Pricing U.S. Dollar Index Futures Options: An Empirical Investigation

This paper develops a pricing model and empirically tests the pricing efficiency of options on the U.S. Dollar Index (USDX) futures contract. Empirical tests of the model indicate that the market consistently overprices these options relative to the derived model. This overpricing is more pronounced for out‐of‐the‐money options than for in‐the‐money options and more pronounced for put options than for call options. To validate the above results, delta neutral portfolios are created for one‐ and two‐day holding periods and consistently generate positive arbitrage profits, indicating that on average the market overprices the options on the USDX futures contracts.     

Trade-off Model of Debt Maturity Structure

In this paper, we suggest the trade-off model to explain the choice of debt maturity. This model is based on balancing between risk and reward of using shorter-term loans. Shorter-term loans have cost advantage over, but incur higher refinancing and interest rate risk than longer-term loans. Using the Compustat data, we show that the principal components of financial attributes are financial flexibility and financial strength. Therefore, only firms with greater financial flexibility and financial strength can use proportionately more short-term loans. We also document that financially strong firms take advantage of lower interest rates of short-term debt. They use proportionately more short-term loans when the term premium is high. The results of our study also provide evidence supporting the agency cost hypothesis, which is strongly supported by current literature.     

A Semiparametric Method for Valuing Residential Locations: Application to Automated Valuation

This paper is motivated by automated valuation systems, which would benefit from an ability to estimate spatial variation in location value. It develops theory for the local regression model (LRM), a semiparametric approach to estimating a location value surface. There are two parts to the LRM: (1) an ordinary least square (OLS) model to hold constant for interior square footage, land area, bathrooms, and other structural characteristics; and (2) a non-parametric smoother (local polynomial regression, LPR) which calculates location value as a function of latitude and longitude. Several methods are used to consistently estimate both parts of the model. The LRM was fit to geocoded hedonic sales data for six towns in the suburbs of Boston, MA. The estimates yield substantial, significant and plausible spatial patterns in location values. Using the LRM as an exploratory tool, local peaks and valleys in location value identified by the model are close to points identified by the tax assessor, and they are shown to add to the explanatory power of an OLS model. Out-of-sample MSE shows that the LRM with a first-degree polynomial (local linear smoothing) is somewhat better than polynomials of degree zero or degree two. Future applications might use degree zero (the well-known NW estimator) because this is available in popular commercial software. The optimized LRM reduces MSE from the OLS model by between 5 percent and 11 percent while adding information on statistically significant variations in location value.     

Price Linkages in Asian Equity Markets: Evidence Bordering the Asian Economic, Currency and Financial Crises

This paper examines price linkages among Asian equity markets in the period surrounding the recent Asian economic, financial and currency crises. Three developed markets (Hong Kong, Japan and Singapore) and six emerging markets (Indonesia, Korea, Malaysia, the Philippines, Taiwan and Thailand) are included in the analysis. Multivariate cointegration and level VAR procedures are conducted to examine causal relationships among these markets. The results indicate that there is a stationary relationship and significant causal linkages between the Asian equity markets. Nevertheless, lower causal relationships that exist between the developed and emerging equity markets suggest that opportunities for international portfolio diversification in Asian equity markets still exist.     

ADRs,Analysts, and Accuracy: Does Cross Listing in the United States Improve a Firm's Information Environment and Increase Market Value?

This paper investigates the relation between cross listing in the United States and the information environment of non‐U.S. firms. We find that firms that cross list on U.S. exchanges have greater analyst coverage and increased forecast accuracy than firms that are not cross listed. A time‐series analysis shows that a change in analyst coverage and forecast accuracy occurs around cross listing. We also document that firms that have more analyst coverage and higher forecast accuracy have higher valuations. Furthermore, the change in firm value around cross listing is correlated with changes in analyst following and forecast accuracy, suggesting that cross listing enhances firm value through its effect on the firm's information environment. Our findings support the hypothesis that cross‐listed firms have better information environments, which are associated with higher market valuations.