Digital twin can be defined as a virtual representation of a physical asset enabled through data and simulators for real-time prediction, optimization, monitoring, controlling, and improved decision making. Recent advances in computational pipelines, multiphysics solvers, artificial intelligence, big data cybernetics, data processing and management tools bring the promise of digital twins and their impact on society closer to reality. Digital twinning is now an important and emerging trend in many applications. Also referred to as a computational megamodel, device shadow, mirrored system, avatar or a synchronized virtual prototype, there can be no doubt that a digital twin plays a transformative role not only in how we design and operate cyber-physical intelligent systems, but also in how we advance the modularity of multi-disciplinary systems to tackle fundamental barriers not addressed by the current, evolutionary modeling practices. In this work, we review the recent status of methodologies and techniques related to the construction of digital twins mostly from a modeling perspective. Our aim is to provide a detailed coverage of the current challenges and enabling technologies along with recommendations and reflections for various stakeholders.

/pdf/digital-twin-values-challenges-and-enablers-from-a-modeling-3hpoelaogn.pdf

Digital Twin: Values, Challenges and Enablers From a Modeling Perspective

Investment in Capital Markets creates a strategic vision on the financial capital investment in the capital markets with the aim to get an increased return premium in the short and long time periods. The book is written with a main goal to explain the pros and cons of the financial capital investment in the capital markets, discussing the sophisticated investment concepts and techniques in the simple understandable readable general format language. We would like to highlight the three interesting facts about the book: 1. It is centered on the consideration of the modern investment products, the investment vehicles and the investment mediums for the financial capital investment in the capital markets; 2. It is focused on the financial risk calculation and mitigation techniques for the financial capital investment in the financial capital markets. 3. It is aimed to describe the quantum winning virtuous investment strategies creation and execution techniques during the financial capital investment in the capital markets. The investors, financiers, economists, financial analysts, financial traders, financial advisers, lawmakers, policy analysts, subject experts, professors, and students will certainly enjoy a breathtaking splendid learning journey with the explained new ideas, established concepts and outlined future prospects toward the financial capital investment in the capital markets with the aim to get an increased return premium in the short and long time periods.

Investment in capital markets

Structural time series models are formulated in terms of components, such as trends, seasonals and cycles, that have a direct interpretation. As well as providing a framework for time series decomposition by signal extraction, they can be used for forecasting and for `nowcasting'. The structural interpretation allows extensions to classes of models that are able to deal with various issues in multivariate series and to cope with non-Gaussian observations and nonlinear models. The statistical treatment is by the state space form and hence data irregularities such as missing observations are easily handled. Continuous time models offer further flexibility in that they can handle irregular spacing. The paper compares the forecasting performance of structural time series models with ARIMA and autoregressive models. Results are presented showing how observations in linear state space models are implicitly weighted in making forecasts and hence how autoregressive and vector error correction representations can be obtained. The use of an auxiliary series in forecasting and nowcasting is discussed. A final section compares stochastic volatility models with GARCH.

Forecasting with Unobserved Components Time Series Models

In this paper we analyze the monetary transmission mechanism. In general, the monetary transmission mechanism describes how policy-induced changes in the nominal money stock or the short-term nominal interest rate impact on real variables such as aggregate output and employment. Specific channels of monetary transmission operate through the effects that monetary policy has on interest rates, exchange rates, equity and real estate prices, bank lending and firm balance sheets (Ireland 2005). Recent research on the transmission mechanism seeks to understand how these channels work in the context of dynamic, stochastic, general equilibrium models (e.g. Barran et al. 1995; Boivin and Giannoni 2006; Fukuda 1993; Golinelli and Rovelli 2005; Goodhart and Hofmann 2005; Kim 2003; Lutkepohl and Wolters 2003).

On the Transmission Mechanism of Monetary Policy

Socially Responsible Investment and cleaner production in the Asia Pacific: does it pay to be good?

Preface. 1. Introduction. 2. Test for parameter stability. 3. Flexible Least Squares. 4. The Kalman filter. 5. Parameter estimation. 6. The estimates, reconsidered. 7. Modeling with the Kalman filter. A. Tables of references. B. The programs and the data. Bibliography. Index.

https://www.springer.com/cda/content/document/productFlyer/productFlyer_978-0-7923-3771-3.pdf?SGWID=0-0-1297-33197774-0

Curt Wells

Papers

The Kalman filter in finance