1,721,005 research outputs found
Interpolation of the Markov Chain and Estimation
No full textLielākā daļa, ja ne visi, pētnieki, kas strādā riska vadības jomā, pievēršas prognozes modeļiem. Laikrindu analīzes metodēm un algoritmiem ir īpaša nozīme, un tie tiek plaši lietoti finanšu ekonometrijā riska novērtēšanai un prognozei. Ir zināms, ka laikrindu analīzes iespējas ierobežo regresijas modeļa izvēle, turklāt pat vienkāršākajiem Markova modeļiem nepieciešama daudzdimensiju sadalījuma funkciju identifikācija. Piedāvātais raksts ir veltīts pusparametrisku stacionāru Markova dinamisku sistēmu analīzei, kas tiek izmantotas mūsdienu matemātikā riska vadības un prognozes lietišķajos autoregresijas modeļos
'Northeast Volatility Wind' Effect Evolution Research by Using Neural Networks
No full textThe ’North-East Volatility Wind’ effect, desribed in previous publications is selected for subsequent research. ’North-East Volatility Wind’ effect is described as effect of volatility transmission from low-frequncy components of the signal to igh-frequency components. According results published before in financial time series a ’North-East Volatility Wind’ effect is observed. This effect is traceable by using special approach approach applicable for stock indexes, allowing to reveal the instability of financial time series initially. This approach is based on time series (signal) decomposition into components by using wavelet filtering with subsequent volatility evolution research of each signal component. According to research, a slight increase in volatility in the low-frequency components of the signal leads to significant disturbances in high-frequency components destine entire signal volatility growth. This approach is based on signal decomposition by using the wavelet filtering. Wavelet filtering is applied by using Direct and Inverse CWT for each scaling parameter.~\cite{3book} Thus for each scaling parameter the signal component (which is part of the original signal) is obtained. For subsequent research volatility indicator is analysed by using 20-days time window, which is shifted on the time axis. Volatility analysis is done for each signal component. As a result volatility
evolution in time is obtained for each signal component. As a next step volatility evolution crosscorrelation analysis is done for each signal component in order to describe volatility transmission from one volatility component to another. In other words volatility transmission from one volatility layer to another is analysed. In current research volatility evolution crosscorrelation analysis is extended by using Neural Network Algorithms, to trace volatility evolution dependences in time, optimising Neural Network structure and finding correspondent weights. In current research complicated relationships between volatility layer are discovered. As a result ’North-East Volatility Wind’ Effect brings out deeper understanding of volatility evolution and opportunity to illuminate most dramatical market drawdowns initially. This opportunity is explained by ability to see a very small changes in volatility logarithm in the low-frequency components of the signal
Nano-Accelerometers for Acceleration Measurement of Objects Moving in the Rarefied Gaseous Environments
No full textSolution of the nano-acceleration problem of solid
body in the diluted gas environment is considered. To resolve the
problem, the interaction effect principle of kinematic solid bodies
in sparse atmosphere is used. The bodies differ by weights,
midsection areas, and facing resistances in the environment,
while there is a variety of moving options for the interaction of
solid bodies in sparse atmosphere in general. A brief
mathematical survey of algorithms, which determine
accelerations of moving objects in the diluted gas environment, is
presented. The nano-accelerometer algorithm for the object with
two solid bodies in case, where the body weights are constants or
changed occasionally in time, is studied. Specifications of the
body orbit under external forces in the sparse gas environment
are received depending on the orbit correction intervals.
Modelling of the object with nano-accelerometer is presented by
means of MatLab/Simulink software. Investigation results
confirm the effectiveness of using nano-accelerometer for this
class of real-world objects in the diluted gas environment
Time-Optimal Adaptive Control of Dynamic Systems
No full textThis paper solves the task of the associative control of an object, whose parameters are changing in an uncertain way. To enable efficient control, it is necessary to constantly correct model’s parameters, that is, to have the associative control with the parametric adaptation. Thus the task of control of an object comprises the algorithm of estimation of object’s parameters and algorithm of control of this object. The solution of this task relates to the class of associative algorithms. This paper provides the results of investigations of the dynamic system with variable parameters. To perform the control efficiently, this system would be supported by timeoptimal high speed adaptive control using the algorithms of object’s parameter identification
Evaluation of Dynamics of the VIX Index Via Heston Model.
No full textA methodology for the estimation of parameter of a stochastic model
using discontinuous models (ARIMA class) and based on the financial market data
is introduced. We show how to apply our technique
to the financial index VIX - a market mechanism that measures the 30-day forward
implied volatility of the underlying index, the S&P 500. Also the results with
regression model of time series which produced by Heston volatility model are
considered
Increments of Normal Inverse Gaussian Process as Logarithmic Returns of Stock Price
No full textNormal inverse Gaussian (NIG) distribution is quite a new distribution introduced in 1997. This is distribution, which describes evolution of NIG process. It appears that in many cases NIG distribution describes log-returns of stock prices with a high accuracy. Unlike normal distribution, it has higher kurtosis, which is necessary to fit many historical returns. This gives the opportunity to construct precise algorithms for hedging risks of options. The aim of the present research is to evaluate how well NIG distribution can reproduce stock price dynamics and to illuminate future fields of application
Copula Based Nonparametric Regression Estimation
No full textThe methods and algorithms of time series analysis play an important role in financial econometrics
for identification and prediction of risk. The paper deals with the identification and
prediction problems of the autoregressive models of nonlinear time series using nonparametric
estimates of the conditional mean and conditional variance
Stochastic Stability of Pipeline, Induced by Pulsed Fluid Flow
No full textThe paper deals with linearized partial differential equation for analysis of the transverse oscillations of a pipeline section under an action of pulse fluid flow.
Assuming the mathematical model of fluid-caused longitudinal force in cosine form with the Brownian phase and applying the stochastic modification of the second Lyapunov method we analyze a phase variance affect the pipeline section stability
Copula Based Nonparametric Regression Estimation
No full textRaksts ir veltīts pusparametrisku stacionāru Markova dinamisku sistēmu analīzei, kas tiek izmantotas mūsdienu matemātikā riska vadības un prognozes lietišķajos autoregresijas modeļos. Mūsu piedāvātā stratēģija izmanto nelineārus autoregresijas modeļus neklasiskām heteroskedastiskām kļūdām. Tā balstās mūsdienīgā tehnoloģijā, ietverot kopulās bāzētu neparametrisku līkņu atbilstības analīzi, gan lai iegūtu novērtējumus, gan lai vērtētu parametrisku modeļu ticamību. Šo stohastisku dinamisku sistēmu analīzes instrumentu raksturo neparametriski marginālie sadalījumi un parametriskas kopulas funkcijas, turklāt kopula ietver sevī visu procesu atkarību bezdimensionālā laika atkarīgā formā
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