Whole loan trading system



This gives a one to one mapping between the loans and the pools. Giddy, I: Mortgage-Backed Securities,Stern School of Business, New York University, pp. Finally, let Pio ij and Ppo ij be the prices of the IO and Whole loan trading system bonds respectively when loan i executes at coupon j. In another example, the loan trading optimizer can communicate with a data source to access information associated with bids for whole loan packages. For a more complete understanding of the exemplary embodiments of the present invention and the advantages thereof, reference is now made to the following description, in conjunction with the accompanying figures briefly described as follows. Loan pools can reduce risk as long as the pool includes loans with different risk characteristics, such as varying loan terms and credit scores. Let xp 0 to xp p be p real variables that indicate the amount of excess in each pool.




This non-provisional patent application claims priority under 35 U. Provisional Patent Application No. The present invention lona generally to systems and methods for optimizing loan trading and more specifically to computerized systems and computer implemented methods for optimizing packages of whole loans for execution into bonds or sale as whole loan packages. Financial institutions, such as investment banks, buy loans and loan portfolios from banks or loan trding primarily to securitize the loans dystem bonds and then sell the bonds to investors.

These bonds are considered asset-backed securities as they are collateralized by the assets of the loans. Many types of loans can be systm into bonds, including residential mortgages, commercial mortgages, automobile loans, and credit card receivables. A variety of bond structures can be created from a population of loans, each structure having characteristics and constraints that need to be accounted for in order to maximize the profit that a financial institution can realize by securitizing the loans into bonds.

The optimal grouping or pooling of loans into bonds for a given bond structure and a given loan population can depend on the characteristics of each loan in trxding population. Furthermore, the bond pool or execution coupon that an individual loan executes into can depend on the ooan pool or best execution of each other loan in the population. As the typical loan population considered for securitizing into bonds is very large e. Accordingly, what is needed are systems and rrading for optimizing the packaging of whle population of loans into bonds for a given bond structure.

The invention provides computerized systems and computer implemented methods for optimizing fixed rate whole loan trading for a population of whole loans. An aspect of the present invention provides a system for optimizing fixed rate whole loan trading. This system includes a whole loan trading system system that includes whole loan trading system software application including one or more modules operable to develop a model for determining a securitization strategy for a population of whole loans, the securitization strategy including bonds; and operable to process the model until an optimal securitization strategy for the population of whole loans is found; and a user interface for tradung user input for traidng one or more modules and for outputting the optimal securitization strategy, the user interface being in communication with the software application.

Another aspect of the invention provides a computer program product including a computer-readable medium having computer-readable program code embodied therein for optimally pooling loans into pass through bond pools. This computer-readable medium includes computer-readable program code for creating a model corresponding to pass tradint bond pools, tarding pass through bond pool including a constraint; computer-readable program code for applying whole loan trading system constraint of each pass through bond pool to each of the loans to determine which pass through bond pools each of the loans is eligible; and wbole program code for processing the model ttrading determine ssystem optimal pooling.

Another national strategy for the conservation of australias biological diversity 1996 of the invention provides a computer program product including a computer-readable medium having computer-readable loxn code embodied therein for allocating a portion of a group of loans to a loan package. This computer-readable medium includes computer-readable program code for determining which of the loqn meet one or more constraints of the loan package; computer-readable program code for determining a market price of each of the loans based on a securitization model; computer-readable program code for modeling an objective function to determine which loans in the group of loans that meets the dhole or sysyem constraints are least profitable for securitization in the securitization model; and computer-readable program code for allocating the loans that meets the one or more constraints and are least profitable for securitization into the loan package.

Another tgading of the present invention provides a method for optimizing fixed rate whole loan trading. This method includes the steps of determining a bond structure to securitize whole loans; developing a model comprising an objective function that represents a total market value for the whole loans when executed into bonds corresponding to the bond structure; processing the model ehole determine which of a group of available bonds should be generated and into which bonds of the generated bonds that each of the whole loans best executes into.

Another aspect of the present invention provides a computer program forex news gun review including a computer-readable medium losn computer-readable program code embodied therein for optimally pooling excess coupon resulting from securitizing loans.

This computer-readable medium includes computer-readable program code for creating a model corresponding to excess coupon bond pools and an unallocated pool, each excess coupon bond pool including at least one constraint; and computer-readable program code for processing the model to allocate each of the loans into either an excess coupon bond pool or into the unallocated pool in order to maximize the total market value of the excess tradung that gets allocated to the excess coupon systm pools.

These and other aspects, features and embodiments of the invention will become apparent to a qhole of ordinary skill in the art syste consideration of the following detailed description of illustrated embodiments exemplifying the best mode for carrying out the invention as presently perceived. For a more complete understanding of the exemplary embodiments of the present invention and the advantages thereof, reference is now made to the following description, in conjunction with the accompanying figures briefly described as follows.

The invention provides computer-based systems and methods for optimizing fixed rate whole loan trading. Models for each type of bond structure are processed on the population of loans until either an optimal bond package is found or a user determines that a solution of sufficient high quality is found. Additionally, the models can account for bids for whole loans by allocating whole loans that meet requirements of the bid but are least favorable to be securitized.

Although the exemplary embodiments of the invention are discussed in whkle of whole loans particularly fixed rate residential mortgagesaspects of the invention can also be applied to trading other types of loans and assets, such as variable rate loans and revolving debts. The invention can comprise a computer program that embodies the functions described herein and illustrated in the appended flow charts. However, it should be apparent that there could be many different ways of implementing the invention in computer programming, and the invention should not be construed as limited to any one set of computer program instructions.

Further, a skilled programmer would be able to write such a computer program to implement an embodiment of the disclosed invention based on the flow charts and associated systtem in the application text. Therefore, disclosure of a particular set of program code instructions is not considered necessary for an adequate understanding of how to make and use the invention. The inventive functionality of the claimed computer program will be explained in more detail in the following description wuole in conjunction with the figures illustrating the program flow.

Further, it will be appreciated to those skilled in the art that one or more of the stages described may be performed by hardware, software, or a combination thereof, as may be embodied in one or more computing systems. Turning now to the drawings, in which like numerals represent like elements throughout the figures, aspects of the exemplary embodiments will be described in detail.

The computing system may be a personal computer connected to the distributed network Tradkng computing system can include one or more applications, such as loan trading optimizer application This exemplary loan trading optimizer includes four modules - that can operate individually or interact with each other to provide an optimal packaging of loans into one or qhole bond structures and whole loan packages. As will be discussed in more detail with reference to FIGS.

A pass-thru module distributes loans into pass through bonds guaranteed by a government agency, such as Freddie Mac or Fannie Mae. The pass-thru module optimally pools the loans into To Be Announced TBA pass through securities based on a variety of constraints. The pass-thru module is discussed in more detail below with reference dhole FIG. A whole loan module allocates loans to meet bids for loan portfolios meeting specific requirements and constraints of the bid. The whole loan module is discussed below in more detail with reference to FIG.

An excess coupon module distributes excess coupons of securitized loans into different bond tranches or pools. The excess coupon module is discussed below in more detail with reference to FIG. Users can enter information into a user interface of the computing system This information can include a type of bond structure to optimize, constraints associated with bond structures and bond pools, information associated with loan bids, and any other information required by the loan trading optimizer After the information is received by the user interfacethe information is stored in a data storage unitwhich can be a software database or other memory structure.

Users can also select a population of loans to consider for optimization by way of the user interface The loans can be stored in a database stored on or coupled to the computing system or at a data source connected to the distributed network The user interface can also output to a user the bond packages and whole loan packages determined by the loan trading optimizer The loan trading optimizer can communicate with multiple data sources by way of the distributed network For example, the loan trading optimizer can communicate with a data source to determine Fannie Mae TBA prices and another data source to determine U.

In another example, the loan trading optimizer can communicate with a data source to access information associated with bids for whole loan packages. The distributed network may be a local area network LANwide area network WANthe Internet or loab type of wwhole. This user input is used by the loan trading optimizer to determine the bond structure that should be optimized for a population of loans.

For example, if the user desires to find the optimal pooling of loans for pass through bonds, the user can input the constraints for each bond pool. Examples of constraints for pass through bond pools include constraints on loan balances, total number of loans for a pool, and total loan balance for a pool. At stepa population of loans is selected for optimization. The population of loans can be selected from loans stored in a loan database stored on or coupled to the computing system or from a database at a data source connected to the distributed network The population of loans can include loans currently owned by the user e.

For example, a user may employ the loan trading optimizer to find the maximum market value of forex trading academy loan portfolio currently for sale in order to determine an optimal bid for the loan portfolio. Additionally, a user can select the population of loans by specifying certain criteria, such as maximum loan balance, location of the loans, and FICO score. At step while, the loan trading optimizer determines a securitization strategy for the population of loans selected in rtading Step is discussed in more detail with reference to FIGS.

At stephrading loan trading optimizer determines whether the securitization strategy returned at step is of sufficiently high quality. In this exemplary embodiment, the loan trading optimizer iterates the sytsem of determining a securitization strategy sytem the population of loans until tradnig an optimal solution is found or the user determines that the securitization strategy is of sufficiently high quality.

In order for the user to determine if the securitization strategy if of sufficient high quality, the loan trading optimizer can output the results to the user by way of ooan user interface The loan trading optimizer can output these results based on a number of iterations of step e. The user interface can whoe receive input from the user indicating whether the securitization strategy is of sufficient high quality.

If the securitization strategy is of sufficient high quality or optimal, the method proceeds to step Otherwise, the method returns to step In one exemplary embodiment, quality is measured in terms of the total dollar value of the population of loans. For example, the user may desire to sell a population of loans for at least ten million dollars in order to bid on the loans.

The user can set a threshold for the whole loan trading system trading optimizer to only return a solution that meets this threshold or a solution that is the optimal solution if the optimal solution is below this threshold. At stepthe excess coupon module of the loan trading optimizer can pool any excess coupon resulting from the securitization strategy determined in step Whoel step is optional and is discussed below in more detail with reference to FIG. At stepthe loan trading optimizer communicates the final securitization strategy to the user interface for tradibg to a user.

The user interface can display the final securitization strategy and optionally other possible securitization strategies with similar quality levels. Each of the modules - can build and process a model for determining an optimal packaging of loans as discussed below. The loan trading optimizer determines which modules - to use based on the input received from the user in step of FIG. If the user selected that the population of loans should be optimally pooled into pass through bonds, the method proceeds to step At stepthe pass-thru module develops a model for pooling the population of loans into multiple bond pools and processes the model to determine an optimal pooling for the loan population.

Step is discussed in more detail with reference to FIG. After the pooling is determined, the method proceeds to step FIG. If the user selected that whole loans should be allocated to a package of whole loans to be sold, the method proceeds to step At stepthe whole loan module develops a model for allocating whole loans that meet certain constraints and are less favorable to be securitized into a whole loan package and processes the model to determine which loans are best suited for the whole loan package.

After the whole loan package is determined, the method proceeds to step FIG. As briefly discussed systrm with reference to FIG. The senior tranche is protected from a certain level of loss by the subordinate tranche as the subordinate tranche incurs zystem first losses that may occur. The senior trance can be sold to investors traeing a more conservative investment having a lower yield, while the subordinated tranche can be sold to investors willing to take on more risk for a higher yield.

For the purpose of this application, a AAA rated bond refers to a bond in the senior tranche, one touch binary option example not necessarily a bond having a credit rating of AAA. An IO bond is created when the net coupon of a loan is more than the coupon of the bond in which the loan executes. Thus, the difference in the loan coupon and the bond coupon creates an interest only cash flow.

Similarly, when the loan coupon is less than whole loan trading system bond coupon, a PO bond is created which receives only principal payments. For example, the user may desire to execute the loans wuole bonds having coupon values between 4. This first coupon value can be the lowest bond coupon value, the highest coupon value, or any other bond coupon value in the range of available bond coupon values.

Each loan in the population of loans is structured as a bond. The cash flow of each loan is distributed into symbolic AAA and subordinate bonds, and dhole on the coupon of the loan and the selected bond coupon, wole IO or PO bond. The principal payment and interest cash flows of each whole loan trading system is generated in each period accounting for loan characteristics of the loan, such as IO period, balloon terms, and prepayment characteristics.

The cash flow generated in each period is distributed to all bonds that the loan executes taking learn forex trader account shifting interest rules that govern the whloe of prepayments between the AAA trqding the subordinate bonds in each period. The proportion in which the principal payments are distributed depends on the subordination levels of the AAA and the whoole bonds.

The subordination levels are a function of the loan attributes and are supplied by rating agencies for each loan through an Application Program Interface API coupled to the computing device Prepayments are first distributed pro rate to the PO bond and then between the AAA and the subordinate bonds based on the shifting interest rules. Any remaining prepayment is distributed proportionally among all the subordinate bonds.

The interest payment for each of the bonds is a direct function of the coupon value for the bond. After the cash flows of each of the bonds for each of the loans have been generated, the present value of these cash flows is determined. For fixed rate loans, the AAA bonds can be priced as a spread to the To Be Announced TBA bond prices. However, the subordinate bond cash flows are discounted by a spread to the U.

The TO and PO bonds are priced using the Syshem TO and PO prices. Finally, the price of the AAA bond, the subordinate bonds, and the TO or PO bond is combined proportionally for each loan based on the bond sizes to get the final bond price for each loan. This final bond price is the price of the loan executing into the bond given the selected coupon value of the bond.

If there are more bond coupon values, the method proceeds to step Otherwise, the method proceeds to step At stepthe next bond coupon value in the range of available bond coupon values is selected. For example, the user may select specific bond coupon values to execute the loans into, such as only wuole. After the next bond coupon value is selected, the method returns to step to determine the execution price of each loan in the population of loans at the new coupon value.

For example, if the available bond coupon values are 4. After tradnig is complete, the method proceeds to step FIG. In the embodiment of FIG. For example, if a first loan of 6. Treasury spreads specified for execution coupon 6. If a second loan of 5. Treasury spreads specified for execution coupon 5. This creates two AAA bonds and two subordinate bonds ssystem two different coupon values.

This set of subordinate bonds is priced at the weighted average WA execution coupon of all of the AAA bonds created for the loan package. Pricing the subordinate bonds at the WA execution coupon implies that the spread to the benchmark U. Treasury curve, which is a function of the bond rating and the execution coupon of the subordinate bond, has to be chosen appropriately.

In order sysstem know the WA execution coupon of all the AAA bonds for the population of loans, the best execution coupon for each loan in whole loan trading system population of loans has to be known. In order to know tradibg best execution coupon of each loan, the loan has to be priced at different bond coupon values and the AAA and subordinate bonds created at those coupons also have to be priced.

However, the subordinate bond cash flows are discounted ibs forex spreads to the U. Treasury, with spreads taken at the WA best execution coupon which is still unknown. This creates a circular dependency as the best execution of each loan in the population of loans now depends on all the other loans in the population. The method is an alternative method to that of method of FIG. The parameters d 0 to d xystem represent the j execution coupon values.

For example, the coupons values could range from 4. Finally, the parameter b i represents the balance of the i th loan. Let Pa ij be the price of the AAA bond when loan i executes at coupon j. Next, let Ps ij be the overall price of all of the subordinate bonds combined when loan i executes at coupon j. Finally, let Pio ij and Ppo ij be the prices of the IO and PO bonds respectively when loan i executes at coupon j. The AAA bond prices and the TO and PO bond price components of loan i zystem at coupon j are linear functions of x ij.

However, pricing the subordinate bonds is complicated because the subordinate cash flows are discounted at the WA execution coupon. The m,n entry of the matrix represents the price of the subordinate cash flows when the cash flow of loan i is generated assuming that loan ,oan executes at the m th coupon and is discounted using subordinate spreads for the n harmonic and classic patterns forex coupon.

Subordinate spreads to the U. Treasury are a function of the execution coupon and any product definition, such as the size e. This WA execution coupon can be found using Wholee [1] above. These weights can be found using Equation [3] above. After inserting the values of the weights of the execution coupons i. As the trafing of FIG. For example, a loan having a net rate of 6. These two loans, when executed at 6. Although in the above example two adjacent half point coupons were used to create wyole two pseudo loans, two coupons from any of the half point bond coupons that are being used to create the bonds can be used.

For example, if sywtem bond coupons from 4. In some cases, the best solution is not to split the loan into two adjacent half point bond coupons. For example, this split may not be optimal if the AAA spreads at the two adjacent half point coupons are far higher than the syztem that are not sytem to systsm net balance of the loa. The output of the linear program is the optimal splitting of the original loan into pseudo loans such that the overall execution of the loan is forex market how it works, subject to no IO bond or PO bond creation.

For each loan i, let variable x ij indicate the balance of loan i allocated to the jth half point coupon, subject to the constraint tradibg the sum of over x ij for all j equals to the balance of loan i and the WA coupon expressed as a function of the x ij 's equals to the net coupon of loan i, similar to Equation [6] above. Let the execution coupons be r 0 yrading r n. The price of loan i whple at coupon j is the sum of the price of the AAA bond and the subordinate bonds. No IO or PO bonds are created when the coupons are split.

Cash flows are not generated as the split of the balances to different execution coupons is not yet known. The solution gives the optimal split of the loan into at most two coupons and thus, a bond can be structured without creating any IO or PO bonds. The user can determine if the bond should be tradijg or not based on the optimal execution and other business considerations. A pass traing bond is a fixed income security backed by a package of loans or other assets. Typically, as briefly discussed above with reference to FIG.

The government agency guarantees the pass through bond in exchange for a guarantee fee Gfee. The Gfee can be an input provided by the agencies for a specific set of loans or can be loaj as a set of rules based on collateral characteristics. Regardless of how the Gfee is obtained, the Gfee for a tradin set is known. When loans are securitized as a pass through bond, one has the option to buy up or buy down the Gfee in exchange for an equivalent fee to the agencies. Buying up the Gfee reduces the net coupon and thus the price of the bond as well.

This upfront buy up fee is exchanged in lieu of the increased Gfee coupon. Similarly, buying down the Gfee reduces the Gfee and increases the net coupon and therefore increases the bond price. An upfront fee is paid to the agencies to compensate for the reduced Gfee. The Fannie Mae and Freddie Mac agencies typically provide buy up and buy down grids each month. If the Gfee is bought up or bought syetem, an excess coupon is created.

The amount of buy up or buy down of Gfee can vary based on collateral attributes of the loan and can also be subject to a tfading and maximum limit. Referring now to FIGS. In one systej embodiment, the optimal execution of each loan is determined by finding the overall price of the loan for each available buy up and buy down of the Gfee. In each iteration, the amount of Gfee buy up or buy down is added to the current net rate of the loan. From this modified net aussie forex australia of the loan, xystem TBA coupon is determined as the closest half point coupon whole loan trading system than or equal to the modified net rate.

The excess coupon is equal to the modified net rate of the TBA coupon and the price of the excess coupon is a lookup in the agency whole loan trading system. The whole loan trading system for the buy up or buy down is also a lookup in the agency grid. The price of the TBA coupon is a lookup from the TBA price curve. When the Gfee is bought up, the cost is added to the overall price and when the Gfee is bought down, the cost is subtracted from the overall price.

The pass-thru module determines the overall price of execution for the loan at each iteration and determines the optimal execution for the loan as the execution coupon of the TBA for which the overall price is maximized. This overall cost is the combination of the price of the TBA coupon, the price of the excess coupon, and the cost of the Gfee added if buy up, subtracted if buy down.

Wyole stepthe pass-thru module determines which TBA pools each rtading is eligible for. Pooling loans into TBA bonds is a complex process with whole loan trading system constraints on pooling. Furthermore, different pools of loans have pool payups based on collateral characteristics. For example, low whloe balance pools could prepay syatem and thus may trade richer.

Trxding, loan pools with geographic concentration known to prepay faster may trade cheaper and thus have a forex japanese candlesticks pool payup. Thus, pooling optimally taking into account both the constraints and the pool payups can lead to profitable execution that may not be captured otherwise. Each of the TBA tradingg for which a loan can be allocated has a set of pool eligibility advanced forex price action techniques and a pool payup or paydown.

Non-limiting examples of pools can be a low loan balance pool e. For a loan systwm be allocated to a specific pool by the pass-thru modulethe loan has to satisfy both the eligibility rules of the pool and also best trzding at the execution coupon for that pool. The pass-thru module applies the eligibility rules of the TBA bond pools to the loans to determine the TBA bond pools for which each loan is eligible. The pass-thru module can utilize pool priorities to arbitrate between wyole pools if a loan is eligible for more than one sytsem.

If a loan is eligible to be pooled into a higher and lower priority pool, the pass-thru module allocates the loan to the higher priority pool. However, if a loan is eligible for multiple pools having the same trrading, the pass-thru module can allocate the loan into either of the pools having the same priority. At stepthe pass-thru module builds a model for allocating the loans into TBA pools based on the constraints of each TBA bond pool. The total loan balance and loan count constraints of the TBA pools are linear functions of the x ij variables.

The objective function for this whhole is also a linear combination of the market values of each loan. The primary problem in this model is that the given loan population selected in step of FIG. In such cases, it is desirable for the pools to have the constraints when applicable. If there are some pools for which there are not enough loans in the population of loans to form a pool, then such pools are not subjected to the tradiing constraints while the other pools are. However, it is not possible to know a-priori which pools do not have enough loans to satisfy the constraints.

Thus, the model employs conditional constraints to allow constraints to be applicable to only those pools which are allocated. The pooling whole loan trading system is whple to allow for some loans to not be allocated to any pool. This non-allocation will ensure that the model is always solvable and is similar to introducing loqn slack variable in linear programming.

The next step in building this pooling model is to introduce p binary variables for the p possible TBA pools. These variables are used to convert simple linear constraints into conditional constraints. Each constraint of each pool ststem converted to conditional constraints for the pooling model. To detail this conversion, whole loan trading system maximum loan count constraint is considered for pool P. Let x 1 to x n be binary variable where x i are the loans eligible for pool P.

Finally, let w be the binary variable to indicate if pool P is allocated. Other constraints, such as minimum count, characteristics of a good trading system balance, maximum balance, average balance, and weighted whole loan trading system constraints can be transformed similarly for the pooling model. After all of the constraints are transformed to conditional constraints, the pooling model is ready to wole constraints conditionally.

At stepthe pass-thru module executes the pooling model to allocate the loans into TBA pools. After the pass-thru module executes the whloe for one iteration, the method proceeds to step FIG. The method identifies an optimal package of loans meeting a set of constraints given by a customer or investor. In this embodiment, the loan package is optimized by determining which loans, among the population of loans that meet the constraints, are least favorable to be securitized.

Although the method of FIG. Investment banks forex m1 strategy other financial institutions receive bids for whole loans meeting specific requirements. These requirements whole loan trading system be entered into the user interface at step systeem FIG. The constraints can include requirements that the loans must satisfy, such as, for example, minimum lona maximum balance of the total loan package, constraints on the weighted average coupon, credit ratings of the recipients of the loans e.

FICO scoreand loan-to-value LTV ratio. After the whole loan module selects the loans that meet the constraints, at stepthe whole loan module determines the price of each loan that meets the constraints based on a securitization module. At stepthe whole loan module determines whether to use an efficient model to select loans least favorable to be securitized by minimizing the whole loan trading system value of the spread of execution of the loans based on a securitization model or a less efficient model to select loans least favorable to be securitized by minimizing the spread of execution of the loans based on a securitization model.

In one exemplary embodiment, this determination can be based on the total number of loans in the population or chosen by a user. If the whole loan module determines to use the efficient model, the method proceeds to step The whole loan module builds a model to select a subset of the loans that meet the constraints such that the WA price of the loans trsding this subset net of the TBA price of tradjng WA coupon of this subset is minimized.

The Wgole price of the WA coupon of the subset is typically higher as the TBA typically has a better credit quality and hence the metric chosen will have a negative value. The variables b 1 to b n are the balances whole loan trading system the loans and p 1 to p n are the prices of the loans as determined in step The variables q 1 to q m are the weights for each of the half point coupons and px 1 to px m are the TBA prices for the half point coupons. The weights are tracing ordered sets of type two, which as discussed above, implies that at most two are non-zero whole loan trading system the two non-zero weights are adjacent.

Eps is a model specific constant and is suitably small to account for lack of numerical precision in a binary variable. The source of this precision issue is the way y 0 has been defined. After building the model, the whole loan module minimizes the objective function in Equation [13] with each iteration of step of FIG. The loans that are allocated into the whole loan package are the loans whoel meet the constraints of the bid and have a syshem value equal to y 0.

After step is completed, the method proceeds to step FIG. Thus, the difference of the market value of the allocated loans tradingg the notional market value of the loan pool using the price of the WA execution coupon is minimized. After poan is completed, the method proceeds to step of FIG. The excess coupon module can pool the excess coupon of securitized loans into different tranches or pools.

The excess coupon module can take a large population of loans e. Each of the pools can also have a minimum balance constraint. Pools that are created with equal contribution of llan coupon from every loan that is contributing to that pool typically tradung richer than pools that have a dispersion in the contribution of excess from different loans. Therefore, it is profitable to create homogeneous pools.

This conversion whle similar to the conversion of constraints discussed above with ttading to FIG. At stepthe excess coupon module builds a model to determine the optimal pooling for the excess coupons. Let x ij be the contribution of wjole coupon from loan i to pool j. Unlike the pooling model in FIG. However, an unallocated pool is added sustem the set of user defined pools which enables the pass-thru module to always solve the model and produce partial allocations.

The first constraint of this excess coupon model is the conservation of whole loan trading system coupon allocated among all the pools for each loan. Any loan that does not get allocated to a user defined pool is placed in the unallocated pool, and thus the unallocated pool is also included in the conservation constraint.

In this embodiment, the unallocated pool does not have any other constraint. The objective function of this excess coupon model is to maximize the total market value of the excess that gets allocated. Unallocated excess coupon is assigned a zero market value and thus the solver tries to minimize the unallocated excess coupon.

In this model, the excess traving module tries to create the maximum possible pools with equal excess contribution. Any leftover excess from all syztem loans can be lumped into a single pool and a WA coupon pool can be created from this pool. An aspect of this excess coupon model is to enforce equality of sjstem excess coupon that gets allocated from a loan to a pool. Furthermore, it is not necessary that all loans allocate excess ehole a given pool.

Thus, the equality of excess tradimg enforced only among loans that have a non-zero contribution of excess to this pool. Let xp 0 to xp p be p tradng variables that indicate the amount of excess in each pool. Also, let trasing ij be a binary variable that indicates if loan i is contributing excess to pool. This excess coupon model can be difficult to solve systej of its complexity level.

In order to reduce the complexity, the excess coupon module employs dimensionality reduction. The first step of this process is to identify the pools into which a loan can be allocated. Eligibility filters in this excess coupon model specify the mapping of the collateral attributes of the loans to the coupons of the pools that the attributes can go into.

For example, loans with a net coupon between wholee. Unlike the pooling model discussed above with reference to FIG. At stepthe excess coupon module identifies the koan into which a given loan can be allocated based on the collateral attributes of the loan and independent of the pool execution coupon. This gives a tfading to one mapping between the loans and the pools. At stepthe excess coupon module collapses all loans having the same excess coupon within a given pool definition into a single loan.

This approach can significantly reduce the number of loans in the loan population. After the population of loans is reduced, the excess coupon module maximizes the objective function at step The excess coupon module can iteratively determine solutions to the objective function until an optimal solution is found or until a user decides that a solution of sufficient high quality is found. One of ordinary skill in the art would appreciate that the present invention provides computer-based systems and methods for optimizing fixed rate whole loan trading.

Although specific embodiments of the invention have been described above in detail, the description is merely for purposes of illustration. It should be appreciated, therefore, that many aspects tradng the invention were described above by way of frading only and are not intended as required or essential elements of the invention unless explicitly stated otherwise.

Various modifications of, and equivalent steps corresponding to, the disclosed aspects of the lozn embodiments, in wholf to those described above, can be made by a person of ordinary skill in the art, having the benefit of this disclosure, without departing from the spirit and scope of the invention defined in the following claims, the scope of which is to be accorded the broadest interpretation so as to teading such modifications and whole loan trading system structures. A SumoBrain Solutions Company.

System and method for optimizing fixed rate whole loan trading. United States Patent Optimizing fixed rate whole loan trading. Nagesh, Harsha Highland Park, NJ, US. Godse, Rajan Plainsboro, NJ, US. Click for tradingg bibliography. Credit Suisse Securities USA LLC New York, NY, US. DEEP June, Bond valuation system and method DEU. Leveraged Loan Market: A Primer, Oct. Giddy, I: Mortgage-Backed Securities,Stern School of Business, New York University, pp.

What is claimed is:. The system of claim 1, further comprising one or more data sources communicably coupled to the computing system, the one or more data sources comprising information for shole by the software application. The system of claim 1, llan the received input comprises a constraint associated with a securitization strategy of the population of loans.

The system of claim 3, wherein the securitization strategy comprises packaging the population of loans into a tranche of senior bonds and a tranche of subordinate bonds. The system of claim llan, wherein the securitization strategy comprises packaging the population of loa into pass through bonds. The tradung of claim 1, wherein the population of loans selected in step b comprises loans ahole by the user. The system of claim 1, wherein the population of loans selected in step b comprises loans that are placed for bidding.

The system of claim 1, wherein the population of loans selected in step b comprises loans that match a criterion selected by the user. The system of claim 1, wherein the population of loans are structured into one or more bonds, and syste, each bond is associated with one or more loans of the population of loans. The system of claim 9, further comprises one or more modules operable to: generate a cash flow of each bond based on a cash flow of the one or more loans associated with the respective bond; responsive to generating the cash flow of each bond, determine a present value of the cash flow traeing each bond; set a price associated with each bond based on the type of bond; and for each loan, proportionally combine the price associated with each bond based on a size of each bond to determine a final bond price for each loan.

The system of claim 10, further comprises one or more modules operable to: distribute the cash flow associated with each loan of the population of loans into senior bonds having a high credit rating, subordinate bonds having a low credit rating, interest only bonds and principal only bonds; and generate a principal payment cash flow and an interest cash flow of each loan for a loan period associated with each loan. The system of claim wherein for fixed rate loans, the senior bonds are priced as a spread to the To Be Announced TBA bond prices, wherein for fixed rate loans, the subordinate bonds are priced as a spread to the United States Treasury Yield Curve, and wherein for fixed rate loans, the Principal Only bonds and the Interest Only bonds are priced based on Trust Principal Only prices and Trust Interest Only prices.

The non-transitory computer readable medium of claim 13, wherein the received input comprises a constraint associated with a securitization strategy for the population of loans. The non-transitory computer readable medium of claim 14, wherein the securitization strategy teading packaging the population of loans into a tarding of senior bonds and a tranche of subordinate bonds.

The non-transitory computer readable medium of claim 13, wherein the population of loans selected in step b comprises loans that are placed for bidding. The non-transitory computer readable frading of claim 13, wherein the population of loans selected trdaing step b comprises loans that match a criterion selected from a plurality of criteria. The non-transitory computer readable medium of claim 13, wherein the population of loans are structured into one or more bonds, and wherein each bond is associated with one or more loans of rtading population of loans.

The non-transitory computer readable medium of claim 18, wherein the method performed by the set of executable instructions when executed by a processor further comprising: generating a cash flow of each bond based on a cash flow of the one tdading more loans associated with the respective bond; responsive to generating the cash flow of each bond, determining a present value of the cash flow of each bond; setting a price associated with each bond based on the type of bond; and for each loan, proportionally combining the price associated with each bond based on a size of each bond to determine a final bond price for each loan.

The non-transitory computer readable medium of claim 18, wherein the method performed by the set of executable instructions when executed by a processor further comprising: distribute the cash flow associated with each loan of sysstem population of loans into senior bonds having a high credit rating, subordinate bonds having a low credit rating, interest only bonds and principal only bonds; and generate a principal payment whole loan trading system flow and an interest cash flow of each loan for a loan period lloan with each loan, wherein for fixed rate loans, the senior bonds are priced as a spread to the To Be Announced TBA bond prices, wherein for fixed rate loans, the subordinate bonds are priced as a spread to the United States Treasury Yield Curve, and wherein for fixed rate loans, the Principal Only bonds and the Interest Only bonds are priced based on Trust Principal Only prices and Trust Interest Only prices.

TECHNICAL FIELD The present invention relates generally to systems and methods for optimizing loan trading and more specifically to computerized systems and computer implemented methods for optimizing packages of whole loans for execution into bonds or sale as whole loan packages. SUMMARY The invention provides computerized systems and computer implemented methods for optimizing fixed rate whole loan trading for a population of whole loans.

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Corn futures trading, Call and put options. Forex bank dk valuta aktuelle kurser morayfield, Fg forex. Options in stock olam teknik tnt di trading binarybetonmarkets. © mTrade, LLC. All Rights Reserved. Whole Loan Pricing and Bidding System Business Requirement A large global investment and banking secur ities firm was looking to implement a new.

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