scholarly journals A Simple Out-of-Sample Test of Predictability against the Random Walk Benchmark

Mathematics ◽  
2022 ◽  
Vol 10 (2) ◽  
pp. 228
Author(s):  
Pablo Pincheira ◽  
Nicolas Hardy ◽  
Andrea Bentancor
Keyword(s):  
Monte Carlo ◽  
Random Walk ◽  
Monte Carlo Simulations ◽  
Strong Assumption ◽  
Empirical Application ◽  
Random Walk Hypothesis ◽  
Out Of Sample ◽  
Sample Test ◽  
Commodity Currencies

We show that a straightforward modification of a trading-based test for predictability displays interesting advantages over the Excess Profitability (EP) test proposed by Anatolyev and Gerco when testing the Driftless Random Walk Hypothesis. Our statistic is called the Straightforward Excess Profitability (SEP) test, and it avoids the calculation of a term that under the null of no predictability should be zero but in practice may be sizable. In addition, our test does not require the strong assumption of independence used to derive the EP test. We claim that dependence is the rule and not the exception. We show via Monte Carlo simulations that the SEP test outperforms the EP test in terms of size and power. Finally, we illustrate the use of our test in an empirical application within the context of the commodity-currencies literature.

scholarly journals Testing for the presence of measurement error in Stata

2020 ◽  
Vol 20 (2) ◽  
pp. 382-404
Author(s):  
Young Jun Lee ◽  
Daniel Wilhelm
Keyword(s):  
Monte Carlo ◽  
Measurement Error ◽  
Monte Carlo Simulations ◽  
Nonparametric Test ◽  
Parametric Models ◽  
Empirical Application ◽  
Explanatory Variables ◽  
Working Paper ◽  
Linear Regressions

In this article, we describe how to test for the presence of measurement error in explanatory variables. First, we discuss the test of such hypotheses in parametric models such as linear regressions and then introduce a new command, dgmtest, for a nonparametric test proposed in Wilhelm (2018, Working Paper CWP45/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies). To illustrate the new command, we provide Monte Carlo simulations and an empirical application to testing for measurement error in administrative earnings data.

SELF-ATTRACTING WALK ON NON-UNIFORM SUBSTRATES

2002 ◽  
Vol 16 (12) ◽  
pp. 449-457
Author(s):  
ZHI-JIE TAN ◽  
XIAN-WU ZOU ◽  
WEI ZHANG ◽  
SHENG-YOU HUANG ◽  
ZHUN-ZHI JIN
Keyword(s):  
Monte Carlo ◽  
Random Walk ◽  
Monte Carlo Simulations ◽  
Fractal Dimensions ◽  
Attractive Interaction ◽  
Percolation Clusters ◽  
Small Scales ◽  
End Distance ◽  
The Mean ◽  
End To End

Self-attracting walk (SATW) on non-uniform substrates has been investigated by Monte Carlo simulations. The non-uniform substrates are described by Leath percolation clusters with occupied probability p. p stands for the degree of non-uniformity, and takes on values in the range pc≲p ≤1 where pc is the threshold of percolation. For the case of strong attractive interaction u, p has little influence on the walk which is dominated by attractive interactions. Furthermore, in the case of small scales, the exponent ν of the mean end-to-end distance <R2(t)> versus time t is given by ν≃1/(ds+1), while the exponent k of visited sites versus t is given by k≃ds/(ds+1), where ds are the fractal dimensions of the substrates. For u ≃ 0, the walk reduces to the random walk on percolations with p in pc≲p≤1. Also, ν and k decrease sensitively with the reduction of p. It is found, the blocked sites in the substrates (i.e. defects) have much greater influence on the walk driven by thermal flunctuation than that dominated by the attractive interaction.

Study of the Bioheat Equation Using Monte Carlo Simulations for Local Magnetic Hyperthermia

2008 ◽  
Author(s):  
Gustavo Gutierrez ◽  
Mauricio Giordano
Keyword(s):  
Monte Carlo ◽  
Random Walk ◽  
Heat Equation ◽  
Monte Carlo Simulations ◽  
Cancer Cells ◽  
High Temperatures ◽  
Heat Diffusion ◽  
Maximum Temperature ◽  
Heat Diffusion Equation ◽  
Spherical Domain

Hyperthermia is a type of cancer treatment in which cancer cells are exposed to high temperatures (up to 44–45°C). Research has shown that high temperatures can damage and kill cancer cells, by a localized and concentrated heating source. By killing cancer cells and damaging proteins and structures within cells, hyperthermia may shrink tumors, with minimal injury to normal tissues. Penne’s bio-heat equation is used to model a heat diffusion process inside a tumor, modeled as a spherical domain with magnetic nanoparticles distributed within the diseased tissue. These magnetic particles are considered as point heat sources. Heat is generated as the result of magnetic relaxation mechanisms (Brownian and Neel relaxation) by the application of alternating magnetic fields. The Bio-Heat equation is solved using Monte Carlo techniques. Monte Carlo simulations are based on departing random walkers from the point where temperature is going to be determined. The probability in each step of the random walk is given by the coefficients of the nodal temperatures after a Finite Difference Discretization of the Penne’s bio-heat diffusion equation. The main advantage of Monte Carlo simulations versus classical numerical methods lies in the possibility of solving the temperature in a specific point without solving for all the points within the domain. This feature and the fact that each random walk is independent from each other results in an easy parallelization of the computer code. Parametric studies of the temperature profiles are carried out to study the effect of different parameters like the heat generation rate, perfusion rate and diameter of the point source on the maximum temperature and on the temperature profile.

scholarly journals Nonstationary INAR(1) Process with th-Order Autocorrelation Innovation

2013 ◽  
Vol 2013 ◽  
pp. 1-10
Author(s):  
Kaizhi Yu ◽  
Hong Zou ◽  
Daimin Shi
Keyword(s):  
Monte Carlo ◽  
Random Walk ◽  
Monte Carlo Simulations ◽  
Least Squares ◽  
Limit Distribution ◽  
Nonstationary Process ◽  
Random Walk Process ◽  
Autoregressive Coefficient ◽  
Least Squares Estimators ◽  
Conditional Least Squares

This paper is concerned with an integer-valued random walk process withqth-order autocorrelation. Some limit distributions of sums about the nonstationary process are obtained. The limit distribution of conditional least squares estimators of the autoregressive coefficient in an auxiliary regression process is derived. The performance of the autoregressive coefficient estimators is assessed through the Monte Carlo simulations.

scholarly journals Using the Extremal Index for Value-at-Risk Backtesting*

2020 ◽  
Vol 18 (3) ◽  
pp. 556-584
Author(s):  
Axel Bücher ◽  
Peter N Posch ◽  
Philipp Schmidtke
Keyword(s):  
At Risk ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Value At Risk ◽  
Extremal Index ◽  
General Measure ◽  
Independence Property ◽  
Forecasting Models ◽  
Out Of Sample ◽  
Extremal Clustering

Abstract We introduce a set of new Value-at-Risk independence backtests by establishing a connection between the independence property of Value-at-Risk forecasts and the extremal index, a general measure of extremal clustering of stationary sequences. For this purpose, we introduce a sequence of relative excess returns whose extremal index is to be estimated. We compare our backtest to both popular and recent competitors using Monte Carlo simulations and find considerable power in many scenarios. In an applied section, we perform realistic out-of-sample forecasts with common forecasting models and discuss advantages and pitfalls of our approach.

Low-voltage EDS of magnesium ferrite Dendrites in a FEG-SEM

1996 ◽  
Vol 54 ◽  
pp. 478-479
Author(s):  
Matthew T. Johnson ◽  
Ian M. Anderson ◽  
Jim Bentley ◽  
C. Barry Carter
Keyword(s):  
Monte Carlo ◽  
Quantitative Analysis ◽  
Monte Carlo Simulations ◽  
Cross Section ◽  
Spatial Resolution ◽  
Low Voltage ◽  
Magnesium Ferrite ◽  
X Ray ◽  
Interaction Volume ◽  
Ferrite Spinel

Energy-dispersive X-ray spectrometry (EDS) performed at low (≤ 5 kV) accelerating voltages in the SEM has the potential for providing quantitative microanalytical information with a spatial resolution of ∼100 nm. In the present work, EDS analyses were performed on magnesium ferrite spinel [(MgxFe1−x)Fe2O4] dendrites embedded in a MgO matrix, as shown in Fig. 1. spatial resolution of X-ray microanalysis at conventional accelerating voltages is insufficient for the quantitative analysis of these dendrites, which have widths of the order of a few hundred nanometers, without deconvolution of contributions from the MgO matrix. However, Monte Carlo simulations indicate that the interaction volume for MgFe2O4 is ∼150 nm at 3 kV accelerating voltage and therefore sufficient to analyze the dendrites without matrix contributions.Single-crystal {001}-oriented MgO was reacted with hematite (Fe2O3) powder for 6 h at 1450°C in air and furnace cooled. The specimen was then cleaved to expose a clean cross-section suitable for microanalysis.

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