Recent studies by Fisher and Weil [7], Bierwag [2], [3], Bierwag and Kaufman [5], and Khang [9] demonstrate that it is possible to immunize a portfolio of default-free assets against unexpected interest rate changes so that at the end of the planning period the investor will realize at least the returns expected at purchase. However, this immunization strategy is applicable for the case in which the change in unexpected interest rate occurs only once at the instant after the purchase of the asset. Obviously, the case depicted above is not likely to resemble the real world situation in at least two respects. First, the interest rate change is likely to occur at any time and, second, the interest rate change is likely to occur many times during the investor's planning period.

The purpose of this note is to present a simple computational algorithm to approximate the E, S portfolio selection model. The essential feature of the model is the utilization of the familiar linear programming framework by representing risks as a series of linear constraints. Suppose we have m states and n securities, and we assume the investor is able to specify the contingent returns for all securities in each state. Following [7], we define risk as being the downside deviation from the investor's target rate of return.

In a previous paper in the Journal of the American Statistical Association, Fielitz and Smith [16] present evidence in favor of asymmetric members of the stable family of distributions for describing stock price changes. Support for this contention is derived from an empirical examination of the daily closing prices of 200 stocks from the New York Stock Exchange from December 23, 1963, to November 29, 1968. In particular, the empirical examination of these securities indicates that outliers (especially in the short-tail (left-hand tail) of the distributions), kurtosis, and skewness are present in the distributions. These characteristics are consistent with the hypothesis that stock price changes belong to the asymmetric class of members of the stable family of distributions.

Banks are growing ever larger compared to their national economies. We show that increases in relative bank size (measured as a bank’s liabilities divided by national GDP) are linked to banks displaying higher tail risk. This effect is not entirely due to risk channels that disproportionately expose relatively large banks to systematic tail risks, sovereign risks, or banking crises. Instead, we detect a persistent component in the tail risk of relatively large banks that is bank-specific and connected to government guarantees. Furthermore, as banks grow in relative size, tail risks are shifted to debtholders without wealth gains for shareholders.

Using a fast numerical technique, we investigate a large database of investors’ suboptimal nonexercise of short-maturity American call options on dividend-paying stocks listed on the Dow Jones. The correct modeling of the discrete dividend is essential for a correct calculation of the early exercise boundary, as confirmed by theoretical insights. Pricing with stochastic volatility and jumps instead of the Black–Scholes–Merton benchmark cuts the amount lost by investors through suboptimal exercise by one-quarter. The remaining three-quarters are largely unexplained by transaction fees and may be interpreted as an opportunity cost for the investors to monitor optimal exercise.

We develop an equilibrium model in a 2-country, 2-good, pure exchange economy in which investors with logarithmic utility functions have heterogeneous beliefs about exogenously given output or endowment processes. We obtain closed-form representations of real exchange rates and of stock prices. We show that heterogeneous beliefs together with heterogeneous preferences make the volatility of real exchange rates and of stocks exhibit some properties that have been well documented in the empirical literature. These properties include the high volatility of both real exchange rates and stocks compared with that of economic fundamentals, the high correlation of stocks during periods of volatile markets. The model can also generate the clustering of the volatility of foreign exchange rates and stocks if the differences of beliefs are clustering.

Relying on a simple general equilibrium model of the term structure, we show that both nominal yields and real consumption growth rates can be affine in the unobservable state variables. We can then express real consumption growth rates in terms of nominal yields rather than the unobservable state variables with the coefficients of the resultant forecasting relation being endogenously determined by the term structure model. Using term structure data over the 1985 to 2000 sample period, the empirical evidence is consistent with our model more accurately predicting real consumption growth rates than a regression model based on the term spread.

When market returns follow a long memory volatility process, standard approaches to estimating dynamic minimum variance hedge ratios (MVHRs) are misspecified. Simulation results and an application to the S&P 500 index document the magnitude of the misspecification that results from failure to account for basis convergence and long memory in volatility. These results have important implications for the estimation of MVHRs in the S&P 500 example and other markets as well.

The extant literature suggests that one of the main causes of the recent financial crisis was the excessive use of short-term debt by banks. Using a large sample of banks, we find that increases in repurchase agreements (repos) were recognized by external capital markets to increase bank risk in the pre-crisis period. In the crisis, we find a negative relationship between repos and risk. We attribute this result to evidence suggesting that “good” banks were able to continue funding their repos, whereas “bad” banks had to significantly decrease their repo funding.

While there is growing evidence that stock prices do not follow pure random walks, the degree of existence of temporary components in stock prices is not well known. Modeling stock prices as the sum of a random walk and a general stationary (predictable) component, the paper proposes an estimable lower bound on the proportion of total stock return variance caused by the predictable component. Contrary to the absolute value of the first-order auto-correlation coefficient estimates of Fama and French (1988a), this lower bound reasonably estimates the true variance proportion in finite samples also when the temporary component does not follow a first-order autoregressive process. The estimated mean values of the lower bound reach a maximum of 10 percent for the equal-weighted market portfolio of NYSE stocks over the post-war period 1947–1986, while the maximum is 25 percent for the pre-war period 1926–1946. The value-weighted market portfolio exhibits generally smaller variance proportion estimates. The pure random walk hypothesis is also reexamined using a standard variance ratio statistic extended to multiple return horizons.

A useful distinction is sometimes maintained between business risk associated with uncertainty of receipts generated by a firm's assets and financial risk defined as the probability of incurring penalty costs stemming from liability claims on assets and asset yields. In fact, disagreement in recent literature on the exact role of capital structure in market valuation of business firms is easily interpreted once the risk assumptions of the literature such as perfect certainty, business risk, and financial risk have been identified.

We find that acquirers create higher shareholder returns when advised by investment banks with more experience in the target industry. This finding is stronger when acquirers face more difficulties understanding and evaluating the targets. Further analyses show that these banks help acquirers avoid overpaying for targets and thus capture more of the deal synergy rather than making deals generating higher synergy. Our results are robust to controlling for an exhaustive set of determinants of acquirer returns and an identification strategy that exploits exogenous shocks to the supply of investment banks with target-industry experience.

Consumer credit is an extremely significant factor in the economic expansion of the United States. Its annual compound rate of growth over the postwar period, averaging more than 15 percent, has considerably exceeded that of virtually all other economic indicators, and this expansion rate shows no sign of lessening. The importance of consumer credit is further emphasized by the fact that it accounts for nearly one-third the total liabilities of the household sector [11, p. 142]. And from the stand-point of financial institutions, consumer loans are vital because they are characteristically the most profitable investments in lenders' asset portfolios.

This paper uses an autoregressive approach to test a multi-factor model with time-varying risk premiums. A quasi-differencing approach is used to eliminate the unobservable factors in the model. It is found that the model is capable of capturing the “size effect” and the “dividend yield effect,” but is incapable of explaining the “book-to-market effect” and the “earnings-price ratio effect.” Thus, it is concluded that a constant-beta multi-factor model will not be able to explain the cross-sectional variation in expected returns.

We use credit card data to estimate the impact of increasing minimum payments on delinquency, payments, spending, and write-offs. Our identification strategy exploits an unusual institutional feature: Borrowers can use their account to make purchases with both revolving loans (on which minimum payments increased) and term loans (on which there was no change). Payment increases by delinquent borrowers are insufficient to match increasing minimums, resulting in lower cure rates and an increase in write-offs. Affected borrowers migrate away from these accounts by decreasing charges and increasing payments, consequently lowering the interest earned by the bank.

We study the capital structure changes of drug firms after an investment-opportunity shock brought about by the Biologics Price Competition and Innovation Act. Using a difference-in-difference approach, we show that the shock led drug firms to make their capital structures less constraining by decreasing leverage, shortening debt maturity, increasing unsecured debt, and reducing convertible debt. New debt covenants became less restrictive and firms raised equity to preserve borrowing capacity. Our results support the view that firms actively manage their capital structures to bolster financial flexibility and increase debt capacity in response to new investment opportunities.

We report the results of a longitudinal intervention with students across 5 universities in China designed to reduce online consumer debt. We allocate participants to either a financial literacy treatment group, a self-control treatment group, or a zero-touch control group. Our self-control training intervention features detailed tracking of spending and borrowing, budgeting, and introspection about consumption choices. This intervention reduces online borrowing and delinquency charges, mainly driven by a reduction in entertainment-related spending and borrowing. In contrast, financial literacy interventions improve test scores but only marginally affect borrowing. Our results suggest that cultivating self-regulation and budgeting skills can improve borrowing behavior on e-commerce platforms.

In this paper the traditional capital asset pricing model is reformulated as a system of simultaneous equations in which returns on similar securities are treated as endogenous variables and in which pertinent financial data for particular firms and a market factor are treated as exogenous variables. Such a system is estimated, and serious questions are raised concerning the tenability of the simple linear model so often used to explain capital asset prices under uncertainty.

This paper considers two aspects of the tendency for systematic risk to change during the period surrounding a firm-specific event. First, a statistic allowing for heteroskedasticity is presented as a means of more precisely testing for the incidence of structural change in the market model. Secondly, the bias resulting from the imposition of a single, arbitrary event period on every firm in a market efficiency study is formally demonstrated. Using a sample based upon stock splits, the switching regression technique of Quandt is then adapted to show that event intervals are more appropriately considered on a case-by-case basis. A comparison of alternative residual measures illustrates these procedures.

We examine how organizational form affects corporate payouts. Conglomerates pay out more than pure plays in both cash dividends and total payouts (cash dividends plus share repurchases). Furthermore, their payouts are more sensitive to cash flows compared to pure-play firms. The sensitivity of payouts to cash flow increases as the cross-segment correlation in a conglomerate decreases. Corporate payouts increase after mergers and acquisitions (M&As), especially among M&As in which acquirers and targets are less correlated. These results suggest that the coinsurance among different divisions of a conglomerate allows them to pay out more cash flow to their shareholders than pure-play firms.