The Experts below are selected from a list of 145512 Experts worldwide ranked by ideXlab platform
Jian Liang - One of the best experts on this subject based on the ideXlab platform.
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deep learning based least Square forward backward stochastic differential equation solver for high dimensional derivative pricing
Social Science Research Network, 2020Co-Authors: Jian LiangAbstract:We propose a new forward-backward stochastic differential equation solver for highdimensional derivative pricing problems by combining deep learning solver with least Square Regression technique widely used in the least Square Monte Carlo method for the valuation of American options. Our numerical experiments demonstrate the accuracy of our least Square backward deep neural network solver and its capability to produce accurate prices for complex early exercise derivatives, such as callable yield notes. Our method can serve as a generic numerical solver for pricing derivatives across various asset groups, in particular, as an accurate means for pricing high-dimensional derivatives with early exercise features.
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deep learning based least Square forward backward stochastic differential equation solver for high dimensional derivative pricing
arXiv: Computational Finance, 2019Co-Authors: Jian Liang, Zhe Xu, Peter LiAbstract:We propose a new forward-backward stochastic differential equation solver for high-dimensional derivatives pricing problems by combining deep learning solver with least Square Regression technique widely used in the least Square Monte Carlo method for the valuation of American options. Our numerical experiments demonstrate the efficiency and accuracy of our least Square backward deep neural network solver and its capability to provide accurate prices for complex early exercise derivatives such as callable yield notes. Our method can serve as a generic numerical solver for pricing derivatives across various asset groups, in particular, as an efficient means for pricing high-dimensional derivatives with early exercises features.
Hongwei Sun - One of the best experts on this subject based on the ideXlab platform.
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least Square Regression with indefinite kernels and coefficient regularization
Applied and Computational Harmonic Analysis, 2011Co-Authors: Hongwei SunAbstract:Abstract In this paper, we provide a mathematical foundation for the least Square Regression learning with indefinite kernel and coefficient regularization. Except for continuity and boundedness, the kernel function is not necessary to satisfy any further regularity conditions. An explicit expression of the solution via sampling operator and empirical integral operator is derived and plays an important role in our analysis. It provides a natural error decomposition where the approximation error is characterized by a reproducing kernel Hilbert space associated to certain Mercer kernel. A careful analysis shows the sample error has O ( 1 m ) decay. We deduce the error bound and prove the asymptotic convergence. Satisfactory learning rates are then derived under a very mild regularity condition on the Regression function. When the kernel is itself a Mercer kernel better rates are given by a rigorous analysis which shows coefficient regularization is powerful in learning smooth functions. The saturation effect and the relation to the spectral algorithms are discussed.
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a note on application of integral operator in learning theory
Applied and Computational Harmonic Analysis, 2009Co-Authors: Hongwei SunAbstract:Abstract By the aid of the properties of the Square root of positive operators we refine the consistency analysis of regularized least Square Regression in a reproducing kernel Hilbert space. Sharper error bounds and faster learning rates are obtained when the sampling sequence satisfies a strongly mixing condition.
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Application of integral operator for regularized least-Square Regression
Mathematical and Computer Modelling, 2008Co-Authors: Hongwei SunAbstract:In this paper, we study the consistency of the regularized least-Square Regression in a general reproducing kernel Hilbert space. We characterize the compactness of the inclusion map from a reproducing kernel Hilbert space to the space of continuous functions and show that the capacity-based analysis by uniform covering numbers may fail in a very general setting. We prove the consistency and compute the learning rate by means of integral operator techniques. To this end, we study the properties of the integral operator. The analysis reveals that the essence of this approach is the isomorphism of the Square root operator.
Luca Montanarella - One of the best experts on this subject based on the ideXlab platform.
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prediction of soil organic carbon content by diffuse reflectance spectroscopy using a local partial least Square Regression approach
Soil Biology & Biochemistry, 2014Co-Authors: Marco Nocita, Antoine Stevens, Gergely Toth, Panos Panagos, Bas Van Wesemael, Luca MontanarellaAbstract:Due to the large spatial variation of soil organic carbon (SOC) content, assessing the current state of SOC for large areas is costly and time consuming. Visible and Near Infrared Diffuse Reflectance Spectroscopy (Vis-NIR DRS) is a fast and cheap tool for measuring SOC based on empirical equations and spectral libraries. While the approach has been demonstrated to yield accurate predictions for databases containing samples belonging to soils with similar characteristics such as mineralogy, texture, iron, and CaCO3 content, spectroscopic calibrations have been less successful when applied to large and diverse soil spectral libraries. The scope of this study was to predict SOC using a local partial least Square Regression approach. In total, 19,969 topsoil (0-20cm) samples collected all over the European Union were analyzed for physical and chemical properties, and scanned with a Vis-NIR spectrometer in a single laboratory. The local Regression method builds a different multivariate model for each sample to predict. Each local model is trained with neighbours' samples selected from a large spectral library, based on their spectral similarity with the sample to predict. We modified the local Regression procedure by including other covariates (geographical and texture information) in the computation of the distance between samples. The results showed good prediction ability for mineral soils under cropland (RMSE=3.6gCkg-1) and grassland (RMSE=7.2gCkg-1). Predictions of mineral soils under woodland (RMSE=11.9gCkg-1) and organic soils (RMSE=51.1gCkg-1) were less accurate. The use of sand content in the computation of the sample similarities provided the most accurate SOC predictions due to its influence on light scattering properties of soils. In large datasets, using additional soil or environmental information allows to select neighbours that have overall the same soil composition as the samples to predict, resulting in more accurate models. This study shows that (i) it is possible to realize low-cost estimations of SOC at continental scale using large spectral libraries with a reasonable accuracy, and (ii) the local approach is a valuable tool to deal with large datasets, especially if existing soil property maps or soil legacy data could be used as covariates in the SOC prediction models.
Hung T Nguyen - One of the best experts on this subject based on the ideXlab platform.
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assessment of rice leaf growth and nitrogen status by hyperspectral canopy reflectance and partial least Square Regression
European Journal of Agronomy, 2006Co-Authors: Hung T NguyenAbstract:Abstract Diagnosis of rice growth and nutrient status is critical for prediction of rice yield and grain quality and prescription of nitrogen topdressing at panicle initiation stage. Two experiments, one in 2000 and one in 2003 were conducted to construct a partial least Square model for assessing rice leaf growth and nitrogen status non-destructively at the Experimental Farm (37°16′N, 126°59′E) of Seoul National University, Suwon, Korea. The experiment included two cultivars (Hwasungbyeo and Dasanbyeo) and four levels of nitrogen (N) application in year 2000 and four rice cultivars (Hwasungbyeo, SNU-SG1, Juanbyeo, and Surabyeo) and 10 N treatments in year 2003. Hyperspectral canopy reflectance (300–1100 nm) data recorded at various growth stages before heading were used for partial least Square Regression (PLS) model to predict four crop variables: leaf area index, leaf dry weight, leaf N concentration, and leaf N density. Three hundred and forty two observations from two experiments were randomly split for model calibration (75%) and validation (25%). Coefficient of determination ( R 2 ), root mean Square error in prediction (RMSEP) and relative error of prediction (REP) of model calibration and validation were calculated for the model quality evaluation. The results revealed that PLS model using hyperspectral canopy reflectance data to predict four plant variables produced an acceptable model precision and accuracy. The model R 2 and REP ranged from 0.84 to 0.87 and 10.0 to 23.8% for calibration and 0.79 to 0.84 and 11.1 to 25.6% for validation, respectively. The most important reflectance as judged by factor loading in PLS model for rice leaf characterization was at various bands such as near-infrared (>760 nm) and visible (355, 420, 524–534, 583 and 687 nm) and red edge (707 nm) region.
Thomas Jarmer - One of the best experts on this subject based on the ideXlab platform.
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Quantitative analysis of soil chemical properties with diffuse reflectance spectrometry and partial least-Square Regression: A feasibility study
Plant and Soil, 2003Co-Authors: Thomas Udelhoven, Christoph Emmerling, Thomas JarmerAbstract:Soil chemical properties from different locations in the Trier region, Rhineland-Palatinate, SW Germany were evaluated using VIS/NIR reflectance spectrometry (ASD FieldSpec-II spectrometer, 0.4-2.5 m) and partial least-Square Regression (PLS). Generally, laboratory spectrometry performed better than field spectrometry probably due to strong interferences of soil structure. In a plot experiment reliable estimations were obtained for total amounts of Ca, Mg, Fe, Mn and K but not for organic carbon and nitrogen. In the landscape-scale context the estimations for organic carbon could be significantly improved but it was also concluded that the development of statistical prediction models is limited to geologically homogeneous areas. In both experiments CAL extractable nutrients could not be satisfactorily estimated. This excludes diffuse VIS/NIR spectrometry as a diagnosis tool of short- or medium-term changes of the soil's nutrient status. However, the method can be used as a quick screening method in questions where the spatial distribution of organic carbon and total metal contents is addressed, as in soil development and soil degradation monitoring, and when time or laboratory costs are critical factors.
