Arbitrage Pricing of Weather Derivatives and the Stochastic Process for the Expectation of Non-Linear Weather Indices

2004 ◽  
Author(s):  
Stephen Jewson
Keyword(s):  
Stochastic Process ◽  
Weather Derivatives ◽  
Arbitrage Pricing ◽  
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.

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.

RELIABILITY OF DETERIORATED MARINE STRUCTURES BASED ON MEASURED DATA

2021 ◽  
Vol 158 (A4) ◽  
Author(s):  
Y Garbatov ◽  
C Guedes Soares
Keyword(s):  
Stochastic Process ◽  
Time Series ◽  
Initial Period ◽  
Linear Increase ◽  
General Corrosion ◽  
Marine Structures ◽  
Depth Data ◽  
Reliability Estimates ◽  
Non Linear ◽  
Spectral Density Functions

Reliability assessment of a corroded deck of a tanker ship subjected to non-linear general corrosion wastage is performed, accounting for an initial period without corrosion due to the presence of a corrosion protection system, and a non-linear increase in wastage up to a steady state value. The reliability model is based on the analysis of corrosion depth data. Two types of uncertainties are accounted for. The first one is related to the corrosion degradation trend as a function of time, which is identified by a sequence independent data analysis. The second uncertainty is related to the variation of the corrosion degradation around its trend, which is identified as a stochastic process, and is defined based on the time series analysis. The time series determines the autocorrelation and spectral density functions of the stochastic process applying the Fast Fourier transform. The reliability estimates with respect to a corroded deck of cargo tank of a tanker ship is analysed by a time variant formulation and the effect of inspections is also incorporated employing the Bayesian updating formulation.

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

1982 ◽  
Vol 19 (02) ◽  
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.

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