We study how stock option grants are funded through share repurchases under conditions of option exercisability and moneyness. Using daily repurchase disclosures by U.K. firms, we corroborate our hypothesis that driven by flexibility, firms repurchase early in an option schedule while options are out-of-money and before becoming exercisable. Our findings show that when daily stock prices are below weighted average option exercise price and when options are not immediately exercisable, firms (a) increase daily repurchase volume (value), (b) increase repurchase frequency, and (c) have lower relative repurchase prices. We further evidence this by examining the change in treasury regulation that enabled firms to hold on to repurchased shares rather than cancelling them. Our findings show a strong support for option funding motives in the post-treasury regulation period when repurchase flexibility is greater.

The study of Ferguson and Shockley (2003) shows that, if the Merton (1974) model can reflect reality, the omission of debt claims from the market portfolio proxy may explain the poor pricing ability of the CAPM in empirical tests. We critically re-assess this argument by first reviewing existing, but also new avenues through which the Merton (1974) model can point to a systematic bias in market beta estimates. However, we also show that some avenues are diversifiable, and that they all rely on excessive economy-wide default risk to create a non-negligible bias. We then use the Merton (1974) model to proxy for the total debt portfolio, but find that its application in empirical tests cannot improve pricing performance. We conclude that there are (so far) no valid theoretical reasons to believe that omitted debt claims undermine CAPM tests.

We compare the long run reaction to anticipated and surprise information announcements using stock splits. Although there is underreaction in both cases, anticipated splits are treated differently to those that are unforeseen. After anticipated splits, cumulative abnormal returns peak at one-and-a-half times the level observed after unanticipated splits although the time taken for the announcement to be absorbed into prices is the same. We explain the difference in underreaction by the degree to which split announcements are believed and hence invested in. The favorable signal conveyed in forecast splits is more credible owing to their better pre-split performance, resulting in a far more pronounced underreaction effect.

We present a simple put option pricing procedure within an asset–liability valuation model that can be used to estimate the incentives facing stock-based life insurance firms to voluntarily sell their businesses under various operating and regulatory conditions. Estimates are derived for samples of 11 sold firms and 24 continuing Australian life insurance companies over a period of industry consolidation. The put option values interact with other actuarial and accounting components of the fair value of these life insurance firms and are used to assess the effectiveness of accounting and actuarial measures of capital, under static or dynamic based solvency testing models.

We compare forecasts of the realized volatility of the pound, mark and yen exchange rates against the dollar, calculated from intraday rates, over horizons ranging from one day to three months. Our forecasts are obtained from a short memory ARMA model, a long memory ARFIMA model, a GARCH model and option implied volatilities. We find intraday rates provide the most accurate forecasts for the one-day and one-week forecast horizons while implied volatilities are at least as accurate as the historical forecasts for the one-month and three-month horizons. The superior accuracy of the historical forecasts, relative to implied volatilities, comes from the use of high frequency returns, and not from a long memory specification. We find significant incremental information in historical forecasts, beyond the implied volatility information, for forecast horizons up to one week.

We examine contemporaneous jumps (cojumps) among individual stocks and a proxy for the market portfolio. We show, through a Monte Carlo study, that using intraday jump tests and a coexceedance criterion to detect cojumps has a power similar to the cojump test proposed by Bollerslev et al. (2008). However, we also show that we should not expect to detect all common jumps comprising a cojump when using such coexceedance based detection methods. Empirically, we provide evidence of an association between jumps in the market portfolio and cojumps in the underlying stocks. Consistent with our Monte Carlo evidence, moderate numbers of stocks are often detected to be involved in these (systematic) cojumps. Importantly, the results suggest that market-level news is able to generate simultaneous large jumps in individual stocks. We also find evidence of an association between systematic cojumps and Federal Funds Target Rate announcements.

We analyse the entry decisions of competing firms in a two-player stochastic real option game, when rivals earn different but correlated uncertain profitabilities from operating. In the presence of entry costs, decision thresholds exhibit hysteresis, the range of which is decreasing in the correlation between competing firms. A measure of the expected time of each firm being active in the market and the probability of both rivals entering within a finite time are explicitly calculated. The former (latter) is found to decrease (increase) with the volatility of relative firm profitabilities implying that market leadership is shorter-lived the more uncertain the industry environment. In an application of the model to the aircraft industry, we find that Boeing’s optimal response to Airbus’ launch of the A380 super carrier is to accommodate entry and supplement its current product line, as opposed to the riskier alternative of committing to the development of a corresponding super jumbo.

Risk-neutral and real-world densities are derived from option prices and risk assumptions, and are compared with historical densities obtained from time series. Two parametric risk-transformations are used to convert risk-neutral densities into real-world densities. Both transformations are estimated by maximizing the likelihood of observed index levels, for two parametric density families. Results for the FTSE-100 index show that parametric densities derived from option prices have more explanatory power than historical densities and higher likelihoods than densities estimated by spline methods. A combination of parametric real-world and historical densities provides the preferred predictive densities.