Academic journal article IUP Journal of Applied Economics

Nonlinear Dependence and Conditional Volatility in the Indian Rupee Exchange Rate

Academic journal article IUP Journal of Applied Economics

Nonlinear Dependence and Conditional Volatility in the Indian Rupee Exchange Rate

Article excerpt

(ProQuest: ... denotes formulae omitted.)

Introduction

In the literature on asset return, many studies have employed traditional statistical tests such as autocorrelation tests, and tested the Efficient Market Hypothesis (EMH) mainly focusing on the linear predictability of future asset price changes. If the later turn out to be uncorrelated then the EMH is accepted and the asset market in question is deemed to be informationally efficient, and if they are found to be serially correlated, the EMH is rejected and the market is considered inefficient. However, researchers like Brock et al. (1991 and 1992) point out that lack of linear dependence does not rule out nonlinear dependence, which if present, would contradict the random walk model. Evidence of this possibility is provided by Granger and Andersen (1978) and Sakai and Tokumaru (1980), who demonstrate that nonlinear models may exhibit no serial correlation, while containing strong nonlinear dependence. In fact, as Campbell et al. (1997) argue many aspects of economic behavior may not be linear, and may cause rejection of independent and identical distribution (iid). There may be several reasons behind the nonlinear behavior of financial markets. First, market imperfections and some features of market microstructure may lead to delays of response to new information, implying nonlinearity in asset price changes. Schatzberg and Reiber (1992) suggest that asset prices do not always adjust instantaneously to new information. For instance, transaction costs may make investors unwilling to respond rapidly to the arrival of new information. In turn, they would rather wait until their expected excess profits (net of transaction cost) are high enough to allow for positive returns. This delay in adjustment may lead to nonlinearity in asset price changes. Further as Shleifer and Summers (1990) argue, there are two types of investors in the market: rational arbitrageurs or speculators, who trade on the basis of reliable information, and noise traders, who trade on the basis of imperfect information. Because of the informational asymmetries and lack of reliable information, noise traders may lean towards delaying their responses to new information in order to assess informed traders' reaction, and then respond accordingly. Based on the above line of thinking, one may assume that economic systems may be nonlinear. The presence of nonlinear dependence may have short term, if not long term forecasting potential, provided the actual generating mechanism is known. This paper attempts to test whether the rupee exchange rates present nonlinear behavior and if so, what is its nature.

The paper is organized as follows: it presents a brief review of the related literature explaining the motive of the study. Subsequently, it discusses the data and methodology used in the paper, followed by a discussion of the findings, and finally, the conclusion is offered.

Literature Review

Many researchers have reported that the presence of nonlinear characteristics usually takes the form of ARCH/GARCH (Autoregressive Conditional Heteroscedasticity or Generalized Autoregressive Conditional Heteroscedasticity) type conditional heteroscedasticity for stock markets. Hsieh (1991), Al-Loughani and Chappell (1997) and Opong et al. (1999) have demonstrated the same.

In the case of foreign exchange markets too, the most popular specification to model volatility is the GARCH-type model due to Bollerslev (1986). Many empirical studies have been done in recent years to investigate the characteristics of exchange rate volatility in the context of time series analysis of financial returns such as leverage effect and volatility clustering and persistence. For example, Friedman and Stoddard (1982), Meese and Rogoff (1983), Milhoj (1987), Hsieh (1989), Lastrapes (1989), Bollerslev (1990), McKenzie (1997), Tse and Tsui (1997), Brooks and Burke (1998), Longmore and Robinson (2004), Yoon and Lee (2008), Hamadu and Adeleke (2009), and Fiser and Roman (2010) find evidence of volatility clustering and persistence which mean that large and small values in the log returns tend to occur in clusters and come to conclusion that GARCH models and their many extensions were successful in modeling and forecasting exchange rate volatility. …

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