Finance Theory And Applications
Theory and Application
Book • 2017
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Despite its major advances, finance theory has had scant impact on strategic planning. Strategic planning needs finance and should learn to apply finance theory correctly. However, finance theory must be extended in order to reconcile financial and strategic analysis. Mathematical finance, also known as quantitative finance and financial mathematics, is a field of applied mathematics, concerned with mathematical modeling of financial markets.Generally, mathematical finance will derive and extend the mathematical or numerical models without necessarily establishing a link to financial theory. Finance: Applications and Theory (McGraw-Hill/Irwin Series in Finance, Insurance, and Real Est): 288: Economics Books @ Amazon.com. Simple linear regression is commonly used in forecasting and financial analysis—for a company to tell how a change in the GDP could affect sales, for example. Microsoft Excel and other software can.
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Fractional Calculus and Fractional Processes with Applications to Financial Economics presents the theory and application of fractional calculus and fractional processes to financial data. Fractional calculus dates back to 1695 when Gottfried Wilhelm Leibniz first suggested the possibility of fractional derivatives. Research on fractional calculus started in full earnest in the second half of the twentieth century. The fractional paradigm applies not only to calculus, but also to stochastic processes, used in many applications in financial economics such as modelling volatility, interest rates, and modelling high-frequency data. The key features of fractional processes that make them interesting are long-range memory, path-dependence, non-Markovian properties, self-similarity, fractal paths, and anomalous diffusion behaviour. In this book, the authors discuss how fractional calculus and fractional processes are used in financial modelling and finance economic theory. It provides a practical guide that can be useful for students, researchers, and quantitative asset and risk managers interested in applying fractional calculus and fractional processes to asset pricing, financial time-series analysis, stochastic volatility modelling, and portfolio optimization.
Fractional Calculus and Fractional Processes with Applications to Financial Economics presents the theory and application of fractional calculus and fractional processes to financial data. Fractional calculus dates back to 1695 when Gottfried Wilhelm Leibniz first suggested the possibility of fractional derivatives. Research on fractional calculus started in full earnest in the second half of the twentieth century. The fractional paradigm applies not only to calculus, but also to stochastic processes, used in many applications in financial economics such as modelling volatility, interest rates, and modelling high-frequency data. The key features of fractional processes that make them interesting are long-range memory, path-dependence, non-Markovian properties, self-similarity, fractal paths, and anomalous diffusion behaviour. In this book, the authors discuss how fractional calculus and fractional processes are used in financial modelling and finance economic theory. It provides a practical guide that can be useful for students, researchers, and quantitative asset and risk managers interested in applying fractional calculus and fractional processes to asset pricing, financial time-series analysis, stochastic volatility modelling, and portfolio optimization.
Key Features
- Provides the necessary background for the book's content as applied to financial economics
- Analyzes the application of fractional calculus and fractional processes from deterministic and stochastic perspectives
- Provides the necessary background for the book's content as applied to financial economics
- Analyzes the application of fractional calculus and fractional processes from deterministic and stochastic perspectives
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Language
English
Copyright
Copyright © 2017 Elsevier Ltd. All rights reserved.
No. of pages
118
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Abstract: We develop a new stock market index that captures the chaos existing in themarket by measuring the mutual changes of asset prices. This new index relieson a tensor-based embedding of the stock market information, which in turnfrees it from the restrictive value- or capitalization-weighting assumptionsthat commonly underlie other various popular indexes. We show that our index isa robust estimator of the market volatility which enables us to characterizethe market by performing the task of segmentation with a high degree ofreliability. In addition, we analyze the dynamics and kinematics of therealized market volatility as compared to the implied volatility by introducinga time-dependent dynamical system model. Our computational results whichpertain to the time period from January 1990 to December 2019 imply that thereexist a bidirectional causal relation between the processes underlying therealized and implied volatility of the stock market within the given timeperiod, where it is shown that the later has a stronger causal effect on theformer as compared to the opposite. This result connotes that the impliedvolatility of the market plays a key role in characterization of the market'srealized volatility.
Submission history
From: Masoud Ataei [view email][v1]Tue, 5 Jan 2021 18:01:13 UTC (5,229 KB)
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