207 research outputs found
PAMS: Platform for Artificial Market Simulations
This paper presents a new artificial market simulation platform, PAMS:
Platform for Artificial Market Simulations. PAMS is developed as a Python-based
simulator that is easily integrated with deep learning and enabling various
simulation that requires easy users' modification. In this paper, we
demonstrate PAMS effectiveness through a study using agents predicting future
prices by deep learning.Comment: 7page
Adversarial Deep Hedging: Learning to Hedge without Price Process Modeling
Deep hedging is a deep-learning-based framework for derivative hedging in
incomplete markets. The advantage of deep hedging lies in its ability to handle
various realistic market conditions, such as market frictions, which are
challenging to address within the traditional mathematical finance framework.
Since deep hedging relies on market simulation, the underlying asset price
process model is crucial. However, existing literature on deep hedging often
relies on traditional mathematical finance models, e.g., Brownian motion and
stochastic volatility models, and discovering effective underlying asset models
for deep hedging learning has been a challenge. In this study, we propose a new
framework called adversarial deep hedging, inspired by adversarial learning. In
this framework, a hedger and a generator, which respectively model the
underlying asset process and the underlying asset process, are trained in an
adversarial manner. The proposed method enables to learn a robust hedger
without explicitly modeling the underlying asset process. Through numerical
experiments, we demonstrate that our proposed method achieves competitive
performance to models that assume explicit underlying asset processes across
various real market data.Comment: 8 pages, 7 figure
Error Analysis of Option Pricing via Deep PDE Solvers: Empirical Study
Option pricing, a fundamental problem in finance, often requires solving
non-linear partial differential equations (PDEs). When dealing with multi-asset
options, such as rainbow options, these PDEs become high-dimensional, leading
to challenges posed by the curse of dimensionality. While deep learning-based
PDE solvers have recently emerged as scalable solutions to this
high-dimensional problem, their empirical and quantitative accuracy remains not
well-understood, hindering their real-world applicability. In this study, we
aimed to offer actionable insights into the utility of Deep PDE solvers for
practical option pricing implementation. Through comparative experiments, we
assessed the empirical performance of these solvers in high-dimensional
contexts. Our investigation identified three primary sources of errors in Deep
PDE solvers: (i) errors inherent in the specifications of the target option and
underlying assets, (ii) errors originating from the asset model simulation
methods, and (iii) errors stemming from the neural network training. Through
ablation studies, we evaluated the individual impact of each error source. Our
results indicate that the Deep BSDE method (DBSDE) is superior in performance
and exhibits robustness against variations in option specifications. In
contrast, some other methods are overly sensitive to option specifications,
such as time to expiration. We also find that the performance of these methods
improves inversely proportional to the square root of batch size and the number
of time steps. This observation can aid in estimating computational resources
for achieving desired accuracies with Deep PDE solvers.Comment: 11 pages, 6 figure
Vortex state in double transition superconductors
Novel vortex phase and nature of double transition field are investigated by
two-component Ginzburg-Landau theory in a situation where fourfold-twofold
symmetric superconducting double transition occurs. The deformation from 60
degree triangular vortex lattice and a possibility of the vortex sheet
structure are discussed. In the presence of the gradient coupling, the
transition changes to a crossover at finite fields. These characters are
important to identify the multiple superconducting phase in PrOs_4_Sb_12.Comment: 4 pages, 4 figures, to appear in Phys. Rev. Let
Model-Free Idealization: Adaptive Integrated Approach for Idealization of Ion Channel Currents (AI2)
Single-channel electrophysiological recordings provide insights into
transmembrane ion permeation and channel gating mechanisms. The first step in
the analysis of the recorded currents involves an "idealization" process, in
which noisy raw data are classified into two discrete levels corresponding to
the open and closed states of channels. This provides valuable information on
the gating kinetics of ion channels. However, the idealization step is often
challenging in cases of currents with poor signal-to-noise ratios (SNR) and
baseline drifts, especially when the gating model of the target channel is not
identified. We report herein on a highly robust model-free idealization method
for achieving this goal. The algorithm, called AI2 (Adaptive Integrated
Approach for the Idealization of Ion Channel Currents), is composed of Kalman
filter and Gaussian Mixture Model (GMM) clustering and functions without user
input. AI2 automatically determines the noise reduction setting based on the
degree of separation between the open and closed levels. We validated the
method on pseudo-channel-current datasets which contain either computed or
experimentally recorded noise. The AI2 algorithm was then tested on actual
experimental data for biological channels including gramicidin A, a
voltage-gated sodium channel, and other unidentified channels. We compared the
idealization results with those obtained by the conventional methods, including
the 50%-threshold-crossing method
Juvenile Bow Hunter’s Stroke without Hemodynamic Changes
Bow hunter’s stroke (BHS) is a cerebrovascular disease caused by occlusion of the vertebral artery (VA) on head rotation. BHS is generally associated with hemodynamic changes, often leading to vertebrobasilar insufficiency symptoms, such as vertigo and faintness. Although artery-to-artery embolism has also been proposed as an underlying mechanism, it remains controversial. This report documents a case of BHS without hemodynamic changes. We describe a 26-year-old male patient who had VA occlusion on head rotation and repetitive infarction of thalami. He had an anomalous bypass of the VA and therefore no symptomatic hemodynamic changes. Thus, non-hemodynamic BHS should be considered in juvenile patients with vertebrobasilar stroke
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