For quite a long time now the main concern for investors as well as regulators of financial markets has been the risk of catastrophic market and the sufficiency of capital needed to counter such kind of risk when it occurs. Many institutions have undergone loses despite their gigantic nature and good forecasting and this has been associated with inappropriate forms of pricing and poor management together with the fraudulent cases, factors that have always brought the issue of managing risk and regulating these financial markets to the level of public policy as well as discussion. A basic tool that has been identified as being effective in the assessment of financial risk is the Value at Risk (VaR) process (Artzner, et al., 1997). The VaR has been figured out as being an amount that is lost on a given form of portfolio including a small probability in a certain fixed period of time counted in terms of days. VaR however poses a major challenge during its implementation and this has more to do with the specification of the kind of probability distribution having extreme returns that is made use of during the calculation of the estimates used in the VaR analysis (Mahoney, 1996; McNeil & Frey, 2000; Dowd, 2001). As has been noted, the nature of VaR estimation majorly does depend on the accurate predictions of some uncommon events or risks that are catastrophic. This is attributed to the fact that VaR is a calculation made from the lowest portfolio returns. For this reason, any form of calculation that is employed in the estimation of VaR must be able to encompass the tail events’ prediction and make this its primary goal (Chiang, et al., 2007; Engle, 2002; Engle & Kroner, 1995; Engle & Rothschild, 1990; Francis, et al., 2001). There have been statistical techniques as well as thumb rules that many researchers argue as having been very instrumental in the prediction and analysis of intra-day and in most cases day-to-day risk. These are however; not appropriate for the analysis of VaR. The predictions of VaR now fall under parametric predictions that encompass conditional volatilities and non-parametric prediction that incorporate the unconditional volatilities (Jorion, 2006; Jorion, 2007).
Inhaltsverzeichnis (Table of Contents)
- Introduction
- Background to the Data Sample
- Analytic VAR
- Monte Carlo VAR
- Historical Analysis/Bootstrap VAR
- Discussion
- Conclusion
- References
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This report analyzes the 260-day Value at Risk (VaR) of a portfolio consisting of four shares: Sage Group PLC, Xtrata PLC, Royal Dutch Shell PLC, and Severn Trent PLC. The objective is to assess the portfolio's risk using various VaR calculation methods – analytic VaR, Monte Carlo VaR, and historical analysis/bootstrap VaR – and compare their results. The analysis considers the advantages and disadvantages of each method. The confidence level varies between 95% and 99% to analyze the market's risk surface.
- Value at Risk (VaR) calculation and its challenges
- Comparison of different VaR estimation methods (analytic, Monte Carlo, historical/bootstrap)
- Analysis of a portfolio of four diverse shares
- Impact of sample size and data characteristics on VaR estimation
- Assessment of portfolio risk at different confidence levels
Zusammenfassung der Kapitel (Chapter Summaries)
Introduction: This chapter introduces the concept of Value at Risk (VaR) as a crucial tool for assessing financial risk, highlighting the challenges associated with its implementation, particularly in accurately predicting extreme events. It establishes the context of the report, which focuses on analyzing the VaR of a four-share portfolio over a 260-day period using analytic, Monte Carlo, and historical/bootstrap methods. The importance of accurate prediction of tail events and the selection of appropriate methods based on accuracy and timing requirements are emphasized. The chapter sets the stage for a detailed examination of VaR estimation techniques in the context of the selected portfolio.
Background to the Data Sample: This section provides detailed descriptions of the four companies included in the portfolio: Sage Group PLC (IT), Xtrata PLC (mining), Royal Dutch Shell PLC (energy), and Severn Trent PLC (utilities). It outlines their business activities, market listings, financial performance, and global operations, offering context for the subsequent VaR analysis. The chapter also explains the data selection process, emphasizing the use of pre-sampling to avoid correlation issues and ensure the reliability of the results. The rationale behind choosing a 260-day sample period, the currency used, and the properties of the return data are explained. The chapter’s main contribution lies in establishing the context and justification for the data utilized in the subsequent VaR calculations.
Schlüsselwörter (Keywords)
Value at Risk (VaR), portfolio risk, analytic VaR, Monte Carlo VaR, historical simulation, bootstrap VaR, financial risk management, extreme value theory, portfolio optimization, equity returns, risk prediction, confidence levels.
Frequently Asked Questions: Value at Risk (VaR) Analysis of a Four-Share Portfolio
What is the main topic of this report?
This report analyzes the 260-day Value at Risk (VaR) of a portfolio comprising four diverse shares: Sage Group PLC, Xtrata PLC, Royal Dutch Shell PLC, and Severn Trent PLC. It compares three different VaR calculation methods: analytic VaR, Monte Carlo VaR, and historical simulation/bootstrap VaR, assessing their advantages and disadvantages.
What are the objectives of this Value at Risk analysis?
The primary objective is to assess the portfolio's risk using various VaR calculation methods and compare their results. The analysis also explores the impact of sample size, data characteristics, and different confidence levels (95% and 99%) on VaR estimation.
Which companies are included in the portfolio analyzed?
The portfolio consists of shares from four companies representing different sectors: Sage Group PLC (IT), Xtrata PLC (mining), Royal Dutch Shell PLC (energy), and Severn Trent PLC (utilities).
What VaR calculation methods are used in this report?
The report employs three distinct Value at Risk (VaR) calculation methods: analytic VaR, Monte Carlo VaR, and historical simulation/bootstrap VaR. Each method's results are compared and contrasted.
What is the time period considered in the VaR analysis?
The analysis covers a 260-day period for the selected portfolio.
What are the key themes explored in the report?
Key themes include Value at Risk calculation and its challenges, comparison of different VaR estimation methods, analysis of a diverse portfolio, the impact of sample size and data characteristics on VaR estimation, and assessment of portfolio risk at different confidence levels.
What is covered in the "Background to the Data Sample" chapter?
This chapter provides detailed descriptions of each company in the portfolio, outlining their business activities, market listings, and financial performance. It also explains the data selection process, including pre-sampling techniques to mitigate correlation issues and justify the choice of a 260-day sample period.
What are the key takeaways from the "Introduction" chapter?
The introduction establishes the context of Value at Risk (VaR) as a crucial risk assessment tool, highlighting the challenges of accurately predicting extreme events. It emphasizes the importance of choosing appropriate methods based on accuracy and timing requirements.
What are the key words associated with this report?
Key words include Value at Risk (VaR), portfolio risk, analytic VaR, Monte Carlo VaR, historical simulation, bootstrap VaR, financial risk management, extreme value theory, portfolio optimization, equity returns, risk prediction, and confidence levels.
- Quote paper
- Calvin Monroe (Author), 2012, Report on Analysis of the 260-Day Value at Risk (VAR) of Portfolio of Shares, Munich, GRIN Verlag, https://www.grin.com/document/269413
