3,699 research outputs found
Incorporating statistical model error into the calculation of acceptability prices of contingent claims
The determination of acceptability prices of contingent claims requires the
choice of a stochastic model for the underlying asset price dynamics. Given
this model, optimal bid and ask prices can be found by stochastic optimization.
However, the model for the underlying asset price process is typically based on
data and found by a statistical estimation procedure. We define a confidence
set of possible estimated models by a nonparametric neighborhood of a baseline
model. This neighborhood serves as ambiguity set for a multi-stage stochastic
optimization problem under model uncertainty. We obtain distributionally robust
solutions of the acceptability pricing problem and derive the dual problem
formulation. Moreover, we prove a general large deviations result for the
nested distance, which allows to relate the bid and ask prices under model
ambiguity to the quality of the observed data.Comment: 27 pages, 2 figure
Bounded Delay Scheduling with Packet Dependencies
A common situation occurring when dealing with multimedia traffic is having
large data frames fragmented into smaller IP packets, and having these packets
sent independently through the network. For real-time multimedia traffic,
dropping even few packets of a frame may render the entire frame useless. Such
traffic is usually modeled as having {\em inter-packet dependencies}. We study
the problem of scheduling traffic with such dependencies, where each packet has
a deadline by which it should arrive at its destination. Such deadlines are
common for real-time multimedia applications, and are derived from stringent
delay constraints posed by the application. The figure of merit in such
environments is maximizing the system's {\em goodput}, namely, the number of
frames successfully delivered.
We study online algorithms for the problem of maximizing goodput of
delay-bounded traffic with inter-packet dependencies, and use competitive
analysis to evaluate their performance. We present competitive algorithms for
the problem, as well as matching lower bounds that are tight up to a constant
factor. We further present the results of a simulation study which further
validates our algorithmic approach and shows that insights arising from our
analysis are indeed manifested in practice
Wikipedia and Digital Currencies: Interplay Between Collective Attention and Market Performance
The production and consumption of information about Bitcoin and other digital-, or 'crypto'-, currencies have grown together with their market capitalisation. However, a systematic investigation of the relationship between online attention and market dynamics, across multiple digital currencies, is still lacking. Here, we quantify the interplay between the attention towards digital currencies in Wikipedia and their market performance. We consider the entire edit history of currency-related pages, and their view history from July 2015. First, we quantify the evolution of the cryptocurrency presence in Wikipedia by analysing the editorial activity and the network of co-edited pages. We find that a small community of tightly connected editors is responsible for most of the production of information about cryptocurrencies in Wikipedia. Then, we show that a simple trading strategy informed by Wikipedia views performs better, in terms of returns on investment, than classic baseline strategies for most of the covered period. Our results contribute to the recent literature on the interplay between online information and investment markets, and we anticipate it will be of interest for researchers as well as investors
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