Hasil untuk "q-fin.CP"

M. Rodahl, F. Höök, A. Krozer et al.

C. Sackett, D. Kielpinski, B. King et al.

L. Biedenharn

A. Macfarlane

M. Khashei, M. Bijari

Hansuek Lee, Tong Chen, Jiang Li et al.

Bahare Kiumarsi-Khomartash, F. Lewis, H. Modares et al.

Ripal Nathuji, A. Kansal, Alireza Ghaffarkhah

A. Aral, Vijay Gupta, R. Agarwal

Bohua Chen, Xiaoyan Zhang, Kan Wu et al.

In this paper, we report 4 different saturable absorbers based on 4 transition metal dichalcogenides (MoS(2), MoSe(2), WS(2), WSe(2)) and utilize them to Q-switch a ring-cavity fiber laser with identical cavity configuration. It is found that MoSe(2) exhibits highest modulation depth with similar preparation process among four saturable absorbers. Q-switching operation performance is compared from the aspects of RF spectrum, optical spectrum, repetition rate and pulse duration. WS(2) Q-switched fiber laser generates the most stable pulse trains compared to other 3 fiber lasers. These results demonstrate the feasibility of TMDs to Q-switch fiber laser effectively and provide a meaningful reference for further research in nonlinear fiber optics with these TMDs materials.

L. Accardo, M. Aguilar, D. Aisa et al.

Christopher Potter, Bosiljka Tasic, Emilie V Russler et al.

Zhipeng Huang, Cornelis W. Oosterlee

We propose the Compound BSDE method, a fully forward, deep-learning-based approach for solving a broad class of problems in financial mathematics, including optimal stopping. The method is based on a reformulation of option pricing problems in terms of a system of backward stochastic differential equations (BSDEs), which offers a new perspective on the numerical treatment of compound options and optimal stopping problems such as Bermudan option pricing. Building on the classical deep BSDE method for a single BSDE, we develop an algorithm for compound BSDEs and establish its convergence properties. In particular, we derive an a posteriori error estimate for the proposed method. Numerical experiments demonstrate the accuracy and computational efficiency of the approach, and illustrate its effectiveness for high-dimensional option pricing and optimal stopping problems.

B. J. Barros, T. Barreiro, T. Koivisto et al.

A Bayesian statistical analysis using redshift space distortions data is performed to test a model of Symmetric Teleparallel Gravity where gravity is non-metrical. The cosmological background mimics a $\Lambda$CDM evolution but differences arise in the perturbations. The linear matter fluctuations are numerically evolved and the study of the growth rate of structures is analysed in this cosmological setting. The best fit parameters reveal that the $\sigma_8$ tension between Planck and Large Scale Structure data can be alleviated within this framework.

F. Bajardi, D. Vernieri, S. Capozziello

We consider f(Q) extended symmetric teleparallel cosmologies, where Q is the non-metricity scalar, and constrain its functional form through the order reduction method. By using this technique, we are able to reduce and integrate the field equations and thus to select the corresponding models giving rise to bouncing cosmology. The selected Lagrangian is then used to develop the Hamiltonian formalism and to obtain the Wave Function of the Universe which suggests that classical observable universes can be recovered according to the Hartle Criterion.

Haojie Liu, Zihan Lin, Randall R. Rojas

This study integrates real-time sentiment analysis from financial news, GPT-2 and FinBERT, with technical indicators and time-series models like ARIMA and ETS to optimize S&P 500 trading strategies. By merging sentiment data with momentum and trend-based metrics, including a benchmark buy-and-hold and sentiment-based approach, is evaluated through assets values and returns. Results show that combining sentiment-driven insights with traditional models improves trading performance, offering a more dynamic approach to stock trading that adapts to market changes in volatile environments.

Giovanni Bonaccolto, Nicola Borri, Andrea Consiglio et al.

This paper investigates the dynamic interdependencies between the European insurance sector and key financial markets-equity, bond, and banking-by extending the Generalized Forecast Error Variance Decomposition framework to a broad set of performance and risk indicators. Our empirical analysis, based on a comprehensive dataset spanning January 2000 to October 2024, shows that the insurance market is not a passive receiver of external shocks but an active contributor in the propagation of systemic risk, particularly during periods of financial stress such as the subprime crisis, the European sovereign debt crisis, and the COVID-19 pandemic. Significant heterogeneity is observed across subsectors, with diversified multiline insurers and reinsurance playing key roles in shock transmission. Moreover, our granular company-level analysis reveals clusters of systemically central insurance companies, underscoring the presence of a core group that consistently exhibits high interconnectivity and influence in risk propagation.

Jasper Rou

In this research, we explore neural network-based methods for pricing multidimensional American put options under the BlackScholes and Heston model, extending up to five dimensions. We focus on two approaches: the Time Deep Gradient Flow (TDGF) method and the Deep Galerkin Method (DGM). We extend the TDGF method to handle the free-boundary partial differential equation inherent in American options. We carefully design the sampling strategy during training to enhance performance. Both TDGF and DGM achieve high accuracy while outperforming conventional Monte Carlo methods in terms of computational speed. In particular, TDGF tends to be faster during training than DGM.

Giovanni Ballarin, Jacopo Capra, Petros Dellaportas

Stock return prediction is a problem that has received much attention in the finance literature. In recent years, sophisticated machine learning methods have been shown to perform significantly better than ''classical'' prediction techniques. One downside of these approaches is that they are often very expensive to implement, for both training and inference, because of their high complexity. We propose a return prediction framework for intraday returns at multiple horizons based on Echo State Network (ESN) models, wherein a large portion of parameters are drawn at random and never trained. We show that this approach enjoys the benefits of recurrent neural network expressivity, inherently efficient implementation, and strong forecasting performance.

A. Sirunyan, A. Tumasyan, W. Adam et al.