Application of Kernel Smoothing Methods with Cross-Validation Criterion Bandwidth Choice to Two-Stage Portfolio Selection

2020 ◽  
Vol 37 (4) ◽  
pp. 33-54
Author(s):  
Gyu-Sik Han
Keyword(s):  
Portfolio Selection ◽  
Cross Validation ◽  
Kernel Smoothing ◽  
Two Stage ◽  
Smoothing Methods ◽  
Validation Criterion

scholarly journals Yield Curve Estimation by Kernel Smoothing Methods

2000 ◽  
Author(s):  
Oliver B. Linton ◽  
Enno Mammen ◽  
Jens Perch Nielsen ◽  
Carsten Tanggaard
Keyword(s):  
Kernel Smoothing ◽  
Yield Curve ◽  
Curve Estimation ◽  
Smoothing Methods

scholarly journals New Bandwidth Selection for Kernel Quantile Estimators

2012 ◽  
Vol 2012 ◽  
pp. 1-18
Author(s):  
Ali Al-Kenani ◽  
Keming Yu
Keyword(s):  
Cross Validation ◽  
Kernel Smoothing ◽  
Bandwidth Selection ◽  
Unbiased Estimation ◽  
Optimal Bandwidth ◽  
Squared Error ◽  
Bandwidth Parameter ◽  
Integrated Squared Error ◽  
Selection For ◽  
Quantile Estimators

We propose a cross-validation method suitable for smoothing of kernel quantile estimators. In particular, our proposed method selects the bandwidth parameter, which is known to play a crucial role in kernel smoothing, based on unbiased estimation of a mean integrated squared error curve of which the minimising value determines an optimal bandwidth. This method is shown to lead to asymptotically optimal bandwidth choice and we also provide some general theory on the performance of optimal, data-based methods of bandwidth choice. The numerical performances of the proposed methods are compared in simulations, and the new bandwidth selection is demonstrated to work very well.

scholarly journals Two-Stage Fuzzy Portfolio Selection Problem with Transaction Costs

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Yanju Chen ◽  
Ye Wang
Keyword(s):  
Transaction Costs ◽  
Portfolio Selection ◽  
Optimal Solution ◽  
Single Stage ◽  
Selection Problem ◽  
Stage Model ◽  
Two Stage ◽  
Portfolio Selection Problem ◽  
Fuzzy Portfolio Selection ◽  
Proposed Model

This paper studies a two-period portfolio selection problem. The problem is formulated as a two-stage fuzzy portfolio selection model with transaction costs, in which the future returns of risky security are characterized by possibility distributions. The objective of the proposed model is to achieve the maximum utility in terms of the expected value and variance of the final wealth. Given the first-stage decision vector and a realization of fuzzy return, the optimal value expression of the second-stage programming problem is derived. As a result, the proposed two-stage model is equivalent to a single-stage model, and the analytical optimal solution of the two-stage model is obtained, which helps us to discuss the properties of the optimal solution. Finally, some numerical experiments are performed to demonstrate the new modeling idea and the effectiveness. The computational results provided by the proposed model show that the more risk-averse investor will invest more wealth in the risk-free security. They also show that the optimal invested amount in risky security increases as the risk-free return decreases and the optimal utility increases as the risk-free return increases, whereas the optimal utility increases as the transaction costs decrease. In most instances the utilities provided by the proposed two-stage model are larger than those provided by the single-stage model.

Best subset selection via cross-validation criterion

Top ◽  
2020 ◽  
Vol 28 (2) ◽  
pp. 475-488 ◽  
Author(s):  
Yuichi Takano ◽  
Ryuhei Miyashiro
Keyword(s):  
Cross Validation ◽  
Subset Selection ◽  
Validation Criterion

scholarly journals Estimating Spatio-Temporal Variations of PM2.5 Concentrations Using VIIRS-Derived AOD in the Guanzhong Basin, China

2019 ◽  
Vol 11 (22) ◽  
pp. 2679 ◽  
Author(s):  
Kainan Zhang ◽  
Gerrit de Leeuw ◽  
Zhiqiang Yang ◽  
Xingfeng Chen ◽  
Xiaoli Su ◽  
...  
Keyword(s):  
Statistical Model ◽  
Cross Validation ◽  
Temporal Variations ◽  
Coefficient Of Determination ◽  
Prediction Errors ◽  
Two Stage ◽  
Temporal Models ◽  
Spatio Temporal ◽  
Guanzhong Basin ◽  
Gwr Model

Aerosol optical depth (AOD) derived from satellite remote sensing is widely used to estimate surface PM2.5 (dry mass concentration of particles with an in situ aerodynamic diameter smaller than 2.5 µm) concentrations. In this research, a two-stage spatio-temporal statistical model for estimating daily surface PM2.5 concentrations in the Guanzhong Basin of China is proposed, using 6 km × 6 km AOD data available from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument as the main variable and meteorological factors, land-cover, and population data as auxiliary variables. The model is validated using a cross-validation method. The linear mixed effects (LME) model used in the first stage could be improved by using a geographically weighted regression (GWR) model or the generalized additive model (GAM) in the second stage, and the predictive capability of the GWR model is better than that of GAM. The two-stage spatio-temporal statistical model of LME and GWR successfully captures the temporal and spatial variations. The coefficient of determination (R2), the bias and the root-mean-squared prediction errors (RMSEs) of the model fitting to the two-stage spatio-temporal models of LME and GWR were 0.802, −0.378 µg/m3, and 12.746 µg/m3, respectively, and the model cross-validation results were 0.703, 1.451 µg/m3, and 15.731 µg/m3, respectively. The model prediction maps show that the topography has a strong influence on the spatial distribution of the PM2.5 concentrations in the Guanzhong Basin, and PM2.5 concentrations vary with the seasons. This method can provide reliable PM2.5 predictions to reduce the bias of exposure assessment in air pollution and health research.

scholarly journals A Comparative Analysis of TRMM–Rain Gauge Data Merging Techniques at the Daily Time Scale for Distributed Rainfall–Runoff Modeling Applications

2015 ◽  
Vol 16 (5) ◽  
pp. 2153-2168 ◽  
Author(s):  
Daniele Nerini ◽  
Zed Zulkafli ◽  
Li-Pen Wang ◽  
Christian Onof ◽  
Wouter Buytaert ◽  
...  
Keyword(s):  
Cross Validation ◽  
Hydrological Modeling ◽  
Kernel Smoothing ◽  
Rain Gauge ◽  
Rain Gauge Data ◽  
Geostatistical Methods ◽  
Data Merging ◽  
Gauge Data ◽  
The Mean ◽  
Daily Time

Abstract This study compares two nonparametric rainfall data merging methods—the mean bias correction and double-kernel smoothing—with two geostatistical methods—kriging with external drift and Bayesian combination—for optimizing the hydrometeorological performance of a satellite-based precipitation product over a mesoscale tropical Andean watershed in Peru. The analysis is conducted using 11 years of daily time series from the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) research product (also TRMM 3B42) and 173 rain gauges from the national weather station network. The results are assessed using 1) a cross-validation procedure and 2) a catchment water balance analysis and hydrological modeling. It is found that the double-kernel smoothing method delivered the most consistent improvement over the original satellite product in both the cross-validation and hydrological evaluation. The mean bias correction also improved hydrological performance scores, particularly at the subbasin scale where the rain gauge density is higher. Given the spatial heterogeneity of the climate, the size of the modeled catchment, and the sparsity of data, it is concluded that nonparametric merging methods can perform as well as or better than more complex geostatistical methods, whose assumptions may not hold under the studied conditions. Based on these results, a systematic approach to the selection of a satellite–rain gauge data merging technique is proposed that is based on data characteristics. Finally, the underperformance of an ordinary kriging interpolation of the rain gauge data, compared to TMPA and other merged products, supports the use of satellite-based products over gridded rain gauge products that utilize sparse data for hydrological modeling at large scales.

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