We derive formulas for the valuation of call options on stocks and bonds when the defaultfree rate is stochastic. The formulas highlight the role of the correlation between the unanticipated returns on the underlying security and the changes in the short-term rate in determining the options value. Our numerical analysis indicates that option prices predicted by the proposed formula differ from those predicted by the Black and Scholes formula when this correlation is relatively large and the short-term rate's instantaneous variance is relatively large as well. Moreover, the proposed formula predicts higher (lower) stock option prices than those predicted by the Black and Scholes formula for correlation values that are lower (higher) than some positive critical value.

Using a unique data set with detailed information on the derivative positions of upstream oil and gas firms during 1996–2008, we find that hedging intensity is positively related to factors that amplify chief executive officer (CEO) entrenchment and free cash flow agency costs. There is also robust evidence that hedging is motivated by the reduction of financial distress and borrowing costs, and that it is influenced by both intrinsic cash flow risk and temporary spikes in commodity price volatility. We present a comprehensive perspective on the determinants of corporate hedging, and the results are consistent with the predictions of the risk management and agency costs literatures.

The value of information to the investor is best described by Samuelson [15] in his prologue to the theory of speculation: “…Suppose my reactions are not better than those of other speculators, but rather one second quicker… in a world of uncertainty, I note the consequences of each changing event one second faster than anyone else. I make my fortune not once, but every day that important events happen…” Furthermore, the role of heterogeneous expectations was emphasized by Hirshleifer [7]: “…Speculation…emerges not from differences in individual risk aversion, but rather solely from differences in individual belief as to what the future will reveal.” Thus, information which is always partial and different to different investors, in imperfect markets, is perfectly consistent with the existence of heterogeneity in investors' expectations.

It is shown that the class of elliptical distributions extend the Tobin 14 separation theorem, Bawa's 2 rules of ordering uncertain prospects, Ross's 12 mutual fund separation theorems, and the results of the CAPM to non-normal distributions, which are not necessarily stable. Further, the mean-covariance matrix framework is generalized to a mean-characteristic matrix framework in which the characteristic matrix is the basis for a spread or risk measure, and a generalized equilibrium pricing equation is arrived at. The implications to empirical testing of the CAPM and modeling the empirical distribution of speculative prices are discussed.

It is shown that the class of elliptical distributions extend the Tobin [14] separation theorem, Bawa's [2] rules of ordering uncertain prospects, Ross's [12] mutual fund separation theorems, and the results of the CAPM to non‐normal distributions, which are not necessarily stable. Further, the mean‐covariance matrix framework is generalized to a mean‐characteristic matrix framework in which the characteristic matrix is the basis for a spread or risk measure, and a generalized equilibrium pricing equation is arrived at. The implications to empirical testing of the CAPM and modeling the empirical distribution of speculative prices are discussed.

Building on theoretical asset pricing literature, we examine the role of market risk and the size, book‐to‐market (BTM), and volatility anomalies in the cross‐section of unlevered equity returns. Compared with levered (stock) returns, unlevered market beta plays a more important role in explaining the cross‐section of unlevered equity returns, even after controlling for size and BTM. The size effect is weakened, while the value premium and the volatility puzzle virtually disappear for unlevered returns. We show that leverage induces heteroskedasticity in returns. Unlevering returns removes this pattern, which is otherwise difficult to address by controlling for leverage in regressions.