DEVELOPMENT OF MODELS OF TECHNOLOGY TRANSFER FOR PUBLIC WORKS

The aim of the study is to improve the efficiency of technology transfer management in public works by developing a model for the reasonable definition of transfer objects and their components. The subject of the study are models of reasonable definition of technology transfer objects and their components. The basis of the methodology of research are the principles and models of technology transfer, methods and models of the theory of project management, as well as system analysis. The paper shows that for the formation and implementation of public works for various purposes, the goal of using technology transfer processes should be considered the achievement of the highest level of technological development. Under the conditions of the technological development processes analysis of the individual economic sectors, possible models to achieve different levels of technological development and technical-technological indicators of public works are defined. These include the "gradual" transition model, the "jumps" model, and the "big jump" model. These models are characterized by the following: the model of "gradual" transition assumes that its main feature should be considered a gradual transition from the actual level of technological development to the one following it; for the model of "jumps" the main feature should be considered the possibility of transition to a higher level of technological development bypassing the next in order from the actual level; the "big jump" model differs from the "jumps" one in that a significantly low initial level of technological development immediately reaches the maximum level, which takes place for a certain time. The chosen model of processes to achieve different levels of technological development significantly affects the planning of qualitative and quantitative indicators of public works. Identification of potential objects of transfer and their components is proposed to solve through the development of classification features of public works and programs, as well as the formation of their information models. The main classification attributes of public works should be considered: organizational, technological, as well as equipment and materials. As the main indicators to ensure effective management decisions in the planning of public works should be used the cost of transfer, as well as the timing of delivery of objects. The conditions for selecting the object of transfer and its components in terms of cost and time for different models of acquisition of the appropriate technological level are defined. A model of technology transfer object distribution and an algorithm for planning public works using technology transfer was developed. The implementation of the research results on a number of public works for the development of municipal heating and water supply systems has proven their effectivenessTaking into account the significant resources needed for public works and programs, the research findings should be considered important for various sectors of the economy.

The Negative Impact of COVID-19 on Life Insurers

Understanding COVID-19 induced mortality risk is significant for life insurers to better analyze their financial sustainability after the outbreak of COVID-19. To capture the mortality effect caused by COVID-19 among all ages, this study proposes a temporary adverse mortality jump model to describe the dynamics of mortality in a post-COVID-19 pandemic world based on the weekly death numbers from 2015 to 2021 in the United States. As a comparative study, the Lee-Carter model is used as the base case to represent the dynamics of mortality without COVID-19. Then we compare the force of mortality, the survival probability and the liability of a life insurer by considering COVID-19 and those without COVID-19. We show that a life insurer's financial sustainability will deteriorate because of the higher mortality rates than expected in the wake of COVID-19. Our results remain unchanged when we also consider the effect of interest rate risk by adopting the Vasicek and CIR models.

A lattice approach for option pricing under a regime-switching GARCH-jump model

In this study, we consider option pricing under a Markov regime-switching GARCH-jump (RS-GARCH-jump) model. More specifically, we derive the risk neutral dynamics and propose a lattice algorithm to price European and American options in this framework. We also provide a method of parameter estimation in our RS-GARCH-jump setting using historical data on the underlying time series. To measure the pricing performance of the proposed algorithm, we investigate the convergence of the tree-based results to the true option values and show that this algorithm exhibits good convergence. By comparing the pricing results of RS-GARCH-jump model with regime-switching GARCH (RS-GARCH) model, GARCH-jump model, GARCH model, Black–Scholes (BS) model, and Regime-Switching (RS) model, we show that accommodating jump effect and regime switching substantially changes the option prices. The empirical results also show that the RS-GARCH-jump model performs well in explaining option prices and confirm the importance of allowing for both jump components and regime switching.

Interest Rate Modelling in the Presence of Discontinuities and its Sensitivities

Interest rate paths experience discontinuities in the presence of certain factors. Much of the work on interest rate modelling has no consideration for effects of such unexpected occurrences in real life. A good risk manager needs to have a better model that considers possibility of unexpected occurrences. In this paper, we discuss step by step extension of Vasicek model to both jump model and jumpdiffusion model using Itô’s formula as the major tool. We also derive the greeks ‘delta’ and ‘vega’ that measure sensitivity of the interest rate with respect to both changes in its initial interest rate and volatility in an interbank rate.

Prognosis of a Degradable Hydraulic System

This article proposes a preliminary diagnostic/prognostic method for the identification of a critical system, undergoing a continuous evolutionary degradation, in a production area, and the determination of the component responsible for its degradation, called the failing element. Using for this, a model based on learning by multilayer perception (MLP). The purpose of this paper is to provide a modeling approach that makes it possible to determine the level of degradation reached by the system at any given point of time, in a precise way. Thus, the horizon of the failure will be produced with a minimum error compared to the discrete jump model used in the literature. The proposed approach consists of using a neural network with fewer layers and optimal computing time. We performed data learning (tests) in order to illustrate a regression of good correlation of these data (tests) on a centrifugal pump with satisfactory performance parameters and compared it with other commonly used methods.