Knowledge that Transforms

In January 1999, the European monetary union (EMU) was formally launched with 11 member countries. However, before May 1998 there was considerable uncertainty about who would join EMU, and whether the project would start on time. When a monetary union is formed, exchange rates between the member countries are irrevocably fixed, and yield spreads stemming from exchange-rate risk are eliminated. As a direct consequence, EMU affected the prices of long-term bonds well before 1999, but quantifying this effect can be difficult when there is uncertainty about the monetary union. We address these issues and develop a bond-pricing model which explicitly takes into account that a country may join a monetary union at a future, unspecified date. The empirical results show that a narrow EMU, consisting of Germany, France and the Benelux countries, has been priced with almost 100% probability throughout the period 1995—1998, whereas, on average, the implied probability of joining EMU has been somewhat lower for the other EU countries. However, in the period leading up to May 1998, the estimated probabilities have increased considerably for the countries that joined EMU in January 1999.

The option pricing model has been the work horse in the development of financial economics ever since the seminal papers by Black and Scholes (1973) and Merton (1973). Here, the arbitrage free prices of equity options were derived in closed form. Apart from the price of the underlying asset and contractual terms (strike

The paper analyzes the role of agency driven takeover activity. The analysis shows that takeovers can play an important role in reducing agency costs even though the gains from the corporate restructuring that follows the takeovers are zero, which counters existing models of agency driven takeover activity. The model can therefore form the basis for deriving empirical predictions which discriminate between the "agency paradigm" and the "corporate restructuring paradigm" of takeover activity. Negative post-merger performance

Richard A. Brealey, Ian A. Cooper, Evi Kaplanis; What is the International Dimension of International Finance?, European Finance Review, Volume 3, Issue 1,

An important determinant of option prices is the elasticity of the pricing kernel used to price all claims in the economy. In this paper, we first show that for a given forward price of the underlying asset, option prices are higher when the elasticity of the pricing kernel is declining than when it is constant. We then investigate the implications of the elasticity of the pricing kernel for the stochastic process followed by the underlying asset. Given that the underlying information process follows a geometric Brownian motion, we demonstrate that constant elasticity of the pricing kernel is equivalent to a Brownian motion for the forward price of the underlying asset, so that the Black–Scholes formula correctly prices options on the asset. In contrast, declining elasticity implies that the forward price process is no longer a Brownian motion: it has higher volatility and exhibits autocorrelation. In this case, the Black–Scholes formula underprices all options.

This note discusses stochastic discount factor (SDF) measures of mutual fund performance. It shows that the most common SDF performance measures can be interpreted as Jensen's “alphas”.

Risk management methods based on Value-at-Risk estimate the lowest quantile of possible profits and losses over a fixed time horizon. To calculate this value there is a need to construct an approximation of the probabilistic distribution of P&L. One of the most popular techniques is based on an assumption that the portfolio value can be expressed as a deterministic function of some basic market parameters. Having a distribution of these parameters one can construct the distribution of the value function. The most popular method is delta approach. Here a first order expansion of the value function is used in order to approximate the distribution at the end of the period. Typically the time period is assumed short, in which case the changes in market parameters are distributed almost normally and under this linear approximation the value of the approximated portfolio is also normally distributed. Value-at-Risk methods based on a delta approximation can not take into account different forms of convexity. An appropriate solution to this problem is to consider a longer series expansion, for example the so-called delta-gamma approximation. However the delta-gamma approximation loses a very useful property of “delta only” approach ‐ linearity. This linearity property is very convenient computationally, since it guarantees that as soon as the market factors are distributed normally, the resulting changes in the portfolio value are also normally distributed. Denote by x a vector of n market parameters that can be easily measured and their historical distributions are known. For example stock prices, interest rates, exchange rates. Denoting the calendar time by t we can price a portfolio of assets V.t, x/. The Value-at-Risk measures the lowest 1% (sometimes 5%) quantile of the distribution of profits and losses of the fixed portfolio over a fixed time horizon (in banking for example 10 business days). The standard assumption of this measurement is that over a short time horizon the changes in the market factors 1x are normally distributed. If the value of the portfolio is linear in the market factors then the P&L distribution is normal as well and any quantile can be expressed analytically through its mean and standard deviation. However the assumption of linear dependence is often very restrictive, a higher order approximation is required to reflect convexity. Consider the value functionV.x,t/around the current market valuesx. As soon as the market changes are small and the function V smooth, we can use the Taylor expansion. However the variablex is stochastic. Thus instead of the standard series

Edwin J. Elton, Martin J. Gruber; Reflections on the Origins of the European Finance Association, European Finance Review, Volume 3, Issue 1, 1 January 199

This paper analyzes the relationship between a firm's capital structure and its information acquisition prior to capital budgeting decisions. It is found that low-growth industries can sustain a large number of levered firms. In these industries, leverage is negatively related to a firm's incentive to acquire information during the capital budgeting process. In contrast, high-growth industries only sustain a small number of levered firms. In these industries, levered firms acquire more information than all-equity financed firms. The model yields empirical predictions regarding the effects of leverage on the expected amount and the volatility of corporate investment.While leverage does not affect firm value, highly levered firms generate a more volatile cash flow than firms with low debt levels.

This article examines the contribution of hedging to firm value and the cost of hedging in a unified framework. Optimal hedging and firm value are explicitly linked to firm risk, the type of debt covenants and the relative priority of the hedging contract. It is shown that in some cases hedging is possible only if the counterparty to the forward contract also holds a significant portion of the debt. Also, the spread in the hedging contract reduces the optimal amount of hedging to less than the minimum-variance hedge ratio. Among other results this article elucidates why some firms hedge using forward contracts while other firms hedge in the futures markets, as well as why higher priority forward contracts are more efficient hedging vehicles.