We derive an estimator for Black-Scholes-Merton implied volatility that, when compared to the familiar Corrado & Miller [JBaF, 1996] estimator, has substantially higher approximation accuracy and extends over a wider region of moneyness.

This paper develops a novel statistic for firm efficiency called efficiency depth that allows for statistical inference in case of errors-in-variables. We derive statistical tests that require minimal statistical assumptions; neither the sample distribution nor the noise level is required. An...

We consider the issues of noise-to-signal estimation, finite sample performance and hypothesis testing for the nonparametric efficiency estimation technique proposed in Cherchye, L., T. Kuosmanen and G. T. Post (2001) 'Nonparametric efficiency estimation in stochastic environments', forthcoming...

We explore the flow-performance interrelation by explicitly separating the investment and divestment decisions of hedge fund investors. The results show that different determinants and evaluation horizons underlie both decisions. While money inflows are sensitive to past long-run performance,...

We compare single factor Markov-functional and multi factor market models for hedging performance of Bermudan swaptions. We show that hedging performance of both models is comparable, thereby supporting the claim that Bermudan swaptions can be adequately riskmanaged with single factor models....

Hedge funds databases are typically subject to high attrition rates because of fund termination and self-selection. Even when all funds are included up to their last available return, one cannot prevent that ex post conditioning biases a.ect standard estimates of performance persistence. In this...

Currently, there are two market models for valuation and risk management of interest rate derivatives, the LIBOR and swap market models. In this paper, we introduce arbitrage-free constant maturity swap (CMS) market models and generic market models featuring forward rates that span periods other...

Geometric optimisation algorithms are developed that efficiently find the nearest low-rank correlation matrix. We show, in numerical tests, that our methods compare favourably to the existing methods in the literature. The connection with the Lagrange multiplier method is established, along with...