Breaks down of the modeling of the financial market with addition of non-linear terms in the Itô stochastic process

2019 ◽  
Vol 526 ◽  
pp. 120932 ◽  
Author(s):  
Leonardo S. Lima ◽  
S.C. Oliveira ◽  
A.F. Abeilice ◽  
J.H.C. Melgaço
Keyword(s):  
Stochastic Process ◽  
Financial Market ◽  
Non Linear

Testing for non-linear structure in an artificial financial market

2001 ◽  
Vol 46 (3) ◽  
pp. 327-342 ◽  
Author(s):  
Shu-Heng Chen ◽  
Thomas Lux ◽  
Michele Marchesi
Keyword(s):  
Financial Market ◽  
Linear Structure ◽  
Non Linear

The Equivalent Gain Matrix of a Multivariable Non-Linearity

1969 ◽  
Vol 2 (1) ◽  
pp. T1-T5 ◽  
Author(s):  
John C. West
Keyword(s):  
Stochastic Process ◽  
Cross Correlation ◽  
Linear Matrix ◽  
Sinusoidal Wave ◽  
Gain Matrix ◽  
Cross Modulation ◽  
Input Signals ◽  
Non Linear ◽  
Wave Forms ◽  
Random Signals

The equivalent gain concept of a mono-variable non-linear stochastic process is used to evaluate the linear matrix equivalent of a multi-variable non-linearity having n independent inputs and m outputs. It is shown that if the n inputs are restricted to separable class of processes (which includes Gaussian random signals and also sinusoidal wave forms but is much wider), then the distortion terms produced by the non-linearity and by cross modulation have zero cross-correlation with any of the input signals.

scholarly journals Filtration of parameters of the Heston model

2020 ◽  
pp. 48-55
Author(s):  
О.А. Кобилін ◽  
О.Є. Путятіна ◽  
М.В. Гарячий
Keyword(s):  
Linear Function ◽  
Financial Market ◽  
Stock Price ◽  
Linear Functions ◽  
Heston Model ◽  
Model Parameters ◽  
Filtration Problem ◽  
Practical Applications ◽  
Non Linear

In this article we consider the Heston model of the stock price behaviour. While the volatility of the model is the non-linear function of another stochastic unobservable function, that is why we consider linearizing all non-linear functions of the model. The aim is to make the Heston model simpler for practical applications, in particular for solving the filtration problem. The filtration problem for the models of the financial market consists of evaluating of unobservable model parameters, having got the stock price observations.

scholarly journals Entropy-Based Behavioural Efficiency of the Financial Market

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1396
Author(s):  
Emil Dinga ◽  
Camelia Oprean-Stan ◽  
Cristina-Roxana Tănăsescu ◽  
Vasile Brătian ◽  
Gabriela-Mariana Ionescu
Keyword(s):  
Financial Markets ◽  
Financial Market ◽  
Informational Efficiency ◽  
Abstract Model ◽  
Informational Entropy ◽  
Logistic Curve ◽  
Economic Agents ◽  
Implicit Information ◽  
Non Linear

The most known and used abstract model of the financial market is based on the concept of the informational efficiency (EMH) of that market. The paper proposes an alternative which could be named the behavioural efficiency of the financial market, which is based on the behavioural entropy instead of the informational entropy. More specifically, the paper supports the idea that, in the financial market, the only measure (if any) of the entropy is the available behaviours indicated by the implicit information. Therefore, the behavioural entropy is linked to the concept of behavioural efficiency. The paper argues that, in fact, in the financial markets, there is not a (real) informational efficiency, but there exists a behavioural efficiency instead. The proposal is based both on a new typology of information in the financial market (which provides the concept of implicit information—that is, that information ”translated” by the economic agents from observing the actual behaviours) and on a non-linear (more exactly, a logistic) curve linking the behavioural entropy to the behavioural efficiency of the financial markets. Finally, the paper proposes a synergic overcoming of both EMH and AMH based on the new concept of behavioural entropy in the financial market.

The Testing Filtering and Majorized Algorithm of a Kind of Nonstationary Stochastic Transmission System in Intelligence Control

2014 ◽  
Vol 926-930 ◽  
pp. 3581-3584
Author(s):  
Xiao Nan Xiao
Keyword(s):  
Stochastic Process ◽  
Optimal Control ◽  
Mathematical Models ◽  
Incomplete Data ◽  
Transmission System ◽  
Linear Filtering ◽  
Optimal Coding ◽  
Non Linear ◽  
Intelligence Control ◽  
Nonstationary Stochastic Process

In intelligence control, applying the method of optimal non-linear filtering and majorized algorithm, this paper discusses the optimal control of a kind of incomplete data and continuous nonstationary stochastic process; yields two optimal control mathematical models in these two situations; illustrates how to establish the optimal coding and decoding of the nonstationary stochastic process; and provides an effective and reliable approach for the optimal control of such a process.

Product autoregression: a time-series characterization of the gamma distribution

1982 ◽  
Vol 19 (2) ◽  
pp. 463-468 ◽  
Author(s):  
Ed Mckenzie
Keyword(s):  
Stochastic Process ◽  
Time Series ◽  
Gamma Distribution ◽  
Stationary Stochastic Process ◽  
Correlation Structure ◽  
First Order ◽  
Non Linear ◽  
Autoregressive Correlation

A non-linear stationary stochastic process {Xt} is derived and shown to have the property that both the processes {Xt} and {log Xt} have the same correlation structure, viz. the Markov or first-order autoregressive correlation structure. The generation of such processes is discussed briefly and a characterization of the gamma distribution is obtained.

Risk spillover in financial markets based on support vector quantile regression

2020 ◽  
pp. 1-11
Author(s):  
Wangsong Xie
Keyword(s):  
Quantile Regression ◽  
Financial Markets ◽  
Financial Market ◽  
Financial Risk ◽  
Spillover Effects ◽  
Market Risk ◽  
Support Vector ◽  
Non Linear ◽  
Risk Spillover Effects ◽  
Financial Market Risk

In terms of financial market risk research, with the rapid popularization of non-linear perspectives and the improvement of theoretical reasoning, scholars have slowly broken through the cage of linear ideas and derived new and more practical methods from non-linear perspectives to make up for the shortcomings of traditional research. Based on the support vector classification regression algorithm, this research combines the typical facts and characteristics of financial markets, from the perspective of quantile regression and SVR intelligent technology in computer science, to explore the research method of financial market risk spillover effects from a nonlinear perspective. Moreover, this research integrates statistical research, machine learning and other related research methods, and applies them to the measurement of financial risk spillover effects. The empirical analysis shows that the method proposed in this paper has certain effects, and financial risk analysis can be performed based on the risk spillover effect measurement model constructed in this paper.

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