Dark liquidity pools

Real integrity is doing the right thing, knowing that nobody’s going to know whether you did it or not. -  Oprah Winfrey


What are Dark Pools:
Dark pools are internal crossing networks wherein, typically large orders from traders are crossed internally without routing them to the exchange. The prime feature of dark pools is the anonymity they provide with respect to the identity of the trader, the trading price as well as the liquidity.


What is the need for them:
Large institutional investors generally follow sohisticated strategies which may require them to buy/sell large proportions of a stock at short notices. These investors are vary of trading such large quantities because of the market impact they bring about whihc may shift the prices against them. Tradionally, Quants were employeed to come up with complicated strategies of loading/unloading such large quantites by buying and selling in equity as well as derivative markets for that security. However, these methods were soon found not to be sustainable for a long time. This need for an alternate trading facility other than the exchange gave rise to the concept of "dark pools". This allows traders to trade large quantites without having these trades shown up on the exchange side order books. This in turn guarantees no market impact of such trades.

These pools are owned by private brokers like Morgan  Stanley, Goldman Sachs etc. They may also be shared across multiple brokerage houses.
Dark pools are recorded to the national consolidated tape.  However, they are recorded as over-the-counter transactions.  Therefore detailed information about the volumes and types of transactions is left to the crossing network to report to clients if they desire and are contractually obligated. Dark Pool transactions are recorded on the exchanges' consolidated tape. The SEC, and anyone else that wants to purchase the consolidated tape, has some limited visibility into those transactions.

Advantages:

1. Alternate trading systems (ATS), as they are refered by, provide internal crossing of client orders without having to route them to the exchange. This saves execution and routing time. Further, it provides anonymity by not having these orders shown up in the exchange order books.
2. Unlike exchange, where such large orders might  be sliced and routed to different destinations, and executed are possibly different prices, these dark pools can provide a fixed execution price (often midpoint of ap and bp) since they dont need to slice the order, entire chunk can be executed at same price often.
3. They offer high levels of liquidity for trading stocks. Sometimes, traders might find it difficult to trade less liquid stocks in open exchanges. So they may opt for dark pools in such cases.
4. Low transaction fees. This is because, unlike exchanges wherein these costs might be incurred on each slice, the costs for dark pools is much lesser.

Disadvantages/Limitations:

1. These dark pools are only accessible via electronic trading.
2. There is a limitation on the total volume of trading done  on a particular stock that can pass through dark pools.
3. Since they hide the order level information from outside world, one cannot guarantee that the prices one sees on the exchange are indeed the real prices of that stock. Now, although this might not affect retail investors because they can still trade at the price available in exchange relative to the price they expect, as long as both of them are in sync. However, the regulators who are looking to keep the prices of a stock as close to its fundamentals as possible would have problems with this. 

Dark algorithms:
Given thast algorithmic trading is only a sub partt of electronic trading, algorithms will eventually become an integral part of managing liquidity in the markets. Brokers often try to find best possible pools for execution of client order to ensure best prices. For this, they slice and route the orders to different pools based on their availability. Any leftover volume is broken up and placed back across the pools, with the result that the buy-side firm’s order is executed efficiently with minimised risk of overexposure or missed opportunities, and without the firm having to manage the multiple venues itself. As such, traders these days are trying to cook algorithms which expose minimum information of the trade to the external world. Then there are algorithms who try to  find hidden liquidity by tracking such dark pools and efficiently manage the order execution by routing the  orders to the best pool. Credit Suisse's Guerilla and Sniper are two such dark algorithms. This is a snippet of an interview of Toby Bayliss, Citi posted on ftmandate.com about the use of algorithms in dark pool trading -

FTM: The need for gaining access to the growing number of dark pools of liquidity has resulted in the development of new liquidity-seeking algorithms. How do they work?

TB: Smart order routers work by having an understanding of each of the liquidity pools and how they work and interact with flow. Each venue differentiates itself by having its own methodology for interacting with the actual flow that is there. Negotiation takes place in different ways. A smart order router needs to understand each and have access to all of them.

Depending on the aggressiveness of your liquidity-seeking algorithm, it may seek only to interact with dark liquidity –sending in hidden order types to see if they are matched on the other side. To get more aggressive, you can interact with order book flow or you can post liquidity on certain venues.

FTM: And what are the challenges for sell-side firms trying to differentiate themselves in this area?

TB: It really comes down to speed, which is crucial; knowledge of where things are trading; and building up that knowledge over time. Because speed is important, you want to target venues where you have seen liquidity in the past or where liquidity exists at the moment. Because there are so many venues you can only ping so many at a time before you have to prioritise which will target. Pinging is sending the minimum size order for that venue and seeing if you get a fill. The actual knowledge side is also crucial. At Citigroup we own Lava, which has the concept of a dark book. This effectively allows clients to enter the full order size even if they only send a small part to market. Lava has the knowledge that a large order exists so it can match large buyers and sellers of large blocks. It gives you a higher probability of finding the other side for your whole order size.

Trading strategy: Sniffer programs

It is double pleasure to deceive the deceiver

Its been quite sometime since algorithmic trading was introduced on the street and ever since that, these sharp, cutting-edge algo's have been craftily used to beat their mortal counterparts. Now, we have now reached a stage wherein one algo is made to beat another algorithm. This is called "gaming". Lets call these cool next-gen algos as bots (it adds a true gaming feel ;-) ). These bots are basically used to perform only one function extremely well, track down other algos and either use this information to go along with the tide and make profit or work against these algorithms' strategies. These bots are called sniffers. They listen on the network traffic and try to detect patterns in the orders sent. A very basic example of what a sniffer does was explained in my previous post on High Frequency trading in strategy number 3. It explains how HFT traders used sniffers to exploit the naive algos and book profits by buying at a less price and selling at high price to these algos a fraction of a second faster.

Trading strategy: Iceberg orders

False face must hide what the false heart doth know

(Macbeth Act I Scene VII)

Sometimes a client may wish to write off a certain stock off his books, which he had heavily invested in. Such a client may call up his broker and ask to sell say 1M shares of IBM. Now, the most primitive approach the broker could have used is to just place one large sell order with a quantity of 1M (market or limit based on client preferences). However, the disadvantage of doing this is that, if some trader X is actively trading IBM and he suddenly sees this huge order, he may have a doubt that someones got some insider information on IBM stock falling down and is trying to write it off. So he too may follow suit and this may in turn trigger a chain of such transactions. The law of demand-supply commands that IBM stock price will fall in this scenario. This eccentric price shift was not backed by any fundamentals but instead just by the whim of a few traders thinking that IBM is going to fall. So in all this, our poor client who wished to write off IBM is left to face the brunt of selling at a low price. To avoid such situations, his broker may chose to just show a small part of the total order onto the market (before its execution) and hide the rest. Such an order is like an iceberg, tip above the water surface, rest is deep down the water. Thus its called as an "Iceberg order". The broker will slice and dice the original order and time it using some algorithm like VWAP, TWAP etc. Slicing is done to disintegrate the original order while timing is done to make sure it gets the best execution price. The client may specify some slicing parameters like -

• Total size of the order
• Maximum size to be displayed at any given point (know as "Disclosed quantity")
• Actual "Displayed quantity" (this is NOT specified by client but instead calculated based upon the executions received on the "Disclosed quantity".

Note that some exchanges support Iceberg orders by themselves while some dont. For the latter types, the brokers (Investment banks) use sophisticated proprietary algorithms and infrastructure to simulate this.

Operational semantics:
Consider this hypothetical case:

• Iceberg order - total-qty=300 , Disclosed-qty=50
• Two other pure limit orders O1, O2 - qty=50, price=100
• One pure limit order O3 - qty=50, price=99.5

Lets say the broker determines that he should break up the Iceberg into 2 "visible" slices of 50 each(V1, V2) and 4 "invisible" slices of 50 each.  (I1,  I2, I3, I4)
Initially, the broker sends first visible slice. This gets queued up on the exchange as per the price/time priority rules. This order is seen to outside world. This new order will then enter below the already existing pure limit orders queued up on the exchange. A new visible order is sent only when the previous one is completely executed.No invisible slice is sent till ALL visible slices get completely executed. Once all visible slices are executed, invisible slices assume positions below the already existing pure limit order of same qty. So in our case, at three discrete time intervals t1 < t2 < t3, we might have a possible scenario as -
                                    t1: V1, O1, O2
                                    t2: O1, O2, V2, O3
                                    t3: O3, I1, I2, I3, I4
(above listing is in decreasing priority from left to right)
Obviously, the client would wish his total order to get executed ASAP. The repeated lessening of priority on each new slice of the iceberg order, may act as an impediment to this. So brokers often try to somehow ensure that new slices do not enter at low priority, by techniques like revising the "Disclosed quantity" and thus amending a previously partially filled visible slice with an increased quantity or something like that. This ensures minimum delay between different slices of the same iceberg order. Note that brokers may also route these slices to different exchanges in order to bring more randomness to the identity of the original iceberg order, to avoid getting tracked down by sniffers.

To summarize..
Icebergs offer - 

1. Ability to reduce market impact on the prices of the stock.
2. Control the liquidity in the market.
3. Convenience to the original client, by not having him to manually slice-and-dice, but instead having a formalised notion of an iceberg order.
   

Buy side firms and Sell side firms

I have been reading about financial markets for a while, and often encountered this terminology of "buy-side and sell-side firms". Earlier, I was too lazy to know what it really meant and just overlooked it, since it never became a road block to my overall understanding of the actual topic i would be reading :-P Anyways, here it is, finally! (directly lifted from Wiki Answers)

On Wall Street, "buy side" refers to firms that invest money or 'buy' securities and "sell side" refers to the investment banks that provide the buy side firms with products and services such as initial public offerings (IPO's), secondary offerings, trading, research, conferences, etc. The "sell side" firms are 'selling' IPO's and services to the buy side firms.
Examples of buy side firms would be large mutual fund companies like Fidelity or T Rowe Price. Examples of sell side firms would be investment banks like Goldman Sachs, Morgan Stanley, etc.
Most of the large investment banks also have small buy side operations that are run separately from the larger sell side. For example, you can buy a mutual fund from Morgan Stanley or Merrill Lynch, but this isn't where these firms make most of their money.

High Frequency Trading

The speed of communication is wondrous to behold
 
I guess this is the new buzz word on the street these days, after "recession", "financial breakdown" et al especially after Goldman Sachs piled on millions of dollars using it early this year. To capture the essence of this term in brief, lets say high frequency trading is all to do with speed. To me, it seems to have added a new dimension, a rather more domineering one, to the equation of making money- The faster you can trade, the more money you can make. Can it get any simpler to comprehend? With the advent of computers around 1980, the notion of algorithmic trading started taking shape. As explained in my previous post on "Market Makers" , these algorithmic traders started assuming the roles of market makers too. With this new role, came a few added provisions and thus new ways to make money. These set of algorithmic traders became distinguished in their approach of trading and gave rise to what we now know as - High Frequency Trading (HFT). In this, these traders take advantage of their favourable position  (exchange rebates, high speed connectivity to market and so on) to craft new trading strategies in which the basis is to buy and sell at very small intervals, very quickly! This new strategy was reinforced by the exchanges reducing the tick size (minimum value by which price of stock can move) from 1/16 to 0.01. This allowed HFT traders to quote bid and ask prices with  minimum spreads to increase their profits.
A few of the strategies used by these HFT traders are as follows:-

1. The first one involves exploiting the rebates given by the exchange to these market makers (aka HFT traders) on each transaction due to their "noble deed" of providing liquidity to the market. Suppose a institutional trader wants to buy 100 stocks of IBM at $20. He is using algorithm engines for firing his orders into the market. These algorithms often slice the orders while releasing them ot market using strategies like VWAP, TWAP etc to get the best deal. Now, while they are doing this, a HFT trading program is sniffing on these orders. Lets say, the institutional trader got  fill for 10 orders at $20. Then later, he got a fill for another 15 at $20. using this data, the HFT program can track that these orders are coming from an algorithmic engine and quickly place a bid on 75 IBM at @20.01. This will cause the HFT program to get the next fills instead of the algorithmic engine because HFT is willing to buy at a higher price. Now, since HFT  knows that the algo engine is waiting fill its remaining 75 IBM at $20, it will sell it 75 IBM at $20.01 (yes, it didnt try to hole a spread here, indeed). Now, what we see is, the HFT program simply bought IBM and sold to algo engine at same price without making any profit on spread. We call these dead transactions. But even on these dead transactions, the exchange gives market makers (HFT's) a rebate of 1/4th penny. So, for two dead transactions, HFT got a 1/2 penny profit while the algo engine was made to pay a penny more. This 1/2 penny scaled up to million dead transactions will be quite some huh.
2. The second one is what is called predatory algorithms. In simple terms, think of these as algorithms which "feed" on other more innocent algorithms. Typically, a tolerable  range of execution prices is fed into algorithmic engines rather than  one stringent limiting value. These engine then post bid/ask prices in increments of ticker. So if one engine bids for 21.1 and second bids for 21.2, then first one  raises its bid to 21.3 until it reaches its limit. The predatory algorithms take advantage of this behaviour. In our e.g., lets say the institutional traders algorithm has a range of $20 - $20.9. So when it makes few initial bids and gets fills, the HFT program sniffs on it as before and tracks it down. Now, next step is to post competitive values against the  algo engine. So HFT program  will bid for $20.1. Seeing this, the algo engine bids for $20.2 and this continues till the HFT program stretches the algo engine bid till its limit (or close to it). At that point, it withdraws its bid, and instead goes ahead and sells short to the  algo engine at $20.9. Now since the HFT program knows that this new high value is just artificial and is soon going to come down, it will wait until it does. Once prices come down, HFT program will buy  from market at low price and make delivery to the algo engine for  the short sell it made before. And the net spread is pocketed by the HFT program.
3. In the third strategy, the HFT program makes use of the fact that its been allowed to act as a market maker. In this, like before, institutional trader wants to buy at $20. however, his limit is say till $20.9. like before, the HFT will track the algo engine down. Once that is done, its job now is to find the limiting price of the algo engine. For this, it will ping to the algo engine. It does this by sending small namesake orders at varying prices. It will first send an order of say 1 lot at $21.1. Nothing happens so it cancels the order and places another at $21. Again nothing happens, so it cancels it and places another at $20.9. At this, the algo engine will hit a trade with HFT. So now, HFT knows the limiting value of the algo engine. It then immediately makes a bid a tick higher than original bid of algo engine i.e. it will make a bid for $20.1 and pick up all the shares from market. It then wastes no time before it posts an  ask price of $20.9 on its 100 shares, which in high  probability will be bought by the  algo engine. Thus, as before, HFT pocketed the spread.
4. The forth strategy is not so much to do with using complex algorithms but more to do with sheer common sense. The exchange typically rents some space in the same location as its own, to algorithmic traders to place their large server racks and operate from there. In return they get a handsome rent for it. Now because of being located in the same location as the exchange, these algorithms get faster connectivity to the market due to shorter length of cables connecting them to the market (!!! honestly :-D). This reduces the latency of information transfer, and these engine thus get market quotes a few hundredths of second faster than others. This is known as co-location. Least to say, they are the first ones to act on the market and make money.
   

Market Makers

God gave us one face, but these guys have two!

When an exchange facilitates trading of a particular security, it has to ensure that there is a fair bit of and a reasonable market available for those securities. For this, it established the concept of market making. In this, the exchange appoints people who's sole job is to ensure enough amount of liquidity in the market for each security. Sometimes, a trader wants to buy a particular stock but  doesn't have opposite party to trade with or vica versa. In such cases, it is the job of market makers to enter both buy and sell side quotes to ensure that such a scenario does not occur. Thus they are "enabling a market" for a security on the exchange. Most of the exchange typically appoint specialists for the job of market making. These specialist firms get some form of rebate from the exchange since they create a market for them. This rebate may include things like giving a quarter of a penny on every share traded, off. Although traditionally, market makers used to be exchange appointed specialist firms (sell-side firms) offlate, even buy-side firms are allowed to use their super computers (read as mean machines!) for this purpose. These "new market makers" may get added provisions from exchange to perform what is called- naked short sell (selling without owning and more imp without even borrowing).
However, above said, market makers are not god-sent-angels to give their services without any personal intent. The way they look to make money in this business is by capturing the spreads between their deals. In simpler terms, if a market maker bids 10.50 for GOOG scrip and asks 10.55 for the same GOOG scrip. Then if he manages to strike a deal at 10.50, then the next thing he does is to sell it off at 10.55 and thus pocket a cool 0.05 per one stock transaction. This, he does for a million times in a day! The risk, as is apparent, over here is that he may not be able to sell the scrip immediately but after some time delay, which may land him in a not-so-good deal. however, with the advent of the mean machines in this business, smart algorithms are used for market making which ensures that they land on the greener side more often. To summarize, market makers help exchange -

1. To ensure enough  liquidity in the market
2. Act as buyers for sellers and vica versa
3. To ensure that the prices of each security is maintained within its fair limits by reducing the market impact of abrupt movements on the price of a stock.

Forward Contract

Be wary then; best safety lies in fear

(Hamlet -- Act I, Sc. III)

Forward Contract:

A forward contract is an agreement between a buyer and a seller in which buyer agrees to buy and seller agrees to sell a certain commodity or security at a certain fixed price in future. The security that is being traded is called as the underlying. Thus, the difference between a forward contract and any ordinary contract is that the actual transaction takes place at some time in future, not immediately. The parties involved in a forward contract are often private parties for e.g. Two financial institutions or a financial institution and its client. There is no involvment from any Exchange in it.

Example: A construction company may enter into a forward contract with a steel manufacturer on 15^th March 2009 to buy a 1000 tons of steel at the price of Rs 1500/tonne on 15^th Dec 2009. It may so happen that the 1000 tons of steel may not even have been produced yet. But there is a contract binding the two parties and the transaction will take place in Dec for sure. By entering into such a contract, both the parties try to protect themselves from the abnormal movements in steel prices. Thus the construction company protects itself from the risk that in Dec, steel prices could be more than 1500/tonne. Conversely, the steel manufacturer protects himself from the risk that in Dec, the steel prices will be lower than 1500/tonne. By doing so, each party is willing to let go of the profit in the event when the prices slide in their favor.

Exchange traded markets and OTCs:

Exchanges have traditionally served as a middleman between two parties wanting to trade. Derivative exchanges have existed for a long time. In 1848, The Chicago board of Trade (CBOT) was established to help farmers and merchants trade for crops and grains. Its initial purpose was to just bring these two parties together and ensure a good quality of grains. Later, such future-type contracts were developed on these grains. (Note: Futures are similar to forwards with small variance which can be neglected for now) People started becoming more interested in trading these contracts rather than trading the actual commodity. Later, in 1919 another derivatives exchange called Chicago Mercantile Exchange (CME) was established.

However, exchanges arenot the only place where people can trade. There is another mechanism called Over The Counter market (OTCs). In this, trading is done over the phone. There is no physical place such as an exchange. Each party may communicate with eachother over telephone lines. These conversations are recorded for authenticity purposes. Trades done in OTCs are much larger than those in Exchange traded markets. The main advantage of OTCs is there is no transaction costs involved with the trading. Also, the terms of the contract can be privately agreed upon by the parties involved rather than being laid down by the exchange. However, the main disadvantage of OTCs is that either of the parties may default or purposfully chose not to honor the contract. Forward contracts are traded in such OTCs.

Spot price and forward price:

In a forward contract mentioned in the example above, the price of steel on 15^th March is known as the spot price and its price on 15^th Dec is known as its forward price. Thus, spot price is the price of the commodity on any given day. Forward price is its execution price. There is a unique relation between these two prices. Forward price is derived from spot price. As delivery approaches, the future price converges to the spot price of the contract. On the day of delivery, the future price will be equal (or almost equal to) the spot price. If forward price is above spot price on day of delivery, traders have a clear opportunity of doing arbitrage as:

1. Buy the asset from market at spot price.
2. Enter into a forward contract to sell the asset (go short)
3. Make the delivery of asset on day of delivery at forward price.

The difference is pocketed thus. Similarly, one can argue for the reverse case.

Terms of a forward contract:

While entering into a forward contract, its becomes obligatory for each party involved to ensure that there stays no tinge of ambiguity in the terms and conditions of the contract. Therefore, following things have to be well defined in any forward contract:-

1. Delivery terms and location
2. Quality specification of the asset
3. Payment methods
4. Dispute resolution procedure
   
5. Contract cancellation procedure
6. How to close the positions. (Since one cannot backout of a forward contract, if one still wants to cancel the contract, he can take an opposite position of same asset on another contract, thus offsetting his previous position)
   
   

Pricing a forward contract:

As explained above, as the delivery date approaches, the forward price should converge to the spot price. However, at the time of writing a forward contract, how does one determine what should be the fair price say 10 months down the line, of that asset. So in our example above, if price of a tonne of steel on 15^th March was Rs. 1300, how do we come to the conclusion that forward price for 15^th Dec should be Rs. 1500. As intuitive as it may seem, forward prices are not determined using any highly sophisticated predictive modelling technique or something. Neither are they based upon the “mutual intuition” of both the parties involved. They are often calculated taking into account the current price (spot price) and the price of “maintaining” the asset for the period of the forward contract. This maintainance encompasses different kinds of consts like:-

1. Transaction charges: Any broker commisions, administrative fees etc.
   
2. Transportation charges at the time of delivering the asset and also when transporting it to the sellers storage facility initially.
3. Rent paid to rent a storage facility if needed.
4. Insurance on the asset for the time its with the seller.
5. The cost of money: In order to deliver steel, the manufacturer might have to take some loan over which he has to pay interest. Interest is also accrued by other posible loans taken for storage etc.
   

This list is not exhaustive. There are multiple other costs involved based on the nature of the asset. So the forward price is: spot price (1300) + Maintainance costs (200) = 1500.

Hedging using forwards:

Lets us consider an example of FOREX markets for illustrating this. A US company X has bought some goods from a Indian supplier for which it knows it has to pay Rs. 100000 after one year. Let current dollor/rupee FX rate be 1/47 i.e. $1 = Rs. 47. However, the company fears the dollor/rupee FX rate might increase i.e. It might have to pay more number of dollars per rupee. So to tackle this uncertainity, it will take a long position on a forward contract with asset as Indian Rupee. (Note: Going short means selling, going long means buying). So it may sign a contract to buy Rs 100000 after one year @ Rs 47.1. So now, it has hedged its position buy buying Rupees at a fixed rate which is lower than its expectation of the FX rate.

Similarly, if a US supplier supplies goods to an Indian company and it expects to receive Rs.100000 after one year, then it may lock its position by going short on rupee i.e. By selling the rupee at a fixed rate of say Rs. 46.92 for every dollor. It will do this because it fears that dollor might weaken against rupee.

Note that primary purpose of hedging is to remove/reduce risk from a transaction. It is not aimed at making profit. Thus, the above companies could have possibly earned more profit if the FX rates had slid in their favor a year later. However, things could also have gone other way round and they could have suffered losses. So to avoid these extreme scenarios, transactions with big turnovers are often locked using forward contracts.

Mortgage

So sweet was ne'er so fatal
(Othello -- Act V, Sc. II)

What is a mortgage?

A mortgage represents a loan taken to purchase a piece of a property, to be repaid over a specific period of time along with some interest. A lender gives a fixed sum called principal to a borrower. In return for this, the borrower will pay some interest over it. Each mortgage is associated with a term which represents the period of time over which the borrower is entitled to pay back the principal + interest to the lender. Thus, a mortgage represents the guarantee given by the borrower to the lender that he will pay back the sum borrrowed. Its similar in concept to a bond, except that, in case of a bond, the principal is paid at the end of the term and interest is what the lender receives periodically. While in case of a mortgage, interest + principal is paid in varying proportions over a fixed period of time, such that the total sum paid at each discrete interval is the same. mortgages are by nature designed to allow lenders to have a fixed amount of money every month.

Example: If you borrowed $100,000 from a lender with an agreement that at the end of 30 years you would repay the original loan amount plus 7%, then your total repayment would be $107,000. This is not how mortgage loans work. When money is loaned for 30 years, the mortgage agreement requires the borrower to make 360 periodic (monthly) payments to the lender. The payments must remain the same each month and fully repay both the interest and principal during the life of the loan. The quoted interest rate of 7.00% per year is compounded 12 times a year, resulting in a monthly rate of 0.58% (which is computed by dividing the note rate by 12).To calculate the interest due for a given month, the monthly rate is multiplied by the current loan balance. If you borrowed $100,000 at 7%, at the end of the first month your interest due would be $100,000 x (0.07 / 12).

The process of recalculating the interest and principal every month is called amortization.

It is shown in following diagram:

The reason why interest paid reduces every month is that, it is calculated on the total aount left to be paid back. Thus, It follows an exponentiontial as shown. To keep the total amount paid same, the principal is adjusted such that, the principal paid is less initially and more later.

New player: Financers

In the early years, around 1920's, big investors used to invest in the T-Bills, Which offer them the advantage of zero risk since they are completely backed by Govt. Also they used to offer a decent rate of interest, until things started being not as rosy as they always were. The interest rates offered on T-bills were guided by the Federal fund rates i.e. The rates at which one bank can lend money to other bank at federal Reserve. This federal fund rate is one of mechanisms used by Fed to regulate the flow of money in the market. If the Fed feels that there is not enough liquidity in the market, it may decrease the Federal fund rate, thus enabling other banks to borrow cheaply and thus lend more aggresively, bringing in liquidity. Conversely, to reduce the flow of money, it may increase the fund rate to make it difficult for banks to borrow, in turn causing lesser lending. Now, a low Fed rate maybe good news for the banks but not so much for our good old mighty lenders, who rely on these rates to fetch them enough interest on their excess capitals. So, when fed lowers the fund rates, like it recently did during last years crisis, (less than 1%!), lenders will say - “No, thank you.” to the ever-reliable T-bills. So they start looking for other avenues, and guess what, there are our poor little prospective homeowners looking to borrow some sum to buy their dream house. However, these lenders dont have direct exposure to these small borrowers. Furthermore, they dont have the expertise to analyse the quality of the borrower, his ability to pay back, to predict the fluctuations in the prices of the homes, factors affecting them and so on. All they have is a mountain of money to lend. So, the next logical step is to find a middleman who can provide such services. There come our greedy banks, who always look to dip in their hands where ever they can. They say, “We will find prospective borrowers for you. You give us the money and we lend it to them. Further, we will also provide you with an array of other intelligent servies. In return, we will charge you a service fee of 1%”. Now the lenders think, “I am getting a cool 8% return from each of my borrower, and i just have to shell out a paltry 1% to the banks. Huh! Not a bad deal”. So there we have our new flow – Borrowers <=> Banks <=> Lenders. The banks think they have found a golden ghoose! They get a cool 1% on every deal just by joining the two ends of the chain. Everyone stays happy and the world is suddenly all green! Now, acting a middleman is not the only thing banks do. (Infact a part of this work is now delegated to local brokers who in-turn meet the borrowers with the banks). These banks actually borrow huge sums from these lenders themselves at a specific rate and in-turn lend it to the homeowners at a higher rate, thus enabling them to earn much more than mere 1% of the stake. Thus, banks write these mortgages onto their own books. However, tides may turn against them anytime, when big daddy Fed decides to increase the fund rates. This makes it costlier for the banks to borrow. However, since the mortgages they offered were for say 30 years at fixed rate of 6%, these banks now find themselves in an ominous position wherein they are borrowing at a higher rate than what they are lending at. And since they are borrowing for a shorter period but lending for a longer period, their expenses rose much faster than their profits. This has, in past, caused many banks to fail. Thus, banks have now become wary of avoiding this scenario and many of them write these mortgages off their books and pass them on to other investors. Just by adding one new player, we see how a simple concept of the bond-like mortgages can be made more structured resulting in exotic mortgages ultimately.

Prepayments and Refinancing

a. Sometimes, the homeowners may find the rates ofthe mortgages too strintgent or maybe the period of repayment to be too less. To counter such situations, homeowners may choose to refinance. Refinancing means - “Right now i owe a mortgage of Rs 100,000 at 6% for 30 years. Since the interest rates are lower outside offlate, let me borrow 100,000 at 5.5% from outside and payback the entire lumpsum at one shot. I can then save 0.5% every month on my payments.” So the homeowner basically refinanced his existing loan with a new loan at more favourable terms. Refinancing can also be undertaken to increase the term of repayment by taking a longer period loan. There are other reasons for refinancing too, like converting a floating-rate mortgage to a fixed-rate one, to avoid the consequences of fluctuating interest rates. However, these details stay out of the scope.

b. House mortgages are generally given for a long duration like 30 years or so. However, not many mortgages last till their maturity as people may shift, die, get married, get promotions etc. And thus changes houses. In such cases, homwowners choose to prepay their loan by paying back all the pending principal. Such prepayments are a bad news for lenders who rely on these monthly payments for maybe some other investment they may have made somewhere else. Thus, they suddenly find themselves in a position where their one source of income has stopped. However, prepayments are not always so bad to our dear lenders. Only if the interest rates are falling, will it be treacherous for the lender. Because now, he is forced to push his money into some avenue which does not offer as much returns as the previous one did. However, if interes rates are rising, then the lenders are more than happy to get their money back and put it elsewhere where they get better interest. Having said this, if the interest rates were indeed rising, then the homwowners aint that stupid to let go of a good mortgage for some other “little bad” mortgage. So, we see that things can often go round in circles, with each previous entity depending upon next entity.

There are several factors leading to prepayments. Infact, whole lot of mathematical models have been built around predicting risk of prepayments. Few of these facotrs are:

1. Interest rates. (as we saw above. Infact, as it may be obvious, refinancing leads to prepayments)
2. Size of mortgages: All other factors remaining same, larger mortgages prepay faster than smaller ones, since they are more often than not, taken out by wealthier individuals who ay get good promotions elsewhere and decide to move on.
3. “assumable” mortgages: If mortgages have a clause that, they can be transfered to a new owner without any change in the terms and conditions of payment, then its called assumable. If a homwowner wants to sell his house anyway, even if interest rates are sky high, then he hasa good option of finding another home buyer who can take on the mortgage from there on, instead of taking a new one at higher rates from market. Thus, prepayment speed of “assumable” mortgages will be lower than “non-assumable” mortgages during high interest rates.
4. Economic strength of region: Regions with very high and very low economic strength both exhibit high speed of prepayments. High, because wealthier individuals can quickly afford to prepay and move on. Low, because there will be large no. Of defaults and since mortgages are often insured, Govt. Will prepay them. (Note that such an insurance on a mortgage backed security is nothing but a Credit Default Swap).

New player: Credit Rating agencies

As seen above, a mortgage lender has quite a bit of research to do before lending the mortgage to some new homeowner. Because, if the borrower defaults, then there is not much the lender can do except for some basic loss recovery which too is not 100% guaranteed. Thus, it becomes mandatory, to have some metric of comparison between different borrowers. Some may have high probability of repaying back while some have their heads hardly above water to even think of repaying back. This is where a Credit Rating agency comes into picture. These are generally govt established/backed agencies whose role is to assign some credit-level to each borrower. This rating determines their ability to pay back. In case of mortgages, it becomes easier for the banks to market these mortgages to the investors if they have some sort of credibility attached to them from a standard agency. These agencies may play two types of roles – 1. They may just give the credit rating for a mortgage thus helping the mortgage originators to sell the mortgage to investors. 2.) they may themselves buy these mortgages and write them into their books (thus acting as middle investors) and then they will sell it to the investors as a package.
Example of such credit rating agencies are: Ginnie mae, Freddie Mac, Fannie Mae.

New player: Investors

Till now,what we have seen is that, banks give mortgages to homwowners. These banks in turn finance their mortgages from big lenders (typically investment banks) who are then said to have “bought these mortgages”. Buy buying these mortgages, they get the interest paid on these mortgages by the home owners every month, after deducting the middleman feels of banks and brokers and credit rating agencies. Now these lenders may have bought thousands of such mortgages. So what they have now is nothing but a pool of mortgages. Now, based upon the credit rating given by the agencies and using some of their own calculations, these lenders will divide these mortgages into three categories broadly namely – safe, bit risky and risky. Since the risky bucket has more probability of defaulting associated with it, it offers the lenders more money per month than the safe buckets. Now, these lenders do not really want to hold these mortgage pools into their accounts. So after doing all the setting and shaping of the mortgage pool, they market it as what we call a CDO – Collateral Debt Obligation. So now, what originated as a mortgage from a homwowner, has taken shape of a well structured security like any other stock in the market. Investors will then buy these CDO slices in varying percentages. These investors pay the lenders in return and using the money they receive, the lenders will payback any loans they might have taken, and finally will have some profit marginto their name. So, ultimately, the money for the mortgage comes from these end investors.

The origin of sub-prime mortgages

When interest rates are low, it becomes easier for banks to borrow cheap credit from Fed. With so much of surplus of credit, the banks go crazy. They go on a mad spree of lending mortgages to anyone and everyone. Further more, if this is not enough, then banks use this credit to get more leverage. In order to understand leverage, consider this example. Clever Joe will buy one box for Rs. 10000 and sell it to another person, for Rs. 11000. Thus he earns a profit of Rs. 1000. However, Crafty Joe will go and buy 100000 more rupees by keeping his Rs 10000 as a collateral. He will then buy 10 boxes for it. He will then sell these 10 boxes for Rs. 110000. he earns a profit of Rs 10000. He will then pay the interest on the Rs 100000 which is Rs. 1000. And the rest 9000 rupees go in his pocket. This is what we call leverage. So by getting more and more leverage, these banks go about giving mortgages to even those who are almost sure of not being able to repay back. This whole businesss was based on a simple assumption that house prices will keep on increasing in the long term. So even if the home owners default, then the banks could sell thier house and recover their money. However, things done stop here. What happened was more and more no. Of people starting defaulting. So, within a locality of say 10 houses, 7 people defaulted. So, the bank decides to sell their house. This is where the demand-supply law comes into picture. More demand for a commodity plummets its prices while less demands causes the prices to increase. So, with so many houses on sale, their prices started falling down. So, the banks suddenly found themselves in a situation where they were left with a battery of abandoned houses which no soul was ready to buy. This is not it. Because of the 7 houses that were forsakened, the price of the remaining 3 houses in the locality also start dropping. So those stable homeowners start to think, “Why the hell are we paying a 100000 Rs mortgage for a house which is not even worth 30000 Rs now!”. So they raise their hands up. Now what this has done is, it has stopped the monthly flows of interests to the lenders and in turn to the investors. Further more, these guys may not even be able to recover back the total principal. So the banks go bust. Now, the investors who is left with a pool of such useless mortgages will try to sell them up to the investors. Investors raise their hands up too. So, the lenders who have already borrowed millions and billions of rupees from other lenders, are not able to pay them back and declare bankruptcy. Noone in the chain is spared. The investors may, in all probability, already holding lot of such mortgage backed securities. So these investors arent able to do anything with these dead securities. So they too get tapped out. Now whos left in the chain? The credit rating agencies. These agencies were ideally formed by the Govt. To give ratings to the mortgages to differentiate between the good ones and the not so good ones. However, since the greed for money spared no single man, these agencies started writing these mortgages onto their books instead of just giving services. So, when the mortgages went down-and-out, these guys too got busted. This is because as opposed to the popular public opinion that these agencies are backed by govt. flows, it wasnt a 100% truth. With the exception of Ginnie Mae, Freddia Mac and Fannie Mae were simply "eligible" for a Fed stimulus. But knowing the nature of toxic assets these agencies had undertaken on their books, Fed just said "Ward off!", and let them go down in the Dec of 2008, in a way headstarting the journey downwards for the rest. So, in this way, we see how one futile assumption that the house prices will always rise, caused the entire system to come down.