The Risk Measurement under the Variance-Gamma Process with Drift Switching

The paper discusses an extension of the variance-gamma process with stochastic linear drift coefficient. It is assumed that the linear drift coefficient may switch to a different value at the exponentially distributed time. The size of the drift jump is supposed to have a multinomial distribution. We have obtained the distribution function, the probability density function and the lower partial expectation for the considered process in closed forms. The results are applied to the calculation of the value at risk and the expected shortfall of the investment portfolio in the related multivariate stochastic model.

Modeling the volatity of cryptocurrency markets

The application of the model of geometric Brownian motion (GBM) for the problem of modeling and forecasting prices for cryptocurrencies is analyzed. For prediction the solution of the stochastic differential equation of the GBM model is used, which has a linear drift and diffusion coefficients. Different scenarios of price movement are considered. Keywords: geometric Brownian motion (GBM), modeling, forecasting, cryptocurrency.

An Efficient and Flexible Window Function for Memristor Model and its Analog Circuit Application

The memristor is a nanostructure resistive tuning two terminal novel electronics device that has been widely explored in the area of neuromorphic computing systems, memories, digital circuits, analog circuits and many more new applications. In this article an efficient and flexible window function is presented for linear drift memristor model. Propose window function provides a unique feature (controllable window function discontinuity) to linear drift memristor model by which DPHL (Distorted Pinched Hysteresis Loop) problem is resolved and also improved the programming resistance state of the memristor. Five control parameters are introduced in the presented window function, in order to fix the pre-existing problem (like boundary effect, boundary lock and inflexibility) and make it more flexible. The programmable analog gain amplifier circuit is ultimately executed to instantiate the utilization of evolved memristor model.

Degradation modelling of railway infrastructure

<p>In this paper the Wiener process with linear drift is proposed as a degradation model for the railway substructure. The model is based on the empirical data describing loss of tracks quality over the distance of one kilometre. The impact of explanatory variables on tracks degradation is investigated. The Bayesian framework is applied to examine uncertainty in the development of degradation for different sections along the tracks.</p>

Reliability analysis of a system with two-stage degradation using Wiener processes with piecewise linear drift

We study a model of a two-stage degradation process in a dynamic environment. The two stages, the normal stage and the defective stage, are separated by the first hitting time of the alarm threshold by the degradation level. Wiener processes with piecewise linear drift are used in each stage to describe the degradation level in a dynamic environment. System failure is triggered in two ways: the system degradation level reaches the defect-based failure threshold; the duration in the defective stage is larger than the duration-based failure threshold. Explicit expressions for the system reliability for different duration-based failure thresholds are obtained. These include when the duration-based failure threshold is zero, when it is a positive constant and when it tends to infinity. A simulation procedure is described for the case in which the duration-based failure threshold is a random variable. Finally, some numerical examples are presented to illustrate the proposed reliability assessment method. The modelling method and the results can be used for reliability evaluation, residual life prediction and maintenance decision optimization of a system with two-stage degradation in a dynamic environment.