I mean I don't remember that date but I'm sure a victor does the market plummeted 7% so what's gonna happen on those if you own that option and that option is expiring on October 27th or even the 28th you're gonna show up at Victor's door and say hey I want to buy the S&P at what did I say 2800 and maybe it's maybe it's 2600 or whatever that is so of course of course since these were uncovered Victor was forced to wipe out all his cash reserves including his personal savings boy that sounds painful doesn't it here's one of my favorite examples mostly because the Board of Directors was made up of Myron Scholes and Robert Merton of course you guys know them as part of the black Scholes Merton option pricing model which won the Nobel Prize in Economic Sciences in 1997 for Scholz and Merton of course Fischer black would have been a part of that had he still been alive but he was not now of course this hedge fund generated lots and lots of profits in its derivative trading look at that second block point there 42% 48% 17% in the mid-1990s and of course this is this is after are you ready for this this is after her their management fee which was you know around 20% so I mean this is really really doing extremely well oh boy let's take a deep breath now or let's go back to the to this black Scholes Merton option pricing model and remember it needs five variables stock price which is easy to get exercise price which is easy to determine time to expiration which is easy to obtain the risk-free rate of interest all we got to do is look in the Treasury market that's easy and then the standard deviation of the underlying asset alright so how do we get that thing well the easiest way to get it is using historical data let me go back to the last oh I don't know 5 weeks 10 weeks 20 weeks 50 weeks let me compute that daily standard deviation and make the assumption that oh the standard deviation is stable over time and so that's going to apply to the future so let's go back let me just go back here oh my goodness gracious wrong assumptions inaccurate all right so what happens here so of course this historical model which was based on you know let's say a normal distribution or maybe a nearly normal distribution and so there's no big fat tail heavy tail on that left side that we talked about in Value at Risk and so of course of course one of those events occurred and they had losses in the three or four billion dollar range fed Bank of New York had to step in as well as 15 commercial banks to save it all right so what are we going to summarize here reliance on historical models over reliance low frequency high severity events were uncorrelated over time all of these trading strategies are relied on the assumption that the risk premiums and the market volatility would ultimately decline of course that didn't happen died so how do we avoid this in the future large-scale stress testing I mentioned that just a few slides ago how about an initial margin and then potential liquidation costs initial margin of derivative contracts should always be enforced that sounds an awful lot to me like effective and efficient regulations about the London whale with with model risk here there was this guy named Bruno Iksil a synthetic credit portfolio trader what does that mean let's go back to our awareness and our knowledge of put-call parity remember we said that the stock price and the put price must be equal to the call price plus the present value of the risk feet risk-free bond price now what you can do is you can take those and move them on one side or the other of the equal sign and you can synthetically create an option a share of stock or a treasury bill and so what JP Morgan Chase tried to do was they wanted to invest their excess deposits of the bank what this Excel guy did was that he constructed a synthetic credit portfolio using put-call parity among other models to protect the bank against adverse credit scenarios in particular widening credit spreads and essentially what happened was that this looked an awful lot like a basket of credit default swaps and these credit default swaps were written not most not mostly or mainly on individual issues but on stock market indexes now of course in every scenario this works really really well but but over time what's gonna happen oh my goodness gracious so what happened was that in December of 2011 the management decided to reduce the exposure and its risk-weighted assets following a more positive outlook of the economy and so instead of unwinding these positions which would have produced a little bit of a loss they launched the strategy to try to recover those losses and then turn them into gains and so this strategy ended up increasing not only at the portfolio size it increased the risk and increase the risk weighted assets so this net long position eliminated this hedging protections of the original SCP and so this really reduced their ability to exit their markets without suffering losses so here here's kind of a summary here well we're really subject to these rules of marketing to market but what we can do is we can set the marks that were at significant variance to the midpoints of those dealer quotes in the market and so what we're going to do is this London whale painted a much rosier position and a much rosier picture than the actual real-time marking to market would have shown on the books so there was really no availability to prepare those daily reports there was no risk committee meetings and they were able to engage in speculative and risky trades speaking of risky trades we talked about bearings Bank at some point in our life Nick Leeson the British trader boy what happened was that not only did bearings say to Nick hey you know what we want you to be in charge of all of our trading we're going to allow you to monitor yourself and so he essentially had the ability to create dummy accounts where he could put his losses into those accounts and prevent them from being seen by anybody else now f course you can only put so may losses into a special purpose vehicle until somebody asked the question hey what are all those things in there and that ultimately led to bankruptcy so look at the circle points I have at the bottom little management oversight no clear demarcation of roles and responsibilities and and since Nick was in charge of the settlement operations he could hide his trading losses from the main office how about the two examples of Procter & Gamble and Gibson greeting cards Proctor example and Gamble still exist today Gibson greeting cards was taken over by American Greetings after this debacle but essentially what happened here was that both Procter & Gamble and Gibson were trying to manage their interest rate risk on a fixed-rate short relatively short term note in Procter & Gamble's case they were trying to hedge about forty million dollars worth of an interest payment it was a fixed payment so what they wanted was to be able to if interest rates fell they wanted to be able to pay a lower rate Gibson Greetings was in a similar case but their their amount wasn't even 40 million I think it was 20 or 25 million so they called Bankers Trust and Bankers Trust said yeah sure we can we can create a derivative security for you it was actually a swap contract and you know the chapter doesn't really do this just just this example justice you know with a plain vanilla interest rate swap know the swap the pay or the receive fixed or floating you know the difference is between a fixed rate let's say 5% and a floating rate let's just say LIBOR and so if LIBOR is eight then you know there's gonna be a three payment made between those two parties right it's pretty simple to figure out how well and these complex derivatives really I mean this is what Bankers Trust did they made the payment depending on the difference between the yield on risk-free security and the price of some other kind of security so it wasn't apples to apples it was a yield interest rate and and dollars of price and so of course both of these companies could only lose if if the window of interest rates fell within some small narrow range at the you know at the date of the swap payments and of course both of these happened both of these occurred and Procter & Gamble ended up losing about 400 million dollars I mean silly is that 400 million of losses trying to hedge 40 million dollars worth of an interest payment so they sued Bankers Trust Bankers Trust sued back Procter and Gamble and Gibson and it was a whole brouhaha in in these markets and several executives you know lost their jobs and we're forced to put forced to pay some fines but this goes back to let me just swing back here quickly this goes back to identifying the risk okay so neither Procter & Gamble or Gibson could identified the risk quantifying the rest they didn't do that at all now they tried to manage the risk without really having knowledge of the identifying the risk and the quantifying the risk and so of course that's not gonna work remember identify quantify manage those three those three go hand-in-hand all right how about a municipality I'm guessing you guys have heard about this at some point in your life Orange County case illustrates how complex financial products can ruin a municipality yeah now look at this so this fund that was run by the county treasurer Robert citron had about seven or eight billion dollars in equity but this I managed to borrow about 13 billion using the repo market and complex inverse floating rate notes now Robert was betting that interest rates would fall but he would also be relatively profitable if interest rates stayed low now of course what happened in 1994 the Fed announced a hike in interest rates 250 basis points oh my gosh this resulted in a loss of 1.5 billion dollars this was at the time the biggest loss in a municipality and it might be even today but that you know Jackson County Mississippi might may have may have surpassed that how about this Sox and Landis Bank way back in the 2007 financial crisis let's go back to this time period 2004 2005 so you have these US banks who were not only underwriting but then distributing and packaging these mortgage loans and they needed to sell them right and so they were willing to sell them to anybody didn't have to be US investors and so there were lots and lots of financial institutions throughout the world who loved buying these mortgage bank securities in particular the subprime securities because they had a much higher rating than they should have had and and so life in that subprime market was ripe for these European banks and other banks throughout the world including banks in Iceland to buy these mortgage-backed securities now the problem with this Landis Bank was that it opened a network network of what it called conduits and what it did these conduits raised money through the sale of short-term debt so imagining issuing a relatively short term security like a six-month bill or maybe a two-year note taking the proceeds and then investing in these prime securities over in the US I mean this sounds like a great way to diversify the portfolio however of course when the subprime subprime crisis hit us in 2007 the value of those mortgage-backed securities in particular those subprime subprime securities they fell dramatically and this bank had to be sold to another financial institution how about reputational risk there are lots and lots of examples the textbook talks about Volkswagen uh you know and in today's headlines I'm thinking of Boeing but what Volkswagen did back in you know 2008 2009 they did this for several years is that they wanted to avoid being able to have their vehicles detected for pollution here in the United States and particularly Pennsylvania I got to take my car every year to get an emissions test and so they you know run my car and they do some kind of emissions test and I'm not going to sure how it's how it works because we've had a car for a bunch of years and it passes every time so maybe they're making cars better these days of course we don't have a Volkswagen but the the issue is that what Volkswagen did is they intentionally intentionally programmed their computer inside of their cars to reduce the emission when it was tested so they could automatically produce an output that is Oh what does that say they're 40 times more nitrogen oxide I have no idea what that thing is but it sounds to me like we don't want lots of that in the atmosphere now 2014 engineers in the United States carried out these live road tests I remember seeing these on television and and then that scheme was unearthed so this was untold damage to the Volkswagen brand name you know I teach my students in in my corporate finance classes you know the goal of business is to maximize shareholder wealth how do firms do that they do that by branding their product and so those when those product lines become under fire because of reputation then look at this the share price fell by over a third and then they face lots and lots of fines and penalties those of you looking for a more recent example just get up to date on Boeing read The Wall Street Journal one of my favorite stories about corporate governance since my dissertation was on corporate governance was was Enron now of course I wrote my dissertation in 1993 and Enron didn't didn't have its problems for about ten years later but you think about think about this Houston based company which was really an energy firm and like any good energy firm it realized the need to manage its commodity price risk by using just simple oil futures contracts and so over time over time what Enron realized was that it could make way more money by trading futures contracts and other kinds of derivative securities then it could by going out and digging up oil in in the field now of course this all works as long as as long as those oil futures positions are in line with the promised branding and corporate governance issues that is part of Enron's business model but as soon as those kind of decouple then there's going to be a problem and this is what happened and so Enron started losing lots and lots of money in its derivative contracts and it started hiding those into other places and Enron really was the first time that I learned of the value and the pain of the special purpose vehicles I bet you guys remember reading about Ken Lay and also one of the interesting stories about Enron is that I mean these people look at the quotes there let me go ahead and read that falsifying Enron's publicly reported financial results and making false and misleading publicly representations about Enron's business performance and financial conditions it failed to fulfill its fiduciary duties to shareholders and and it did this you know you know maybe with the help of Arthur Andersen among others but the interesting story here is that you know some of these people went to jail but there was a couple a married couple who was all part of this and the judge was very lenient in determining the timing of their sentences he allowed the wife to go to jail for a certain amount of time and then when she got out of jail she let the husband go to jail because they had some children how about this Swift case how swiftest the secure electronic platform that transfers funds between an among 11 thousand financial institutions all right so what are we expecting we're expecting that this network would be secure but of course an attack on a central bank that led to the loss of 81 million dollars exposed to serious weakness in the system you know I don't really know a lot about malware but there was planted in there and and this you know really decimated the system reputational risk etc etc and I think that takes us through these financial disasters we covered every one of those cases good luck in memorizing all the important details about those cases but also try to remember and try to be able to apply what that means for a fictional case on 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