Multivariate Density Estimation

Scott, David W.

doi:10.1002/9780470316849

Cited by 3,020 publications

(572 citation statements)

References 0 publications

Supporting

Mentioning

558

Contrasting

Unclassified

Order By: Relevance

“…Each iteration uses only the index returns observed on days when the paired LETF was trading to ensure that the two samples are derived from the same sequence of data. The null hypothesis in a K-S test states that the two samples being compared are drawn from the same distribution [21]. Our goal is to simulate tracking errors in a way that the K-S test of the LETF returns samples cannot reject the null hypothesis at sufficiently high confidence levels.…”

Section: Lag and Bandwidth Selection For Simulation Of Letf Tracking mentioning

confidence: 99%

“…Column headers prefixed with "21D" or "252D" refer to results produced when using 21 greater than 0.9. There were many lag selections for each LETF that performed well when using 21 day periods, so we simplified the estimation task and present only the lag selection that provided the best results with 252 day periods (the values shown in columns with a "21D" prefix may not actually be the best).…”

Section: Lag and Bandwidth Selection For Simulation Of Letf Tracking mentioning

confidence: 99%

See 1 more Smart Citation

Simulation of Leveraged ETF Volatility Using Nonparametric Density Estimation

Ginley¹,

Scott²,

Ensor³

2015

JMF

Self Cite

View full text Add to dashboard Cite

Leveraged Exchange Traded Funds (LETFs) are constructed to provide the indicated leverage multiple of the daily total return on an underlying index. LETFs may perform as expected on a daily basis; however, fund issuers state that there is no guarantee of achieving the multiple of the index return over longer time horizons. LETF returns are extremely volatile and funds frequently underperform their target for horizons greater than one month. In this paper, we contribute two nonparametric simulation methods for analyzing LETF return volatility and how this is related to the underlying index. First, to overcome the limited history of LETF returns data, we propose a method for simulating implied LETF tracking errors while still accounting for their dependence on underlying index returns. This allows for the incorporation of the complete history of index returns in an LETF returns model. Second, to isolate the effects of daily, leveraged compounding on LETF volatility, we propose an innovative method for simulating daily index returns with a chosen constraint on the multi-day period return. By controlling for the performance of the underlying index, the range of volatilities observed in a simulated sample can be attributed to compounding with leverage and the presence of tracking errors. Our nonparametric methods are flexible-easily incorporating any chosen number of days, leverage ratios, or period return constraints, and can be used in combination or separately to model any quantity of interest derived from daily LETF returns.

show abstract

Section: Lag and Bandwidth Selection For Simulation Of Letf Tracking mentioning

confidence: 99%

Section: Lag and Bandwidth Selection For Simulation Of Letf Tracking mentioning

confidence: 99%

Simulation of Leveraged ETF Volatility Using Nonparametric Density Estimation

Ginley¹,

Scott²,

Ensor³

2015

JMF

Self Cite

View full text Add to dashboard Cite

show abstract

“…In NPE, kernel estimation provides a convenient way to estimate the probability density [65] and [81]. In kernel estimation, both kernel function [68] and bandwidth [66] are of great concern.…”

Section: Case Analysis Of Npementioning

confidence: 99%

A Review of the Extrapolation Method in Load Spectrum Compiling

Wang¹,

Hong-bin²,

Li³

et al. 2016

SV-JME

View full text Add to dashboard Cite

60 INTRODUCTIONIn engineering, many mechanical structures and components are subjected to complex and random loads, which determine the fatigue reliability and life of the machinery [1] and [2]. Thus, it is indispensable to conduct fatigue analysis and life prediction of the structures and components based on a load spectrum [3] and [4]. Currently, a load spectrum is widely used in the fields of aerospace [5] . In practice, a long-term load spectrum contains the complete load information, but it is difficult to be directly measured due to the restrictions of testing technology, as well as time and cost. Therefore, it is necessary to obtain a long-term load spectrum based on a short-term one.The traditional load spectrum compiling method multiplies a short-term load spectrum with a constant proportionality coefficient [15] to [17]. Since only the data measured in a finite time is repeated, the extreme loads that cannot be measured and have a greater impact on damage are ignored. Load extrapolation methods can overcome the above limitation of the traditional method. With the development of statistics and computer software, new methods have been applied to load extrapolation. In load spectrum compiling, results may vary from each other with different extrapolation methods. Therefore, selecting an appropriate load extrapolation method is very important, but that is difficult in practice. For a better understanding of the methods and to provide selection guidance, several commonly used extrapolation methods are reviewed and summarized based on the literature and illustrations in this paper.The extrapolation methods are classified as the parametric extrapolation method (PE), nonparametric extrapolation method (NPE) and quantile extrapolation method (QE). In PE, sample data is supposed to obey a known distribution, and the parameters in the function are estimated according to the load sample. In NPE, an extrapolated result is obtained because the density distribution with an arbitrary shape can be received based on a nonparametric density estimation. When the sample data has different load characteristics due to different working conditions and different operating behaviors in the testing process, QE can break the data into a series of clusters and computes the damage of each rainflow matrix. The literature and illustrations are presented to evaluate the extrapolation methods and the characteristics of various extrapolation methods, such as the critical factors, the advantages and disadvantages, and the application ranges, are summarized. Some potential research prospects are

show abstract

“…The computa--tion of a "real" mode value can be performed only for a discrete set of different values. For continu--ous variables, the maximum of a density function of the sample, such as the Gaussian kernel--density estimator (Scott 1992), are often used instead. Depending on the used estimator and its parameters, different maximum values are chosen.…”

Section: The Static Casementioning

confidence: 99%

A dynamic probabilistic material flow modeling method

Bornhöft

Sun

Hilty

et al. 2016

Environmental Modelling & Software

View full text Add to dashboard Cite

Material flow modeling constitutes an important approach to predicting and understanding the flows of materials through the anthroposphere into the environment. The new "Dynamic Probabilistic Material Flow Analysis (DPMFA)" method, combining dynamic material flow modeling with probabilistic modeling, is presented in this paper. Material transfers that lead to particular environmental stocks are represented as systems of mass-balanced flows. The time-dynamic behavior of the system is calculated by adding up the flows over several consecutive periods, considering changes in the inflow to the system and intermediate delays in local stocks. Incomplete parameter knowledge is represented and propagated using Bayesian modeling. The method is implemented as a simulation framework in Python to support experts from different domains in the development of their application models. After the introduction of the method and its implementation, a case study is presented in which the framework is applied to predict the environmental concentrations of carbon nanotubes in Switzerland.

show abstract

Multivariate Density Estimation

Cited by 3,020 publications

References 0 publications

Simulation of Leveraged ETF Volatility Using Nonparametric Density Estimation

Simulation of Leveraged ETF Volatility Using Nonparametric Density Estimation

A Review of the Extrapolation Method in Load Spectrum Compiling

A dynamic probabilistic material flow modeling method

Contact Info

Product

Resources

About