5,450 research outputs found
A tight security reduction in the quantum random oracle model for code-based signature schemes
Quantum secure signature schemes have a lot of attention recently, in
particular because of the NIST call to standardize quantum safe cryptography.
However, only few signature schemes can have concrete quantum security because
of technical difficulties associated with the Quantum Random Oracle Model
(QROM). In this paper, we show that code-based signature schemes based on the
full domain hash paradigm can behave very well in the QROM i.e. that we can
have tight security reductions. We also study quantum algorithms related to the
underlying code-based assumption. Finally, we apply our reduction to a concrete
example: the SURF signature scheme. We provide parameters for 128 bits of
quantum security in the QROM and show that the obtained parameters are
competitive compared to other similar quantum secure signature schemes
A Hedged Monte Carlo Approach to Real Option Pricing
In this work we are concerned with valuing optionalities associated to invest
or to delay investment in a project when the available information provided to
the manager comes from simulated data of cash flows under historical (or
subjective) measure in a possibly incomplete market. Our approach is suitable
also to incorporating subjective views from management or market experts and to
stochastic investment costs. It is based on the Hedged Monte Carlo strategy
proposed by Potters et al (2001) where options are priced simultaneously with
the determination of the corresponding hedging. The approach is particularly
well-suited to the evaluation of commodity related projects whereby the
availability of pricing formulae is very rare, the scenario simulations are
usually available only in the historical measure, and the cash flows can be
highly nonlinear functions of the prices.Comment: 25 pages, 14 figure
A tutorial on estimator averaging in spatial point process models
Assume that several competing methods are available to estimate a parameter
in a given statistical model. The aim of estimator averaging is to provide a
new estimator, built as a linear combination of the initial estimators, that
achieves better properties, under the quadratic loss, than each individual
initial estimator. This contribution provides an accessible and clear overview
of the method, and investigates its performances on standard spatial point
process models. It is demonstrated that the average estimator clearly improves
on standard procedures for the considered models. For each example, the code to
implement the method with the R software (which only consists of few lines) is
provided
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