Stochastic Discounting And Simulation A Capital Budgeting Model Applied In The Greek Banking Industry

The main purpose of the present research is to develop a capital budgeting stochastic simulation model for quantifying the risk and uncertainty inherent in the establishment of a new branch in Greece by a larqe multinational bank. In examining critically the existing investment evaluation methods the risk simulation approach proved a valid tool for risk and uncertainty analysis, mainly for its strength in quantifying the risk and uncertainty and its applicability to real life situations. The investigation of the Greek banking system, the environment the sample bank operates in, showed the emergence of an increasing number of profitable opportunities over the last years associated, however, with a larger degree of risk. The development of the research model, expresses the key variables of the investment project in the form of mathematical equations showing both all kind of relationships and interdependencies that exist among certain variables, and the way each variable affects the profitability criterion that is used to evaluate the investment project. Subjective probability distributions are used as a means of data inputs. The computer simulation program performs a repeatedly discounted cash flow computation with the values of the random variables being modified between iterations in accordance with their associated subjective probability distributions. The output of the simulation program is a probability distribution of the NPV and the IRR associating each possible outcome with the probability of its occurrence. The statistical analysis of the output of the simulation allows the management of the sample bank to discriminate among measures of expected return based on their probability of occurrence. Moreover, it provides a measure of the maximum risk they would be willing to accept. Finally, the superiority of the information obtained from the risk simulation approach is illustrated numerically, by comparing the output of the computer simulation program with the results produced by the method the sample bank is currently employinq to evaluate investment proposals.

Unlike other books that focus only on selected specific subjects this book provides both a broad and rich cross-section of contemporary approaches to stochastic modeling in finance and economics; it is decision making oriented. The material ranges from common tools to solutions of sophisticated system problems and applications. In Part I, the fundamentals of financial thinking and elementary mathematical methods of finance are presented. The method of presentation is simple enough to bridge the elements of financial arithmetic and complex models of financial math developed in the later parts. It covers characteristics of cash flows, yield curves, and valuation of securities. Part II is devoted to the allocation of funds and risk management: classics (Markowitz theory of portfolio), capital asset pricing model, arbitrage pricing theory, asset & liability management, value at risk. The method explanation takes into account the computational aspects. Part III explains modeling aspects of multistage stochastic programming on a relatively accessible level. It includes a survey of existing software, links to parametric, multiobjective and dynamic programming, and to probability and statistics. It focuses on scenario-based problems with the problems of scenario generation and output analysis discussed in detail and illustrated within a case study. Selected examples of successful applications in finance, production planning and management of technological processes and electricity generation are presented. Throughout, the emphasis is on the appropriate use of the techniques, rather than on the underlying mathematical proofs and theories. In Part IV, the sections devoted to stochastic calculus cover also more advanced topics such as DDS Theorem or extremal martingale measures, which make it possible to treat more delicate models in Mathematical Finance (complete markets, optimal control, etc.) Audience: Students and researchers in probability and statistics, econometrics, operations research and various fields of finance, economics, engineering, and insurance.

Theses on any subject submitted by the academic libraries in the UK and Ireland.

The objective of this paper is to present an integrated tool suite for IFRS 9- and CECL-compatible estimation in top-down solvency stress tests. The tool suite serves as an illustration for institutions wishing to include accounting-based approaches for credit risk modeling in top-down stress tests.

At the height of the Great Depression a number of leading U.S. economists advanced a proposal for monetary reform that became known as the Chicago Plan. It envisaged the separation of the monetary and credit functions of the banking system, by requiring 100% reserve backing for deposits. Irving Fisher (1936) claimed the following advantages for this plan: (1) Much better control of a major source of business cycle fluctuations, sudden increases and contractions of bank credit and of the supply of bank-created money. (2) Complete elimination of bank runs. (3) Dramatic reduction of the (net) public debt. (4) Dramatic reduction of private debt, as money creation no longer requires simultaneous debt creation. We study these claims by embedding a comprehensive and carefully calibrated model of the banking system in a DSGE model of the U.S. economy. We find support for all four of Fisher's claims. Furthermore, output gains approach 10 percent, and steady state inflation can drop to zero without posing problems for the conduct of monetary policy.

This four-volume handbook covers important concepts and tools used in the fields of financial econometrics, mathematics, statistics, and machine learning. Econometric methods have been applied in asset pricing, corporate finance, international finance, options and futures, risk management, and in stress testing for financial institutions. This handbook discusses a variety of econometric methods, including single equation multiple regression, simultaneous equation regression, and panel data analysis, among others. It also covers statistical distributions, such as the binomial and log normal distributions, in light of their applications to portfolio theory and asset management in addition to their use in research regarding options and futures contracts.In both theory and methodology, we need to rely upon mathematics, which includes linear algebra, geometry, differential equations, Stochastic differential equation (Ito calculus), optimization, constrained optimization, and others. These forms of mathematics have been used to derive capital market line, security market line (capital asset pricing model), option pricing model, portfolio analysis, and others.In recent times, an increased importance has been given to computer technology in financial research. Different computer languages and programming techniques are important tools for empirical research in finance. Hence, simulation, machine learning, big data, and financial payments are explored in this handbook.Led by Distinguished Professor Cheng Few Lee from Rutgers University, this multi-volume work integrates theoretical, methodological, and practical issues based on his years of academic and industry experience.

This book teaches multiple regression and time series and how to use these to analyze real data in risk management and finance.