Long Memory in the Volatility of Indian Financial Market: An Empirical Analysis Based on Indian Data

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 ist lagged price changes and has no memory.

Autorentext

Dilip Kumar works in the area of asset pricing. His areas of interest includes Long memory in financial markets, Market efficiency, Extreme value volatility estimator, Bias correction in extreme value volatility estimators.

Leseprobe
Chapter 2.1., Long range dependence in the financial time series:
The study of long-range dependence in financial time series has a long history and has remained an active topic of research in economics and finance. See, for instance, Mandelbrot (1971), Greene and Fielitz (1977) and Cutland, Kopp, and Willinger (1995). Mandelbrot (1972) finds that the R/S analysis shows superior properties over autocorrelation and variance analysis (because it can work with distributions with infinite variance) and spectral analysis (because it can detect non-periodic cycles). Greene and Fielitz (1977) utilize the Hurst rescaled-range (R/S) method and provide evidence in support of long memory in the daily stock return series. With the development of the log periodogram regression estimator by Geweke and Porter-Hudak (1983), based on the order of integration parameter d in the ARFIMA model of Granger and Joyeux (1980) and Hosking (1981), triggered the literature of the fractionally integrated models. Diebold and Rudebusch (1989) explore the long memory characteristics of the US real GNP data. Lo (1991) find that the classical R/S test used by Mandelbrot and Green and Fielitz suffers from a drawback in that it is unable to distinguish between long memory and short range dependence. Lo (1991) proposes a modified test of the R/S statistic which can distinguish between short term dependence and long memory and finds that daily stock returns do not show long-range dependence properties. Cheung and Lai (1995) analyze data from Austria, Italy, Japan and Spain and detect long memory in these markets. In addition, this finding was invariant to the choice of estimation methods employed. In particular, results from both the modified rescaled range and the spectral regression method, which was used to model an ARFIMA process indicated the presence of long memory dynamics in the data. Willinger, Taqqu, and Teverovsky (1999) empirically find that Lo s modified R/S test leading to the acceptance of the null hypothesis of no long-range dependence for CSRP (Center for Research in Security Prices) data is less conclusive than it appears. This is so because of the conservative nature of the test statistic in rejecting the null hypothesis of no long-range dependence, by attributing what is found in the data to short-term dependence instead. Peters (1991) use R/S approach to study the long memory characteristics of daily exchange rates data of US dollars, Japanese yen, British pounds, Euros and Singapore dollars, and finds evidence that support the presence of long memory properties in exchange rates. Baillie, Chung, and Tieslau (1996) investigate the long-range dependence properties in inflation time series and find positive results. Corazza and Malliaris (2002) carry out a study on foreign currency markets and find evidence of long memory. They also find that Hurst exponent does not remain fixed but changes dynamically with time. In addition, they provide evidence that foreign currency returns follow either a fractional Brownian motion or a Pareto-Levy stable distribution. Cajueiro and Tabak (2004) use the rolling sample approach to calculate Hurst exponents over the period October 1992 to October 1996 and provide evidence of long-range dependence in Asian markets. Carbone, Castelli, and Stanley (2004) propose the detrending moving average (DMA) algorithm to estimate the Hurst exponent, which does not require any assumption regarding the underlying stochastic process or the probability distribution function of the random variable. Matteo, Aste, and Dacorogna (2005) study the scaling properties of daily foreign exchange rates, stock market indices and fixed income instruments by using the generalized Hurst exponent approach and find that the scaling exponents can be used to differentiate markets in their stage of development. Cajueiro and Tabak (2005) study the possible sources of long-range dependence in returns of Brazilian stocks and find that firm specific