12,928 research outputs found
A hybrid quantum Wasserstein GAN with applications to option pricing
This paper conducts an in-depth exploration of harnessing Machine Learning techniques in the context of Quantum methods for Option Pricing. We develop and implement a novel Hybrid Quantum Wasserstein GAN for loading arbitrary distributions from a classical state into a quantum state, which is of interest beyond its financial applications. In particular, our hybrid methodology eliminates several potential sources of instability and has superior performance compared to fully quantum generative models if the target distribution is classical.
Our qWGAN can be used to capture the probability distribution of an asset at maturity, and transmute it into a quantum state as demonstrated on synthetic as well as real data experiments. In an Option Pricing context we present a full pipeline using this methodology and leverage the Iterative Quantum Amplitude Estimation algorithm to derive the expected option payoff, ensuring a quadratic enhancement in error scaling compared to traditional methods
Evolving Ensemble Fuzzy Classifier
The concept of ensemble learning offers a promising avenue in learning from
data streams under complex environments because it addresses the bias and
variance dilemma better than its single model counterpart and features a
reconfigurable structure, which is well suited to the given context. While
various extensions of ensemble learning for mining non-stationary data streams
can be found in the literature, most of them are crafted under a static base
classifier and revisits preceding samples in the sliding window for a
retraining step. This feature causes computationally prohibitive complexity and
is not flexible enough to cope with rapidly changing environments. Their
complexities are often demanding because it involves a large collection of
offline classifiers due to the absence of structural complexities reduction
mechanisms and lack of an online feature selection mechanism. A novel evolving
ensemble classifier, namely Parsimonious Ensemble pENsemble, is proposed in
this paper. pENsemble differs from existing architectures in the fact that it
is built upon an evolving classifier from data streams, termed Parsimonious
Classifier pClass. pENsemble is equipped by an ensemble pruning mechanism,
which estimates a localized generalization error of a base classifier. A
dynamic online feature selection scenario is integrated into the pENsemble.
This method allows for dynamic selection and deselection of input features on
the fly. pENsemble adopts a dynamic ensemble structure to output a final
classification decision where it features a novel drift detection scenario to
grow the ensemble structure. The efficacy of the pENsemble has been numerically
demonstrated through rigorous numerical studies with dynamic and evolving data
streams where it delivers the most encouraging performance in attaining a
tradeoff between accuracy and complexity.Comment: this paper has been published by IEEE Transactions on Fuzzy System
Conformal prediction of option prices
The uncertainty associated with option price predictions has largely been overlooked
in the literature. This paper aims to fill this gap by quantifying such uncertainty using
conformal prediction. Conformal prediction is a model-agnostic procedure that
constructs prediction intervals, ensuring valid coverage in finite samples without
relying on distributional assumptions. Through the simulation of synthetic option
prices, we find that conformal prediction generates prediction intervals for gradient
boosting machines with an empirical coverage close to the nominal level. Conversely,
non-conformal prediction intervals exhibit empirical coverage levels that
fall short of the nominal target. In other words, they fail to contain the actual option
price more frequently than expected for a given coverage level. As anticipated,
we also observe a decrease in the width of prediction intervals as the size of the
training data increases. However, we uncover significant variations in the width of
these intervals across different options. Specifically, out-of-the-money options and
those with a short time-to-maturity exhibit relatively wider prediction intervals.
Then, we perform an empirical study using American call and put options on individual
stocks. We find that the empirical results replicate those obtained in the
simulation experiment.info:eu-repo/semantics/publishedVersio
Option Pricing using Quantum Computers
We present a methodology to price options and portfolios of options on a
gate-based quantum computer using amplitude estimation, an algorithm which
provides a quadratic speedup compared to classical Monte Carlo methods. The
options that we cover include vanilla options, multi-asset options and
path-dependent options such as barrier options. We put an emphasis on the
implementation of the quantum circuits required to build the input states and
operators needed by amplitude estimation to price the different option types.
Additionally, we show simulation results to highlight how the circuits that we
implement price the different option contracts. Finally, we examine the
performance of option pricing circuits on quantum hardware using the IBM Q
Tokyo quantum device. We employ a simple, yet effective, error mitigation
scheme that allows us to significantly reduce the errors arising from noisy
two-qubit gates.Comment: Fixed a typo. This article has been accepted in Quantu
EISim: A Platform for Simulating Intelligent Edge Orchestration Solutions
To support the stringent requirements of the future intelligent and
interactive applications, intelligence needs to become an essential part of the
resource management in the edge environment. Developing intelligent
orchestration solutions is a challenging and arduous task, where the evaluation
and comparison of the proposed solution is a focal point. Simulation is
commonly used to evaluate and compare proposed solutions. However, the
currently existing, openly available simulators are lacking in terms of
supporting the research on intelligent edge orchestration methods. To address
this need, this article presents a simulation platform called Edge Intelligence
Simulator (EISim), the purpose of which is to facilitate the research on
intelligent edge orchestration solutions. EISim is extended from an existing
fog simulator called PureEdgeSim. In its current form, EISim supports
simulating deep reinforcement learning based solutions and different
orchestration control topologies in scenarios related to task offloading and
resource pricing on edge. The platform also includes additional tools for
creating simulation environments, running simulations for agent training and
evaluation, and plotting results
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