Leveraging machine learning for high-dimensional option pricing within the uncertain volatility model

This paper aims to study the asymptotic behavior of double barrier American-style put option prices under an uncertain volatility model, which degenerates to a single point. We give an approximation of the double barrier American-style option prices with a small volatility interval, expressed by the Black–Scholes–Barenblatt equation. Then, we propose a novel representation for the early exercise boundary of American-style double barrier options in terms of the optimal stopping boundary of a single barrier contract.

The option pricing model with constant volatility in illiquid markets has been expanded by introducing two uncertain volatility models in this paper volatility. To conquer some insufficient existed in some literature, for example the time step should be small enough to satisfy the stability, the implicit difference equation has been established and numerical solution of the modified model with uncertain volatility has been discussed. Numerical results show that the method is nice and the accurate results can be gained with less computation.

Posttraumatic stress disorder (PTSD) recently becomes one of the most important mental health concerns. However, no previous study has comprehensively reviewed the application of big data and machine learning (ML) techniques in PTSD. We found 873 studies meet the inclusion criteria and a total of 31 of those in a sample of 210,001 were included in quantitative analysis. ML algorithms were able to discriminate PTSD with an overall accuracy of 0.89. Pooled estimates of classification accuracy from multi-dimensional data (0.96) are higher than single data types (0.86 to 0.90). ML techniques can effectively classify PTSD and models using multi-dimensional data perform better than those using single data types. While selecting optimal combinations of data types and ML algorithms to be clinically applied at the individual level still remains a big challenge, these findings provide insights into the classification, identification, diagnosis and treatment of PTSD.

This survey presents an in-depth analysis of machine learning techniques applied to lidar observations for the detection of aerosol and cloud optical, geometrical, and microphysical properties. Lidar technology, with its ability to probe the atmosphere at very high spatial and temporal resolution and measure backscattered signals, has become an invaluable tool for studying these atmospheric components. However, the complexity and diversity of lidar technology requires advanced data processing and analysis methods, where machine learning has emerged as a powerful approach. This survey focuses on the application of various machine learning techniques, including supervised and unsupervised learning algorithms and deep learning models, to extract meaningful information from lidar observations. These techniques enable the detection, classification, and characterization of aerosols and clouds by leveraging the rich features contained in lidar signals. In this article, an overview of the different machine learning architectures and algorithms employed in the field is provided, highlighting their strengths, limitations, and potential applications. Additionally, this survey examines the impact of machine learning techniques on improving the accuracy, efficiency, and robustness of aerosol and cloud real-time detection from lidar observations. By synthesizing the existing literature and providing critical insights, this survey serves as a valuable resource for researchers, practitioners, and students interested in the application of machine learning techniques to lidar technology. It not only summarizes current state-of-the-art methods but also identifies emerging trends, open challenges, and future research directions, with the aim of fostering advancements in this rapidly evolving field.

In recent years, option pricing has garnered significant attention as a critical topic in financial engineering. Classical models, such as Black-Scholes, exhibit limitations in accurately capturing the dynamic changes in complex market environments. Consequently, machine learning techniques and neural network methods have emerged as pivotal research directions in option pricing. This paper provides a systematic review of the advances in option pricing based on optimization methods, machine learning techniques, and neural network models. Optimization methods, incorporating techniques like genetic algorithms and particle swarm optimization, significantly enhance the adaptability and accuracy of classical models. Machine learning methods, such as support vector machines and decision trees, demonstrate their advantages in nonlinear data modeling. Neural networks and deep learning models, with their robust nonlinear fitting capabilities, have become central tools in pricing complex financial products. Additionally, this review summarizes the application of hybrid models and evaluates the strengths and weaknesses of various approaches in practical applications. Finally, the paper discusses future research directions, including the integration of reinforcement learning techniques, improvement in model interpretability, and deep fusion of heterogeneous multi-source data. This work aims to provide theoretical references and technical support for further research in the field of option pricing.

Today, intrusion detection system using the neural network is an interested and considerable area for the research community. The computational intelligence systems are defined on the basis of the following parameters: fault tolerance and adaptation; adaptable the requirements of make a better intrusion detection model. In this paper, provide an overview of the research progress using computational intelligence to the problem of intrusion detection. The goal of this paper summarized and compared research contributions of Intrusion detection system using computational intelligence and neural network, define existing research challenges and anticipated solution of machine learning. Research showed that application of machine learning techniques in intrusion detection could achieve high detection rate. Machine learning and classification algorithms help to design "Intrusion Detection Models" which can classify the network traffic into intrusive or normal traffic. This paper discusses some commonly used machine learning techniques in Intrusion Detection System and also reviews some of the existing machine learning IDS proposed by researchers at different times.

ABSTRACTThis paper reviews the activity-travel behaviour literature that employs Machine Learning (ML) techniques for empirical analysis and modelling. Machine Learning algorithms, which attempt to build intelligence utilizing the availability of large amounts of data, have emerged as powerful tools in the fields of pattern recognition and big data analysis. These techniques have been applied in activity-travel behaviour studies since the early ’90s when Artificial Neural Networks (ANN) were employed to model mode choice decisions. AMOS, an activity-based modelling system developed in the mid-’90s, has ANN at its core to model and predict individual responses to travel demand management measures. In the dawn of 2000, ALBATROSS, a comprehensive activity-based travel demand modelling system, was proposed by Arentze and Timmermans using Decision Trees. Since then researchers have been exploring ML techniques like Support Vector Machines (SVM), Decision Trees (DT), Neural Networks (NN), Bayes Classifiers, and more recently, Ensemble Learners to model and predict activity-travel behaviour. A large number of publications over the years and an upward trend in the number of published articles over time indicate that Machine Learning is a promising tool for activity-travel behaviour analysis and prediction. This article, first of its kind in the literature, reviews these studies and explores the trends in activity-travel behaviour research that apply ML techniques. The review finds that mode choice decisions have received wide attention in the literature on ML applications. It was observed that most of the studies identify the lack of interpretability as a serious shortcoming in ML techniques. However, very few studies have attempted to improve the interpretability of the models. Further, some studies report the importance of feature engineering in ML-based studies, but very few studies adopt feature engineering before model development. Spatiotemporal transferability of models is another issue that has received minimal attention in the literature. In the end, the paper discusses possible directions for future research in the area of activity-travel behaviour modelling using ML techniques.

We investigate the applicability of machine learning techniques in studying the finite-size effects associated with many-body physics. These techniques have an emerging presence in many-body theory as they have been used for interpolations, extrapolations, and in modeling wavefunctions. We will resolve several issues associated with machine learning and many-body calculations such as small datasets, outliers, and discontinuities, for the purpose of extrapolating finite calculations to macroscopic scales. We carry out a systematic investigation of two related systems by developing metrics that aim to avoid spurious effects and capture desired features. This work uses neural networks to extrapolate the Unitary Gas to the thermodynamic limit at zero-range, which is otherwise difficult to reach. The effective mass of strongly interacting neutron matter is also studied and makes use of the non-interacting problem to resolve discontinuous predictions. For this investigation, we also carried out new Auxiliary Field Diffusion Monte Carlo (AFDMC) calculations for a variety of densities and particle numbers. Ultimately, we demonstrate an effective utility for neural networks in this context.

We examine, from both analytical and numerical viewpoints, the uncertain volatility model by Hobson-Rogers in the framework of degenerate parabolic PDEs of Kolmogorov type.

Machine learning (ML) of kinetic energy functionals (KEFs), in particular kinetic energy density (KED) functionals, is a promising way to construct KEFs for orbital-free density functional theory (DFT). Neural networks and kernel methods including Gaussian process regression (GPR) have been used to learn Kohn-Sham (KS) KED from density-based descriptors derived from KS DFT calculations. The descriptors are typically expressed as functions of different powers and derivatives of the electron density. This can generate large and extremely unevenly distributed datasets, which complicates effective application of ML techniques. Very uneven data distributions require many training datapoints, can cause overfitting, and can ultimately lower the quality of an ML KED model. We show that one can produce more accurate ML models from fewer data by working with smoothed density-dependent variables and KED. Smoothing palliates the issue of very uneven data distributions and associated difficulties of sampling while retaining enough spatial structure necessary for working within the paradigm of KEDF. We use GPR as a function of smoothed terms of the fourth order gradient expansion and KS effective potential and obtain accurate and stable (with respect to different random choices of training points) kinetic energy models for Al, Mg, and Si simultaneously from as few as 2000 samples (about 0.3% of the total KS DFT data). In particular, accuracies on the order of 1% in a measure of the quality of energy-volume dependence B'=EV0-ΔV-2EV0+E(V0+ΔV)ΔV/V02 (where V0 is the equilibrium volume and ΔV is a deviation from it) are obtained simultaneously for all three materials.

BackgroundProlonged Disorders of Consciousness (PDOC) resulting from severe acquired brain injury can lead to complex disabilities that make diagnosis challenging. The role of machine learning (ML) in diagnosing PDOC states and identifying intervention strategies is relatively under-explored, having focused on predicting mortality and poor outcome. This study aims to: (a) apply ML techniques to predict PDOC diagnostic states from variables obtained from two non-invasive neurobehavior assessment tools; and (b) apply network analysis for guiding possible intervention strategies.MethodsThe Coma Recovery Scale-Revised (CRS-R) is a well-established tool for assessing patients with PDOC. More recently, music has been found to be a useful medium for assessment of coma patients, leading to the standardization of a music-based assessment of awareness: Music Therapy Assessment Tool for Awareness in Disorders of Consciousness (MATADOC). CRS-R and MATADOC data were collected from 74 PDOC patients aged 16–70 years at three specialist centers in the USA, UK and Ireland. The data were analyzed by three ML techniques (neural networks, decision trees and cluster analysis) as well as modelled through system-level network analysis.ResultsPDOC diagnostic state can be predicted to a relatively high level of accuracy that sets a benchmark for future ML analysis using neurobehavioral data only. The outcomes of this study may also have implications for understanding the role of music therapy in interdisciplinary rehabilitation to help patients move from one coma state to another.ConclusionsThis study has shown how ML can derive rules for diagnosis of PDOC with data from two neurobehavioral tools without the need to harvest large clinical and imaging datasets. Network analysis using the measures obtained from these two non-invasive tools provides novel, system-level ways of interpreting possible transitions between PDOC states, leading to possible use in novel, next-generation decision-support systems for PDOC.

Precision agriculture represents the new age of conventional agriculture. This is made possible by the advancement of various modern technologies such as the internet of things. The unparalleled potential for data collection and analytics has resulted in an increase in multi-disciplinary research within machine learning and agriculture. However, the application of machine learning techniques to agriculture seems to be out of step with core machine learning research. This gap is further exacerbated by the inherent challenges associated with agricultural data. In this work, we conduct a systematic review of a large body of academic literature published between 2000 and 2022, on the application of machine learning techniques to agriculture. We identify and discuss some of the key data issues such as class imbalance, data sparsity and high dimensionality. Further, we study the impact of these data issues on various machine learning approaches within the context of agriculture. Finally, we identify some of the common pitfalls in the machine learning and agriculture research including the misapplication of machine learning evaluation techniques. To this end, this survey presents a holistic view on the state of affairs in the cross-domain of machine learning and agriculture and proposes some suitable mitigation strategies to address these challenges.

This study uses machine learning (ML) techniques to predict maximum flank wear on cutting tools during the turning of Ti6Al4V, a titanium alloy known for its challenging machinability. We used three models (a) support vector machines (SVM), (b) random forests (RF), and (c) neural networks (NN) to predict maximum flank wear. The machining parameters are taken as input data sets, which compromise cutting speed, feed, and machining time. All the experiments are done at a constant depth of cut to predict maximum flank wear as an output variable. The forecasted maximum flank wear was validated using experimental data to verify their precision. The SVM model outperformed the other ML models in a wider range of cutting parameters concerning tool flank wear. The SVR model is the most accurate model with an average forecasting error of 3.24%. In forecasting maximum flank wear, the RF and NN models followed, with average forecasting errors of 3.88% and 3.71%, respectively. Additionally, the SVM model's robustness was further validated by its ability to maintain stability at varying cutting speeds and feed rates. The research enhances the economics of the machining process by predicting tool flank wear and fostering sustainability in the manufacturing industry, thereby demonstrating the applicability of ML techniques.

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.

The uncertain volatility model has long ago attracted the attention of practitioners as it provides worst-case pricing scenario for the sell-side. The valuation of a financial derivative based on this model requires solving a fully non-linear PDE. One can rely on finite difference schemes only when the number of variables (that is, underlyings and path-dependent variables) is small - in practice no more than three. In all other cases, numerical valuation seems out of reach. In this paper, we outline two accurate, easy-to-implement Monte-Carlo-like methods which hardly depend on dimensionality. The first method requires a parameterization of the optimal covariance matrix and consists in a series of backward low-dimensional optimizations. The second method relies heavily on a recently established connection between second-order backward stochastic differential equations and non-linear second-order parabolic PDEs. Both methods are illustrated by numerical experiments.