Contributions in Merlin

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Many traditional signal processing techniques in finance have limited ability to explain trading processes and distributional properties of the actual market prices. This is typically manifested in model misspecification and pricing and forecasting inaccuracy. For instance, the assumption that log stock returns are normally distributed is widely used in modern mathematical finance.

Der Schweizer Schienengüterverkehr steht vor wesentlichen Herausforderungen. Zum einen sind die Anbieter einem starken intra- und intermodalen Wettbewerb ausgesetzt; zum anderen spiegeln die gesetzlichen Rahmenbedingungen vielfältige, sich teils widersprechende politische Anliegen im Bereich der Versorgung, Verlagerung und Nichtdiskriminierung neuer Anbieter wider. Der vorliegende Beitrag analysiert den heutigen regulatorischen Rahmen im Schienengüterverkehr in der Schweiz. Ausgehend von den verfolgten politischen Zielen und der ökonomischen Theorie wird dieser kritisch zusammengefasst und gewürdigt. In einem zweiten Schritt wird der Handlungsbedarf abgeleitet zur kohärenten Weiterentwicklung des Regulierungsrahmens für den Schienengüterverkehr in der Schweiz.

Applied researchers often test for the difference of the variance of two investment strategies; in particular, when the investment strategies under consideration aim to implement the global minimum variance portfolio. A popular tool to this end is the F-test for the equality of variances. Unfortunately, this test is not valid when the returns are correlated, have tails heavier than the normal distribution, or are of time series nature. Instead, we propose the use of robust inference methods. In particular, we suggest to construct a studentized time series bootstrap confidence interval for the ratio of the two variances and to declare the two variances different if the value one is not contained in the obtained interval. This approach has the advantage that one can simply resample from the observed data as opposed to some null-restricted data. A simulation study demonstrates the improved finite-sample performance compared to existing methods.

Trackers are used in peer-to-peer (P2P) networks for provider discovery, that is, mapping resources to potential providers. Centralized trackers, e.g., as in the original BitTorrent protocol, do not benefit from P2P properties, such as no single point of failure, scalability, and load balancing. Decentralized mechanisms have thus been proposed, based on distributed hash tables (DHTs) and gossiping, such as BitTorrent's Peer Exchange (PEX). While DHT-based trackers suffer from load balancing problems, gossip-based ones cannot deliver new mappings quickly. This paper presents B-Tracker, a fully-distributed, pull-based tracker. B-Tracker extends DHT functionality by distributing the tracker load among all providers in a swarm. Bloom filters are used to avoid redundant mappings to be transmitted. This results in the important properties of load balancing and scalability, while adding the ability for peers to fetch new mappings instantly. B-Tracker shows, through simulations, improved load balancing and better efficiency when compared to pure DHTs and PEX.

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We consider a canonically perturbed system of (in general) infinitely many inequalities defined by continuously differentiable functions in finite or infinite dimension. Calmness of the solution set map of this system is characterized, on the one hand, by uniform linear convergence of a suitable iterative procedure for solving an associated bounded-norm system of linear inequalities and, on the other hand, by a certain constraint qualification. We also show how to apply this to the optimal set map in semi-infinite optimization.