This book examines the long memory characteristics in the volatility of the Indian stock market, the Indian exchange rates and the Indian banking sector. This book also reviews the chain of approaches to estimate the long memory parameter.
The long memory characteristics of the financial time series are widely studied and have implications for various economics and finance theories. The most important financial implication is related to the violation of the weak-form of market efficiency which encourages the traders, investors and portfolio managers to develop models for making predictions and to construct and implement speculative trading and investment strategies. In an efficient market, the price of an asset should follow a random walk process in which the price change is unaffected by its lagged price changes and has no memory.
Inhaltsverzeichnis (Table of Contents)
- Chapter 1: Introduction
- 1.1 Volatility and long memory
- 1.2 Structure of the book
- Chapter 2: Literature Review
- 2.1 Long range dependence in the financial time series
- Chapter 3: Long memory tests
- 3.1 Long memory in a financial time series
- 3.2 R/S analysis
- 3.3 Modified R/S analysis (R/S-AL)
- 3.4 Detrending moving average analysis (DMA)
- 3.5 Generalized Hurst exponent
- 3.6 Lo's modified R/S analysis
- 3.7 Detrended fluctuation analysis (DFA)
- 3.8 Local Whittle method
- 3.9 Exact Local Whittle test
- 3.10 Discrete wavelet transform
- 3.11 Aggregated variance method
- 3.12 Geweke and Porter-Hudak (GPH) (1983) test
- 3.13 The autoregressive fractionally integrated moving average (ARFIMA) model
- 3.14 The fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) model
- 3.15 The fractionally integrated exponential generalized autoregressive conditional heteroskedasticity (FIEGARCH) model
- 3.16 The fractionally integrated asymmetric power autoregressive conditional heteroskedasticity (FIAPARCH) model
- Chapter 4: Long memory in the volatility of the Indian stock market
- 4.1 Abstract
- 4.2 Data description and computational details
- 4.3 Empirical results
- 4.3.1 Evidence from semi-parametric long memory test
- 4.3.2 Evidence from the FIGARCH model
- 4.4 Conclusion
- Chapter 5: Long memory in the volatility of the Indian exchange rates
- 5.1 Abstract
- 5.2 Monte Carlo experiment
- 5.3 Data description and computational details
- 5.4 Empirical Results
- 5.4.1 Evidence from aggregated variance method
- 5.4.2 Evidence from semi-parametric (Geweke and Porter-Hudak (GPH) (1983)) long memory test
- 5.5 Conclusion
- Chapter 6: Asymmetry and long memory in the volatility of the Indian banking sector
- 6.1 Abstract
- 6.2 Data and computational details
- 6.3 Empirical results
- 6.3.1 Long memory in absolute daily returns and squared daily returns
- 6.3.2 Results of GARCH family models across the periods
- 6.3.3 Impact of sub-prime crisis on the volatility of the CNX Bank Nifty index
- 6.3.4 Asymmetric long memory characteristics in volatility
- 6.4 Conclusion
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This book analyzes the long memory properties in the volatility of the Indian financial market using a variety of empirical methods. The study investigates long memory in the volatility of different financial assets, including the Indian stock market, exchange rates, and banking sector. The primary aim is to contribute to the understanding of the dynamics of these markets. Key themes explored in this work include:- Long memory in financial time series
- Volatility and its persistence in the Indian financial markets
- Application of various long memory tests and models
- Empirical evidence for long memory in the volatility of different asset classes
- Asymmetric long memory characteristics in volatility
Zusammenfassung der Kapitel (Chapter Summaries)
Chapter 1 provides an introduction to the concept of long memory and its significance in the context of financial markets. It also outlines the structure of the book, highlighting the key chapters and their respective objectives.
Chapter 2 presents a comprehensive literature review of the long range dependence phenomenon in financial time series. It discusses various studies and theories that have explored the presence and implications of long memory in financial markets.
Chapter 3 delves into the different methods and tests used for detecting long memory in financial time series. This chapter provides a detailed description of various techniques, including the R/S analysis, Detrended Fluctuation Analysis, and the Local Whittle method, among others. It also explores the use of models like ARFIMA, FIGARCH, and FIEGARCH to capture the long memory property in volatility.
Chapter 4 investigates the presence of long memory in the volatility of the Indian stock market. It analyzes data from various stock indices and applies both semi-parametric and parametric tests to identify long memory. This chapter discusses the empirical findings and their implications for understanding the dynamics of the Indian stock market.
Chapter 5 focuses on long memory in the volatility of Indian exchange rates. It describes the data used and the computational details involved. The chapter utilizes both aggregated variance methods and semi-parametric tests to analyze the long memory property in exchange rate volatility. It also includes a Monte Carlo experiment to test the robustness of the findings.
Chapter 6 explores the relationship between asymmetry and long memory in the volatility of the Indian banking sector. This chapter analyzes data from the CNX Bank Nifty index and investigates the impact of the sub-prime crisis on the volatility of the banking sector. It also examines the asymmetric long memory characteristics of volatility and discusses their implications for understanding the risk dynamics of the banking sector.
Schlüsselwörter (Keywords)
The book focuses on the long memory properties of financial time series, particularly in the Indian financial markets. It explores various long memory tests and models, including semi-parametric and parametric techniques. The primary focus is on analyzing the volatility of different asset classes such as stocks, exchange rates, and the banking sector. Key concepts examined include long range dependence, volatility persistence, and asymmetric long memory characteristics. The study contributes to the understanding of the dynamics of Indian financial markets and the implications of long memory for risk management and investment decisions.- Quote paper
- Dilip Kumar (Author), 2014, Long Memory in the Volatility of Indian Financial Market, Munich, GRIN Verlag, https://www.grin.com/document/269636
