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.
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
Objectives and Topics
The primary objective of this work is to empirically investigate the existence of long memory properties in the volatility of the Indian financial market, specifically covering the stock market, exchange rates, and the banking sector.
- Analysis of long memory characteristics in financial time series.
- Evaluation of various semi-parametric and parametric estimation techniques for long memory.
- Examination of volatility dynamics using GARCH-class models (e.g., FIGARCH, FIAPARCH).
- Assessment of the impact of structural changes and crises on market volatility and efficiency.
Excerpt from the Book
1.1 Volatility and long memory
Volatility is considered to be an important ingredient of quantitative finance and a plethora of literature exist related to estimating, modeling and forecasting volatility. If we want to estimate daily volatility using daily closing prices, the most widely used estimators are the demeaned squared daily returns and the demeaned absolute daily returns. But these estimates of volatility are very noisy, inefficient and biased in nature. Another way of estimating volatility more precisely is to use intraday high frequency data. However, in many cases, high frequency data is not available at all or sometime it is available only over smaller intervals. High frequency data is generally very expensive and requires considerable computational resources for analysis. High frequency data also suffers from market microstructure issues which makes volatility estimation using high frequency data highly complex. Several studies have highlighted the importance of volatility estimators that utilize the opening, high, low and closing prices of an asset because they give rise to much more efficient estimates of volatility compared to volatility estimated using conventional return data. The opening, high, low and closing prices are also readily available for most of the tradable assets and indices in financial markets and potentially contain more information for estimating volatility when compared to the close to close return data that is conventionally made use of.
Summary of Chapters
Chapter 1: Introduction: This chapter highlights the importance of volatility in finance and provides background information regarding market efficiency and long memory properties.
Chapter 2: Literature Review: This section covers the development of research concerning long-range dependence in financial time series and the estimation of associated parameters.
Chapter 3: Long memory tests: This chapter details various popular approaches to estimate the long memory parameter in both time and frequency domains.
Chapter 4: Long memory in the volatility of the Indian stock market: This chapter examines long memory characteristics in the Indian stock market using both semi-parametric and parametric methods.
Chapter 5: Long memory in the volatility of the Indian exchange rates: This chapter analyzes long memory in exchange rates against major currencies and validates the estimation methods using Monte Carlo simulations.
Chapter 6: Asymmetry and long memory in the volatility of the Indian banking sector: This chapter investigates asymmetry and long memory in the banking sector, specifically the CNX Bank Nifty index, considering the impact of the sub-prime crisis.
Keywords
Long memory, Volatility, Indian Financial Market, Stock Market, Exchange Rates, Indian Banking Sector, Hurst Exponent, GARCH, FIGARCH, FIAPARCH, Detrended Fluctuation Analysis, Local Whittle, Market Efficiency, Asymmetry, Financial Crises.
Frequently Asked Questions
What is the core focus of this research?
The research focuses on analyzing the long memory properties of volatility within the Indian financial markets, specifically examining how volatility behaves in stock indices, exchange rates, and banking sector indices.
What are the central thematic areas?
The central themes include financial time series analysis, long-range dependence, volatility modeling, market efficiency, and the impact of economic crises on volatility dynamics.
What is the primary research goal?
The primary goal is to empirically verify the existence of long memory in volatility proxies and to determine which econometric models (such as GARCH, FIGARCH, or FIAPARCH) best capture these characteristics in the Indian market context.
Which scientific methods are employed?
The study utilizes a wide range of semi-parametric methods, including R/S analysis, DFA, and the Local Whittle method, alongside parametric GARCH-family models to estimate the Hurst exponent and fractional integration parameters.
What does the main body cover?
The main body systematically explores long memory in three distinct pillars: the broader Indian stock market, exchange rates against major global currencies, and the specific dynamics of the Indian banking sector under crisis conditions.
Which keywords characterize this work?
Key terms include long memory, volatility, Indian Financial Market, Hurst exponent, and various GARCH-class models used for estimation and forecasting.
How does the sub-prime crisis affect the findings for the banking sector?
The study finds that the sub-prime crisis led to a reduction in asymmetric volatility and that market behavior changed across different periods, with specific models like ARMA-GJR-GARCH showing better performance in capturing these dynamics.
Why are GARCH-class models compared in the study?
Different GARCH variants are compared to identify which models are best suited to handle features like leptokurtosis, asymmetry, and long memory, providing more precise forecasts than standard volatility estimators.
- Quote paper
- Dilip Kumar (Author), 2014, Long Memory in the Volatility of Indian Financial Market, Munich, GRIN Verlag, https://www.grin.com/document/269636
