Termination of distressed residential mortgages: An empirical analysis

A sample of 209 distressed mortgages is used to analyze the terminations of distressed mortgages. An option-based model is compared to a traditional default model. Results show that the traditional model is statistically superior. However, the model's ability to identify a default is similar to that of the simpler option-based model. Alternative measures of borrower's equity are compared. Measuring borrower's equity using total debt more accurately explains default than using either the mortgage balance or the mortgage value.     

Regularity conditions and scope estimates: The case of large-sized U.S. banks

We consider economies and diseconomies of scope for large U.S. banks by employing ordinary and hybrid translog cost functions. We examine the regularity conditions in output space where scope estimates are calculated and reject all models for which these conditions fail. The translog model always possesses violations. For the hybrid translog, violations occur in every case except one. In this one case, we find economies of scope.     

Loan Loss Severity and Optimal Mortgage Default

This paper tests the contingent claims model of mortgage default in its ruthless or frictionless form. The principal tests of the model are based on an unconventional source of data, namely, loan loss severities on defaulted mortgages. The frictionless model has well-defined predictions about loss severities which we test in detail. The data analyzed include a random sample of all mortgages originated during the period 1975–90 and purchased by Freddie Mac, as well as the loss severities on all mortgages purchased by Freddie Mac which defaulted during the period. The frictionless model does not do well in these tests.     

On Choosing Among House Price Index Methodologies

This paper compares housing price indices estimated using three models with several sets of property transaction data. The commonly used hedonic price model suffers from potential specification bias and inefficiency, while the weighted repeat-sales model presents potentially more serious bias and inefficiency problems. A hybrid model combining hedonic and repeat-sales equations avoids most of these sources of bias and inefficiency. This paper evaluates the performance of each type of model using a particularly rich local housing market database. The results, though ambiguous, appear to confirm the problems with the repeat sales model but suggest that systematic differences between repeat-transacting and single-transacting properties lead to bias in the hedonic and hybrid models as well.     

The impact of Saturday trading on stock returns: Evidence from the Tokyo stock exchange January 1976 to January 1989

This paper examines the impact of TSE Saturday trading on daily TOPIX returns and TSE trading volume over the January 1976 to January 1989 period. Saturday trading is shown to have no significant impact on mean stock returns for the other days of the week. However, a significant shift in the pattern of Monday and Tuesday TOPIX returns is documented in the post-August 1986 period. This shift does not appear to be related to Saturday trading. TSE Saturday trading is found to have a significant impact on the variance of stock returns on surrounding days. In addition, trading volume is significantly lower on trading days surrounding Saturday trading. These findings are relevant to the timing of portfolio adjustment decisions.     

Bias in estimates of discrimination and default in mortgage lending: The effects of simultaneity and self-selection

The common practices of estimating single-equation models of mortgage rejection to test for discrimination in mortgage markets or single-equation ex ante mortgage default equations to validate underwriting criteria produce biased and inconsistent parameter estimates. This is due to problems of simultaneous equations bias which arise because, in a world of imperfect information, mortgage terms are not exogenous to the rejection or default decision. In addition, mortgage default estimates are also subject to selection bias. Monte Carlo experiments are used to study the nature and extent of likely bias in single-equation estimation results. We find that rejection equation estimates indicate discrimination when none exists and that estimated coefficients of mortgage terms, such as the loan-to-value ratio, are also subject to significant bias in both rejection and default equations.     

Junk bonds,life insurer insolvency,and stock market reactions: The case of first executive corporation

The First Executive Corporation was the largest failure in the history of the life insurance industry. The company was one of the most aggressive purchasers of junk bonds through the 1980s and was the first of several large failures in the staid life insurance industry. In this article, we examine the effect of First Executive's failure on the value of companies in the life insurance industry. We find that the price of other life insurance companies' stock is negatively affected by the earnings announcement that preceded First Executive's failure. The magnitude of an individual company's reaction to First Executive's loss varies according to the proportion of the company's assets invested in junk bonds, the proportion of the company's assests invested in real estate, and the financial strength of the company as measured by A.M. Best's rating.     

Allocation,externalities, and building value

Externalities generally are viewed as impacting land values rather than building values. Yet when locational obsolescence is attributed to externalities, the implication is that externalities impact primarily on building values. The presence of negative externalities generally does not determine whether a building suffers from locational obsolescence; the more general cause is a misallocation of land. At the market allocation, there is no locational obsolescence even in the presence of an externality, while at the optimal allocation only an externality can produce locational obsolescence. Because locational obsolescence can exist without externalities, an externality is not a necessary condition for locational obsolescence. Because an externality can be present without accompanying locational obsolescence, an externality also is not a sufficient condition for the existence of locational obsolescence.     

Noise trading and prime and score premiums

This paper documents that a common element drives the time-series variation of the premium pricing of Primes and Scores. I argue that this common element is noise trading. The noise trading model of Delong, Shleifer, Summers and Waldmann (1990) predicts that returns on assets that are predominantly traded by noise traders will be correlated, since the misperceptions of noise traders are cross-sectionally correlated. Consistent with the noise trading hypothesis, changes in the average premium of Primes and Scores, which are predominantly traded by individual investors, are correlated with both changes in average discounts of closed-end funds and small firms returns. These empirical facts provide additional evidence that noise traders can affect security prices.     

A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks

We propose a nonparametric method for estimating the pricing formula of a derivative asset using learning networks. Although not a substitute for the more traditional arbitrage-based pricing formulas, network-pricing formulas may be more accurate and computationally more efficient alternatives when the underlying asset's price dynamics are unknown, or when the pricing equation associated with the no-arbitrage condition cannot be solved analytically. To assess the potential value of network pricing formulas, we simulate Black-Scholes option prices and show that learning networks can recover the Black-Scholes formula from a two-year training set of daily options prices, and that the resulting network formula can be used successfully to both price and delta-hedge options out-of-sample. For comparison, we estimate models using four popular methods: ordinary least squares, radial basis function networks, multilayer perceptron networks, and projection pursuit. To illustrate the practical relevance of our network pricing approach, we apply it to the pricing and delta-hedging of S&P 500 futures options from 1987 to 1991.