Implied volatility of basket options at extreme strikes

In the paper, we characterize the asymptotic behavior of the implied volatility of a basket call option at large and small strikes in a variety of settings with increasing generality. First, we obtain an asymptotic formula with an error bound for the left wing of the implied volatility, under the assumption that the dynamics of asset prices are described by the multidimensional Black-Scholes model. Next, we find the leading term of asymptotics of the implied volatility in the case where the asset prices follow the multidimensional Black-Scholes model with time change by an independent increasing stochastic process. Finally, we deal with a general situation in which the dependence between the assets is described by a given copula function. In this setting, we obtain a model-free tail-wing formula that links the implied volatility to a special characteristic of the copula called the weak lower tail dependence function

Constructing self-similar martingales via two Skorokhod embeddings

With the help of two Skorokhod embeddings, we construct martingales which enjoy the Brownian scaling property and the (inhomogeneous) Markov property. The second method necessitates randomization, but allows to reach any law with finite moment of order 1, centered, as the distribution of such a martingale at unit time. The first method does not necessitate randomization, but an additional restriction on the distribution at unit time is needed. Key words: Skorokhod embeddings, Hardy-Littlewood functions, convex order, Schauder fixed point theorem, self-similar martingales, Karamata’s representation theorem

Exact tail asymptotics of the supremum of strongly dependent gaussian processes over a random interval

Let be a positive random variable independent of a real-valued stochastic process . In this paper, we investigate the asymptotic behavior of as u -> a assuming that X is a strongly dependent stationary Gaussian process and has a regularly varying survival function at infinity with index lambda a [0, 1). Under asymptotic restrictions on the correlation function of the process, we show that with some positive finite constant c and function m(center dot) defined in terms of the local behavior of the correlation function and the standard Gaussian distribution