arXiv subjects

Abdul Khaliq

Publications and source records attributed to Abdul Khaliq.

6 recordsLinked to original sources

arXiv2025

The Impact of COVID-19 on FinTech Lending in Indonesia: Evidence From Interrupted Time Series Analysis

This study measures the impact of COVID-19 outbreaks on financial technology (FinTech) lending in Indonesia. Using monthly FinTech data published by Financial Services Authority (OJK) over the period 2018M02-2021M04, the article examines the impact of COVID-19 started on March 2020 on FinTech by adopting an interrupted time series (ITS) experiment. The estimation shows that the COVID-19 outbreaks negatively affect changes in FinTech lending level in Indonesia, but the changes in the trend are positive. Moreover, the COVID-19 has been found to have a negative and statistically significant effect on the 90-day success loan settlement rate level. However, COVID-19 has positive and statistically significant effects on the 90-day default rate of loan repayment level. These estimation results recommend that the financial services authority of Indonesia should intensively promote various innovative financial technology (FinTech) lending post-COVID-19 to increase digital financial inclusion by providing peer to peer lending (P2P) to unbanked populations.

econ.GN↗

arXiv2025

The quest for explosive bubbles in the Indonesian Rupiah/US exchange rate: Does the uncertainty trinity matter?

The Generalized Supremum Augmented Dickey-Fuller (GSADF) technique is performed to resolve whether the Indonesian Rupiah/US exchange rate has experienced multiple explosive bubbles. The GSADF uncovers that the Indonesian Rupiah/US exchange rate deviates from the fundamental values by six times from January 1985 to September 2023, periodically indicating the presence of numerous explosive behaviors. Once the full-sample period separates into the managed-floating regime and the free-floating regime, the GSADF still detects multiple bubbles. Of particular curiosity on uncertainty trinity, this study underlines that global geopolitical risk negatively drives explosive actions in the ratio of exchange rates for non-traded and traded goods. The global economic policy uncertainty negatively affects speculative bubbles in the exchange rate and the ratio of exchange rates for non-traded. The country's geopolitical risks negatively strike only speculative bubbles in the exchange rate. Further, we find heterogeneity in our results by examining different exchange rate systems. The robustness checks further firmly ascertain across baseline empirical findings.

econ.GN↗

arXiv2025

Quantum Recurrent Neural Networks with Encoder-Decoder for Time-Dependent Partial Differential Equations

Nonlinear time-dependent partial differential equations are essential in modeling complex phenomena across diverse fields, yet they pose significant challenges due to their computational complexity, especially in higher dimensions. This study explores Quantum Recurrent Neural Networks within an encoder-decoder framework, integrating Variational Quantum Circuits into Gated Recurrent Units and Long Short-Term Memory networks. Using this architecture, the model efficiently compresses high-dimensional spatiotemporal data into a compact latent space, facilitating more efficient temporal evolution. We evaluate the algorithms on the Hamilton-Jacobi-Bellman equation, Burgers' equation, the Gray-Scott reaction-diffusion system, and the three dimensional Michaelis-Menten reaction-diffusion equation. The results demonstrate the superior performance of the quantum-based algorithms in capturing nonlinear dynamics, handling high-dimensional spaces, and providing stable solutions, highlighting their potential as an innovative tool in solving challenging and complex systems.

cs.LG↗

arXiv2023

Magnetic phase diagram of Ge1-x-ySnxMnyTe multiferroic semiconductors; coexistence of ferromagnetic and cluster glass ordering

We report the structural and magnetic results of polar α-GeTe doped with Sn and Mn from x = 0.185 to 0.841 and y = 0.02 to 0.086, respectively. The magnetic results of Ge1-x-y(SnxMny)Te (GSMT) crystals identify Mn-clustering effect with scaling parameter, R = 0.033 for x ~ 0.2 and y = 0.06. The excessive Sn ions are assumed to drive the inception of short range ferromagnetic clusters. For the crystals revealing glassy magnetic behavior, the irreversibility temperature, Tirr shifts at high dc magnetic field which is well described by de Almeida-Thouless equation, δTirr ~ HΦ/2 yielding Φ values of 1.55 and 1.7 that validates the formation of Mn clusters. The spin relaxation time, τ0 ~ 10-9 s, activation energy, Ea/kB ~ 5 TF where TF is freeing temperature and Vogel-Fulcher temperature, T0 ~ TF also signify intracluster interactions. Also, the Mn-hole magnetic exchange constant, Jpd drops from 0.24 eV for the samples with smaller Sn content of x ~ 0.2 to 0.16 eV for Sn rich alloy with x ~ 0.8. Consequently, we present a magnetic phase diagram for GSMT bulk crystals as a function of Mn(y) ions.

cond-mat.mtrl-sci↗

arXiv2021

Identification and prediction of time-varying parameters of COVID-19 model: a data-driven deep learning approach

Data-driven deep learning provides efficient algorithms for parameter identification of epidemiology models. Unlike the constant parameters, the complexity of identifying time-varying parameters is largely increased. In this paper, a variant of physics-informed neural network (PINN) is adopted to identify the time-varying parameters of the Susceptible-Infectious-Recovered-Deceased model for the spread of COVID-19 by fitting daily reported cases. The learned parameters are verified by utilizing an ordinary differential equation solver to compute the corresponding solutions of this compartmental model. The effective reproduction number based on these parameters is calculated. Long Short-Term Memory (LSTM) neural network is employed to predict the future weekly time-varying parameters. The numerical simulations demonstrate that PINN combined with LSTM yields accurate and effective results.

math.DS↗

arXiv2018

Slow-scale split-step tau-leap method for stiff stochastic chemical systems

Tau-leaping is a family of algorithms for the approximate simulation of the discrete state continuous time Markov chains. Motivation for the development of such methods can be found, for instance, in the fields of chemical kinetics and systems biology. It is known that the dynamical behavior of biochemical systems is often intrinsically stiff representing a serious challenge for their numerical approximation. The naive extension of stiff deterministic solvers to stochastic integration often yields numerical solutions with either impractically large relaxation times or incorrectly resolved covariance. In this paper, we propose a splitting heuristic which helps to resolve some of these issues. The proposed integrator contains a number of unknown parameters which are estimated for each particular problem from the moment equations of the corresponding linearized system. We show that this method is able to reproduce the exact mean and variance of the linear scalar test equation and demonstrates a good accuracy for the arbitrarily stiff systems at least in the linear case. The numerical examples for both linear and nonlinear systems are also provided and the obtained results confirm the efficiency of the considered splitting approach.

math.PR↗