Pricing Of Mortgage-backed Securities Research Articles

Security Design in Non-Exclusive Markets with Asymmetric Information

Abstract We study the problem of a seller (e.g. a bank) who is privately informed about the quality of her asset and wants to exploit gains from trade with uninformed buyers (e.g. investors) by issuing securities backed by her asset cash flows. In our setting, buyers post menus of contracts to screen the seller, but the seller cannot commit to trade with only one buyer, i.e. markets are non-exclusive. We show that non-exclusive markets behave very differently from exclusive ones: (1) separating contracts are never part of equilibrium; (2) mispricing of claims faced by the seller is always greater than in exclusive markets; (3) there is always a semi-pooling equilibrium where all sellers issue the same debt contract priced at average-valuation, and sellers of low-quality assets issue remaining cash flows at low-valuation; (4) market liquidity can be higher or lower than in exclusive markets, but (5) the average quality of originated assets is always lower. Our model’s predictions are consistent with empirical evidence on the issuance and pricing of mortgage-backed securities, and we use the theory to evaluate recent reforms aimed at enhancing transparency and exclusivity in markets.

Mortgage Securitization and Shadow Bank Lending

AbstractWe show how securitization affects the size of the nonbank lending sector through a novel price-based channel. We identify the channel using a regulatory spillover shock to the cross-section of mortgage-backed security prices: the U.S. liquidity coverage ratio. The shock increases secondary market prices for FHA-insured loans by granting them favorable regulatory status once securitized. Higher prices lower nonbanks’ funding costs, prompting them to loosen lending standards and originate more FHA-insured loans. This channel accounts for 22% of nonbanks’ growth in overall mortgage market share over 2013–2015. While the shock creates risks for financial stability, homeownership also increases.

Security Design in Non-Exclusive Markets with Asymmetric Information

We revisit the classic problem of a seller (e.g. firm) who is privately informed about her asset and needs to raise funds from uninformed buyers (e.g. investors) by issuing securities backed by her asset cash flows. In our setting, buyers post menus of contracts to screen the seller, but the seller cannot commit to accept contracts from only one buyer, i.e., markets are non-exclusive. We show that an equilibrium of this screening game always exists, it is unique and features semi-pooling allocations for a wide range of parameters. In equilibrium, the seller tranches her asset cash flows into a debt security (senior tranche) and a levered-equity security (junior tranche). Whereas the seller of a high quality asset only issues her senior tranche, the seller of a low quality asset issues both tranches but to distinct buyers. Consistent with this, whereas the senior tranche is priced at pooling valuation, the junior tranche is priced at low valuation. Our theory's positive predictions are consistent with recent empirical evidence on issuance and pricing of mortgage-backed securities, and we analyze its normative implications within the context of recent reforms aimed at enhancing transparency of financial markets.

Credit Risk, Informed Markets, and Securitization

Mortgage backed securities (MBS) funded the US housing bubble, while the bust resulted in systemic risk and the Global Financial Crisis. The pricing of MBS and the ABX securitization index failed to reveal growing credit risk. This paper draws lessons from this failure for the use of Credit Risk Transfers (CRT) to price credit risk. The central question is would the CRT market, as constituted today, have behaved differently than financial asset markets in the bubble years? If no, then this is a problem. If yes, then why?

A stochastic partial differential equation model for the pricing of mortgage-backed securities

We develop a dynamic structural model for the wealth of individual mortgagors in a mortgage pool. We model the process of default and prepayment and, by taking a limit as the pool size goes to infinity, derive a stochastic partial differential equation (SPDE) which can be used to describe the evolution of the loss process from the pool. We prove existence and uniqueness of solutions to this SPDE and show how our model is able to capture, in a flexible way, the prices of credit risky tranches of mortgage-backed securities under different market conditions.

Macroeconomic-Driven Prepayment Risk and the Valuation of Mortgage-Backed Securities

We develop a three-factor no-arbitrage model for valuing mortgage-backed securities in which we solve for the implied prepayment function from the cross-section of market prices. This model closely fits the cross-section of mortgage-backed security prices without needing to specify an econometric prepayment model. We find that implied prepayments are generally higher than actual prepayments, providing direct evidence of significant macroeconomic-driven prepayment risk premiums in mortgage-backed security prices. We also find evidence that mortgage-backed security prices were significantly affected by Fannie Mae credit risk and the Federal Reserve’s quantitative easing programs. Received May 10, 2016; editorial decision September 22, 2017 by Editor Stijn Van Nieuwerburgh.

Optimal Portfolios with Mortgage-backed Securities

Executive Summary. In this paper we consider portfolio optimization problems based on simulated scenarios and extend their usual application by including prepayment-sensitive fixed-rate agency mortgage-backed securities (MBS) into the universe of available assets. This has become feasible due to recent closed-form approximation approaches for the pricing of MBS, which have long been neglected in modern portfolio optimization applications due to the computational burden. In an empirical case study we show that an optimal asset allocation strategy with MBS is indeed able to substantially outperform strategies based on stocks and regular bonds only.

Explicit formulas for pricing of callable mortgage-backed securities in a case of prepayment rate negatively correlated with interest rates

In this paper, we deal with the pricing of Mortgage-Backed Securities (MBS) in the reduced-form framework. Based on the ideas presented by Brunel and Jribi (2008) [8] and Rom-Poulsen (2007) [7], we introduce a stochastic process Qt=e−∫0tλsds to model the prepayment factor and assume that the prepayment rate λt is inversely proportional to the stochastic interest rate rt, which follows a CIR process. Explicit formulas for pass-through MBSs and semi-analytical solutions for Collateralized Mortgage Obligations (CMO) are obtained through PDE approaches. Based on the formulas, numerical results are provided to explain the dependence of MBS prices on mortgage parameters and the negative correlation between MBS prices and interest rates.

How do path generation methods affect the accuracy of quasi-Monte Carlo methods for problems in finance?

Quasi-Monte Carlo (QMC) methods are important numerical tools in computational finance. Path generation methods (PGMs), such as Brownian bridge and principal component analysis, play a crucial role in QMC methods. Their effectiveness, however, is problem-dependent. This paper attempts to understand how a PGM interacts with the underlying function and affects the accuracy of QMC methods. To achieve this objective, we develop efficient methods to assess the impact of PGMs. The first method is to exploit a quadratic approximation of the underlying function and to analyze the effective dimension and dimension distribution (which can be done analytically). The second method is to carry out a QMC error analysis on the quadratic approximation, establishing an explicit relationship between the QMC error and the PGM. Equalities and bounds on the QMC errors are established, in which the effect of the PGM is separated from the effect of the point set (in a similar way to the Koksma–Hlawka inequality). New measures for quantifying the accuracy of QMC methods combining with PGMs are introduced. The usefulness of the proposed methods is demonstrated on two typical high-dimensional finance problems, namely, the pricing of mortgage-backed securities and Asian options (with zero strike price). It is found that the success or failure of PGMs that do not take into account the underlying functions (such as the standard method, Brownian bridge and principal component analysis) strongly depends on the problem and the model parameters. On the other hand, the PGMs that take into account the underlying function are robust and powerful. The investigation presents new insight on PGMs and provides constructive guidance on the implementation and the design of new PGMs and new QMC rules.

European CMBS Pricing:Bond, Mortgage, and Real Estate Characteristics

The recent global economic crisis is often associated with the development and pricing of mortgage-backed securities (MBS) and underlying products (i.e., subprime mortgages). In their research, Marcato and Tira use a rich database of MBS issues and present the first attempt to price commercial MBS (CMBS) in the European market. Their results are consistent with research carried out in the U.S. market. They find that multinational as well as bond-, mortgage-, and real estate–related characteristics show different degrees of significance in explaining European CMBS spreads at issuance. Multiple linear regression analysis, using a databank of CMBSs issued between 1997 and 2007, indicates a strong relationship with bond-related factors, followed by real estate and mortgage market conditions. Marcato and Tira also find that multinational factors are significant, with country of issuance, collateral location, and access to more-liquid markets all being important in explaining the cost of secured funding for real estate companies. As floater-coupon tranches tend to be riskier and exhibit higher spreads, they also estimate a model using this subset of data. The results hold and hence reinforce their findings. Finally, the authors estimate their model for both tranches A and B and find that real estate factors become relatively more important for the riskier investment products. <b>TOPICS:</b>Real estate, MBS and residential mortgage loans, CMBS and commercial mortgage loans

A note on applying option pricing theory to emerging mortgage and mortgage-backed securities markets

Option-based models have been the dominant paradigm for researches on the risks and pricing of mortgages and Mortgage-Backed Securities (MBS) in the USA. However, the adaptability and flexibility of option-based models in the emerging mortgage markets has been obviously neglected. This study provides the first analysis of the potential strengths and weaknesses of application of the option pricing theory to emerging markets with referring to both theoretical and empirical literatures. This study finds that the mortgage type, the attitude of mortgagees to risk and the institutional features are the key determinants of success of application of the option pricing theory to emerging mortgage and MBS markets. The option pricing literature in finance has been extended to study the risks and pricing of mortgages and MBS in the USA over the last three decades. However, given the robust and rapid growth of emerging mortgage and mortgage-related capital markets, there is surprisingly little known about to what extent the option pricing theory can be applied to the emerging mortgage and MBS markets in the global arena. The purpose of this study is to fill this gap by analysing the potential strengths and weaknesses of application of the option pricing models to emerging markets with specifically referring to both theoretical and empirical literatures.

How Important is Having Skin in the Game? Originator-Sponsor Affiliation and Losses on Mortgage-Backed Securities

This paper examines the relationship between mortgage originator loss exposure (i.e., risk retention) and the ex post performance and initial structure and pricing of mortgage-backed securities (MBS). We argue that originator risk retention varies positively with originator-sponsor and originator-servicer affiliation. Overall, we find that ex post loss and foreclosure rates are significantly higher for MBS in which originators are not affiliated with the sponsor or servicer. The relationship between ex post performance and affiliation is confined to low documentation mortgages in which originator screening based on “soft” information is likely to be most important in determining the creditworthiness of borrowers. Consistent with investors expecting ex post performance to vary with affiliation, we find that initial MBS yields are lower and the percentage of AAA-rated securities issued against the mortgage pool is higher for affiliated deals.

Are All Ratings Created Equal? The Impact of Issuer Size on the Pricing of Mortgage-Backed Securities

Initial yields on both AAA-rated and non-AAA rated mortgage-backed security (MBS) tranches sold by large issuers are higher than yields on similar tranches sold by small issuers during the market boom years of 2004-2006. Moreover, the prices of MBS sold by large issuers drop more than those sold by small issuers, and the differences are concentrated among tranches issued during 2004-2006. These patterns suggest that investors priced the risk that large issuers received more inflated ratings than small issuers, especially during booming periods.

Mortgage Prepayments as a Precursor to the Financial Crisis

Mortgage-backed security prices have depended on interest rates, while idiosyncrasies of prepayments had obscured the effect of rates on portfolio value before the financial crisis. The principal components of balance sheet accounts identified managerial dimensions that could help clarify the portfolio sensitivity to interest rates. The real impact of prepayments and default on the value of equity had appeared negligible since the first two components were correlated, as was required by hedge-accounting rules at that time.

Valuation of Mortgage-Backed Securities: A Portfolio Credit Derivatives Approach

Non-agency mortgage-backed securities (MBS) are typically priced and traded on discounted cashflow basis where a cashflow projection is made under a prepayment and default scenario and discounted with a discount margin (DM) that supposedly measures credit risk. Whilest simple and intuitive to traditional buy-and-hold investor base, it quickly became clear that DM was not effective for pricing and risk management, when mortgage credit rapidly grew out of usually mild credit environment and more sophisticated participants entered into the market. In particular, synthetic players' push for standardization of CDS on ABS and index products (ABX and CMBX) started to demand pricing consistency across cash and synthetics. In this paper we advocate application of portfolio credit derivative no arbitrage pricing framework to mortgage securitization products and their derivatives. To demonstrate we focus on the first toxic out of the Pandora's box into the financial system -- subprime MBS. We develop an amortizing mortgage loan model with competing arrival of random prepayment and default events following reduced form modeling of corporate default. Loan prepayment correlation, default correlation, and cross correlation can be built drawing into vast literature of corporate default correlation modeling. As both prepayment and default depend on house price appreciation and interest rate, we introduce a prepayment and default factor copula (Gaussian) to statically model these two factors. Analysis of recent vintage subprime deal structure reveals a near sequential pay waterfall which allows semi-analytical pricing of MBS. Calibration to ABX.HE benchmark is satisfactory and pricing of bespoke ABS off the index not only offers fair value data points when there is no market but also an innovative way to assess relative values. ABS CDO and TABX can be consistently priced with underlying ABS and the model naturally extends to CMBS/CMBX/CRE CDO and CLO/LCDX.

Estimating Default Probabilities Implicit in Commercial Mortgage Backed Securities (CMBS)

This paper uses a structural credit risk model, providing an analytical formula to estimate default probabilities implicit in commercial mortgage backed security prices. Empirical studies on CMBS default have focused on the probability of default depending on loan characteristics at the origination and market indices. Recent studies show that unobservable current loan-to-value (LTV) ratio is a key state variable driving default. We update this variable using Real Estate Investment Trust (REIT) property-type indices over time. Later, we employ first passage time approach to study CMBS default using implied LTV.

Borrower Credit and the Valuation of Mortgage-Backed Securities

We study the valuation of mortgage-backed securities when borrowers may have to refinance at premium rates because of their credit. The optimal refinancing strategy often results in prepayment being delayed significantly relative to traditional models. Furthermore, mortgage values can exceed par by much more than the cost of refinancing. Applying the model to an extensive sample of mortgage-backed security prices, we find that the implied credit spreads that match these prices closely parallel borrowers' actual spreads at the origination of the mortgage. These results suggest that models that incorporate borrower credit into the analysis may provide a promising alternative to the reduced-form prepayment models widely used in practice.

Some Truths about Ostriches: Who Doesn't Prepay Their Mortgages and Why They Don't

Prepayment estimation is essential in forecasting expected mortgage cash flow patterns. Accordingly, mortgage and mortgage-backed security prices are highly dependent on prepayment assumptions. Yet borrower prepayment behavior appears to be highly irrational, in the sense that many borrowers prepay their mortgages when it is not optimal to do so and fail to prepay their mortgages when the prepayment option is substantially in the money. In this paper, we explore the latter phenomenon, using a large data set of loans originated during the relatively high-rate 1980s that have failed to prepay by year-end 1996. As a control group, we examine similar loans that did prepay during the refinancing boom of 1993. By coupling Case–Shiller house price index information at the zip-code level, we can analyze the effect of broader housing market trends, especially housing appreciation, on borrower prepayment behavior. Although housing prices did decline significantly during the late 1980s and early 1990s in many areas of the United States, we find evidence that only about 25% of non-refinancing households might have been constrained by declining collateral values. Household demographic characteristics may provide some further explanation for this apparently non-optimizing behavior; however, we do not have a complete explanation.

Pricing Mortgage-Backed Securities in a Multifactor Interest Rate Environment: A Multivariate Density Estimation Approach

Multivariate density estimation (MDE) suggests that mortgage-backed security (MBS) prices can be well described as a function of the level and slope of the term structure. We analyze bow this function varies across MBSs with different coupons. An important finding is that the interest rate level proxies for the moneyness of the option, the expected level of prepayments, and the average life of the cash flows, while the term structure slope controls for the average rate at which these cash flows should be discounted. Though the origination and prepayment behavior of mortgages differ substantially across coupons, there remains an unexplained common factor in MBS prices. This factor does not seem to be related to the usual suspects and therefore presents a puzzle to financial economists. Coauthors are Robert F. Whitelaw, Matthew Richardson, and Richard Stanton. Article published by Oxford University Press on behalf of the Society for Financial Studies in its journal, The Review of Financial Studies.

Unobservable heterogeneity and rational learning: Pool-specific versus generic mortgage-backed security prices

Previous mortgage prepayment and valuation models assume that two mortgage pools with the same observable characteristics should behave indistinguishably. However, even pools with apparently identical characteristics often exhibit markedly different prepayment behavior. The sources of this heterogeneity may be unobservable, but we can infer information about a pool from its prepayment behavior over time. This article develops a methodology for using this information to calculate pool-specific mortgage-backed security prices. Knowledge of these prices is important both for portfolio valuation and for determining the cheapest pool to deliver when selling mortgagebacked securities. We find that unobservable heterogeneity between mortgage pools is statistically significant, and that pool-specific prices, calculated for a sample of outwardly identical mortgage pools between 1983 and 1989, may differ greatly from any single representative price.