Computational Methods for Management
This article focuses on computational methods for business and financial management. It provides an overview of the wide range of computational methods employed by business and financial managers in the public and private sectors. Computational tools for managerial forecasting and decision-making, including financial econometrics, computational finance, futures, options, and derivatives analysis, capital budgeting analysis, decisional analysis, and financial time series analysis, will be described. The issues associated with using computational methods for financial or corporate risk analysis are addressed.
Keywords Computational Finance; Computational Methods; Decisional Analysis; Econometrics; Financial Management; Financial Time Series Analysis; Forecasting
Economics: Computational Methods for Management
The financial and business management fields use a wide range of computational methods to solve business and management problems. Computational methods refer to a wide range of numerical and quantifiable approaches to data gathering and analysis. Examples of computational methods common in management include financial econometrics; computational finance; futures, options, and derivatives analysis; capital budgeting analysis; decisional analysis; and financial time series analysis. Computational methods, in contrast to purely theoretical methods, are increasingly used in the field of financial risk management. Financial risk management refers to the effort of financial institutions to protect against the negative outcomes caused by fluctuations in interest rates, exchange rates, commodity prices, and equity prices. Computational methods, financial instruments, and mathematical techniques are used by an increasing number of firms, traders, and financial risk managers across businesses and industries. Financial and business managers use computational methods to develop forecasts of future conditions in areas such as economic cycles, product demand, and market activity. Managers base their financial and business decisions on these forecasts of future conditions.
While business and financial management uses computational methods to forecast future conditions, computational methods, as a general approach to analysis and problem solving, are common practice in multiple fields and industries. According to the "International Journal of Computational Methods" (IJCM), modern computational methods are used across industries and fields of scientific inquiry. Examples of modern computational methods used in multiple fields and industries include the following: “Mathematical formulations and theoretical investigations; interpolations and approximation techniques; error analysis techniques and algorithms; fast algorithms and real-time computation; multi-scale bridging algorithms; and adaptive analysis techniques and algorithms” (About IJCM, 2007, ¶2).
Computational methods, as expressed through theory, algorithm, programming, coding, and numerical simulation, are most common in all fields with quantifiable data such as economics, engineering, science, and computer science. Examples of computational methods used in engineering, science, computer science, and economics include risk management, computational mechanics, computational inverse problem, computational mathematics, quantum methods, advanced finite volume methods, and high-performance computing techniques. Financial and business managers employ subfields of computational methods such as econometrics and computational finance. Econometrics refers to applying statistical theories to economic ones in order to predict future trends. Computational finance refers to the use of advanced computing techniques to study problems in economics and finance. Examples of computational finance include genetic programming, used for financial forecasting, and financial markets models used to facilitate the design of new market mechanisms.
Econometrics and computational finance are just two of a wide range of computational subfields and tools used by financial and business managers. Financial and business managers use computational methods to accomplish the following goals: Forecasts of future conditions and events; financial modeling; value estimation; financial risk analysis; futures, options, and derivatives analysis; capital budgeting analysis; and financial time series analysis. Computational data contributes to the following knowledge base: Explains predictability, persistence, and differences between conditional and unconditional moments; answers questions of asset pricing and financial motivation; simulates financial models on the modern computational environment; and conducts a computational evaluation of derivative instruments (Chaundry, Varano, & Xu, 2000).
This article provides an overview of the wide range of computational methods employed by business and financial managers in the public and private sectors. The following sections provide a discussion and analysis of financial econometrics, computational finance, futures, options, and derivatives analysis, capital budgeting analysis, decisional analysis, and financial time series analysis. This sections serves as a foundation for later discussion of the issues associated with using computational methods for financial or corporate risk analysis.
Computational methods, while not necessary or applicable for every management problem, are appropriate for computationally intensive management tasks. For example, optimization problems, which tend to be computationally intensive tasks for managers, are well suited to computational analysis (Tsompanakis, Y., & Papadrakakis, M., 2000). In addition, computational methods are appropriate for using rough sets to identify classes, dependencies, and rules in datasets and databases. These findings may be used for managerial operations, forecasting, and problem solving (Bell & Guan, 1998). Computational methods, for both managers and all other users, generally require a foundation in computer modeling, programs, and languages, hypothesis testing, simulation methodology, calculus, probability, and statistics. Managers who use computational methods for forecasting and data analysis often rely on software packages, such as Mathematica, for technical computing tasks. Computational software packages do simple calculations, large-scale computations, complex programming, and data modeling. The following sections describe the main computational methods used in financial and business management.
Financial econometrics, the act of applying statistical theories to economic ones in order to predict future trends, is a form of financial modeling. Financial econometrics combines tools and perspectives from statistics, mathematics, economics, and business. Econometric methods are intended to be actively applied and adapted to financial problems and datasets. The simplest econometric model in financial econometrics is the first-order autoregressive model often referred to as the autoregressive moving average (ARMA). This model helps explain predictability, persistence, and differences between conditional and unconditional moments.
Econometric methods of vector auto-regression (VAR), vector-autoregressive moving average (VARMA), simultaneity, and co-integration are used to answer questions of asset pricing and understand financial motivation. These methods are useful for the creation and maintenance of efficient portfolios. Financial econometrics includes numerous value estimation strategies such as generalized method of moments (GMG). Autoregressive conditionally heteroskedastic (ARCH) and stochastic volatility models connect the volatility of asset prices to risk management. The autoregressive conditionally heteroskedastic model is considered to be a significant breakthrough that helped economists develop empirical evidence contradicting the assumption of the unpredictability of returns.
In addition, asset-pricing models, such as the present value model, dynamic factors model, and derivatives model, are financial econometrics used to analyze financial motivation. Markets can be understood with the financial econometric tools of high-frequency data, market indexes, vale-at-risk (VaR), and extreme-risk modeling. Market indexes refer to approximations to the market portfolio. "Value-at-risk determines the minimum capital required to cover a financial loss with a fixed probability of occurrence" (Kmenta, 2002, p.70). Financial econometrics uses...
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