Results 121 to 130 of about 21,657 (246)

Monetary Policy When Preferences Are Quasi‐Hyperbolic

Journal of Money, Credit and Banking, EarlyView.
Abstract We study discretionary monetary policy in an economy where economic agents have quasi‐hyperbolic discounting. We demonstrate that a benevolent central bank is able to keep inflation under control for a wide range of discount factors. If the central bank, however, does not adopt the household's time preferences and tries to discourage early ...
RICHARD DENNIS, OLEG KIRSANOV
wiley   +1 more source

Application of Chebyshev collocation method for solving two classes of non-classical parabolic PDEs

Ain Shams Engineering Journal, 2015
This article contributes a numerical scheme for finding approximate solutions of one-dimensional parabolic partial differential equations (PDEs) under non-classical boundary conditions. This scheme is based on the direct Chebyshev collocation method that
Emran Tohidi
doaj   +1 more source

Sequential Outlier Detection in Nonstationary Time Series

Journal of Time Series Analysis, EarlyView.
ABSTRACT A novel method for sequential outlier detection in nonstationary time series is proposed. The method tests the null hypothesis of “no outlier” at each time point, addressing the multiple testing problem by bounding the error probability of successive tests, using extreme‐value theory. The asymptotic properties of the test statistic are studied
Florian Heinrichs, Patrick Bastian, Holger Dette   +2 more
wiley   +1 more source

A numerical method for the expected penalty–reward function in a Markov-modulated jump–diffusion process. [PDF]

A generalization of the Cramér–Lundberg risk model perturbed by a diffusion is proposed. Aggregate claims of an insurer follow a compound Poisson process and premiums are collected at a constant rate with additional random fluctuation.
Usábel, Miguel A., Diko, Peter
core  

Counting on Chebyshev Polynomials [PDF]

, 2009
Chebyshev polynomials have several elegant combinatorial interpretations. Specificially, the Chebyshev polynomials of the first kind are defined by T0(x) = 1, T1(x) = x, and Tn(x) = 2x Tn-1(x) - Tn-2(x). Chebyshev polynomials of the second kind Un(x) are
Benjamin, Arthur T., Walton, Daniel, \u2707   +1 more
core   +1 more source

Polinomios de Chebyshev [PDF]

, 2021
Este laboratorio virtual permite estudiar de forma gráfica los polinomios de Chebyshev, sus propiedades y proporcionar una interpretación geométrica de sus nodoshttps://laboratoriosvirtuales.upv.es/webapps/chebyshev.htmlGimenez Palomares, F.
Gimenez Palomares, Fernando
core  

Solving Stochastic Climate‐Economy Models: A Deep Least‐Squares Monte Carlo Approach

Mathematical Finance, EarlyView.
ABSTRACT Stochastic versions of recursive integrated climate‐economy assessment models are essential for studying and quantifying policy decisions under uncertainty. However, as the number of state variables and stochastic shocks increases, solving these models via deterministic grid‐based dynamic programming (e.g., value‐function iteration/projection ...
Aleksandar Arandjelović, Pavel V. Shevchenko, Tomoko Matsui, Daisuke Murakami, Tor A. Myrvoll   +4 more
wiley   +1 more source

On the Exact Limiting Distribution of a Volatility Target Index

Mathematical Finance, EarlyView.
ABSTRACT Assuming a lognormal distribution for the underlying risky asset, we study the limiting distribution of a volatility target index as the rebalancing time step approaches zero. Two limit theorems (a strong law of large numbers and a central limit theorem) are established, and as an application, the exact limiting distribution is derived.
Xuan Liu, Michel Gauthier
wiley   +1 more source

Generalized Chebyshev acceleration

Numerical Algorithms
We use generalized Chebyshev polynomials, associated with the root system $A_2$, to provide a new semi-iterative method for accelerating simple iterative methods for solving linear systems. We apply this semi-iterative method to the Jacobi method, and give an example. There are certain restrictions but the resulting acceleration is rather high.
openaire   +2 more sources

Multivariate Chebyshev Inequalities

The Annals of Mathematical Statistics, 1960
If $X$ is a random variable with $EX^2 = \sigma^2$, then by Chebyshev's inequality, \begin{equation*}\tag{1.1}P\{|X| \geqq \epsilon\} \leqq \sigma^2/\epsilon^2.\end{equation*} If in addition $EX = 0$, one obtains a corresponding one-sided inequality \begin{equation*}\tag{1.2}\quad P\{X \geqq \epsilon\} \leqq \sigma^2/ (\epsilon^2 + \sigma^2)\end ...
Marshall, Albert W., Olkin, Ingram
openaire   +3 more sources