Decision Making with Quantitative Financial Market Data: Applications, Precautions and Pitfalls (SpringerBriefs in Operations Research)

✍ Scribed by Alain Ruttiens

Publisher: Springer
Year: 2021
Tongue: English
Leaves: 69
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

Use of quantitative data, especially in financial markets, may provide rapid results due to the ease-of-use and availability of fast computational software, but this book advises caution and helps to understand and avoid potential pitfalls.

It deals with often underestimated issues related to the use of financial quantitative data, such as non-stationarity issues, accuracy issues and modeling issues. It provides practical remedies or ways to develop new calculation methodologies to avoid pitfalls in using data, as well as solutions for risk management issues in financial market.

The book is intended to help professionals in financial industry to use quantitative data in a safer way.

✦ Table of Contents

Preface
Contents
Chapter 1: Basic Notions
1.1 Data: Quantitative Versus Qualitative
1.2 Measure: Exact, or Not?
1.3 Differences, Spreads, and Percentages
1.4 Data as Time Series
1.5 Usual Statistics on Data in Probabilistic Time Series
Chapter 2: A Major Problem in Using Time Series of Data: Stationarity
2.1 A Preliminary Issue: The Case of Data Frequency
2.2 The Problem of Stationarity
Chapter 3: Another Major Problem in Using Time Series of Data: The Accuracy of the Statistical Measures
3.1 Confidence Interval and Standard Error
The Confidence Interval
The Standard Error
3.2 Application 1
3.3 Application 2
3.4 A Simplified Alternative to the Use of Confidence Interval or Standard Error
Non-stationarity and Sample Size: Squaring the Circle?
Chapter 4: Issues About Modeling
4.1 General
4.2 Modeling the Behavior of a Market and Related Valuation Models
4.3 Modeling Derivatives Prices
4.4 Using the ARCH Family of Processes in Derivatives Modeling
4.5 Modeling Credit Derivatives Prices
4.6 Use of Non-Gaussian Distributions in Modeling
4.7 Use of Non-Gaussian Distributions in Modeling: A Step Further
4.8 Other Potential Troubles with Derivatives Valuation
4.9 Algorithms: Quantitative (Algorithmic´´) Trading Models 4.10 As a Conclusion to This Chapter: Issues About Modeling Chapter 5: Financial Data: Some Risk Management Issues 5.1 Using the Volatility Measure of Losses, That Is, of Negative Returns 5.2 The Time´s Arrow Matters: TheAccrued Returns Variability´´ Measure
5.3 The Case of the Controversial VaR (``Value at Risk´´) Measure
Chapter 6: Synthesis

📜 SIMILAR VOLUMES

Military Operations Research: Quantitati

📁 Military Operations Research: Quantitative Decision Making

✍ N. K. Jaiswal (auth.) 📂 Library 📅 1997 🏛 Springer US 🌐 English

Operations Research (OR) emerged in an effort to improve the effectiveness of newly inducted weapons and equipment during World War II. While rapid growth ofOR led to its becoming an important aid to decision making in all sectors including defense, its contribution in defense remained largely co

Military Operations Research: Quantitati

📁 Military Operations Research: Quantitative Decision Making

✍ N.K. Jaiswal 📂 Library 📅 1997 🏛 Springer 🌐 English

Operations Research (OR) emerged in an effort to improve the effectiveness of newly inducted weapons and equipment during World War II. While rapid growth ofOR led to its becoming an important aid to decision making in all sectors including defense, its contribution in defense remained largely confi

Quantile Regression for Cross-Sectional

📁 Quantile Regression for Cross-Sectional and Time Series Data: Applications in Energy Markets Using R (SpringerBriefs in Finance)

✍ Jorge M. Uribe 📂 Library 📅 2020 🏛 Springer 🌐 English

This brief addresses the estimation of quantile regression models from a practical perspective, which will support researchers who need to use conditional quantile regression to measure economic relationships among a set of variables. It will also benefit students using the methodology for

Optimal Decision Making in Operations Re

📁 Optimal Decision Making in Operations Research and Statistics: Methodologies and Applications

✍ Irfan Ali, Leopoldo Eduardo Cárdenas-Barrón, Aquil Ahmed, Ali Akbar Shaikh 📂 Library 📅 2021 🏛 CRC Press 🌐 English

The book provides insights in the decision-making for implementing strategies in various spheres of real-world issues. It integrates optimal policies in various decisionmaking problems and serves as a reference for researchers and industrial practitioners. Furthermore, the book provides so

Fuzzy Optimization, Decision-making and

📁 Fuzzy Optimization, Decision-making and Operations Research: Theory and Applications

✍ Chiranjibe Jana (editor), Madhumangal Pal (editor), Ghulam Muhiuddin (editor), P 📂 Library 📅 2023 🏛 Springer 🌐 English

After developing fuzzy set theory, many contributors focused their research on the extension of fuzzy sets and their computational methodologies, strengthening modern science and technology. In some real-life phenomena, the conventional methods and traditional fuzzy sets cannot be explained

Tutorials in Operations Research: Models

📁 Tutorials in Operations Research: Models, Methods, and Applications for Innovative Decision Making

✍ Johnson M.P. 📂 Library 📅 2006 🌐 English

This year, the Pittsburgh meeting Chair, Michael Trick, appointed three Tutorials Co-Chairs—Michael P. Johnson and Nicola Secomandi of Carnegie Mellon University, and Bryan Norman of the University of Pittsburgh who serve as coeditors of this volume. They have assembled nine tutorials for this volum