Basabi Bhattacharya

Foreign Direct Investment and Macroeconomic Indicators in India: A Causality Analysis

The objective of the present study is to analyse the pattern of movement of external capital flows to Indian economy in terms of foreign direct investment (FDI) along with other macroeconomic indicators, namely, real GDP, US dollar exchange rate, inflation, T-bill rate, trade openness, and Dow Jones Index value, and their probable impact on the financial and overall performance of the economy. Given that the current study period is from 1997–98 to 2013–14, and the tremendous growth in terms of external capital inflows in India during that time simultaneously with near double digit economic growth, the focus of the chapter is primarily to unravel the linkage between the FDI inflows and the selected macroeconomic fundamentals with the help of quarterly time series data using cointegration and causality analysis.

Understanding the Interrelationship Between Commodity and Stock Indices Daily Movement Using ACE and Recurrence Analysis

The relationship between the temporal evolution of the commodity market and the stock market has long term implications for policy makers, and particularly in the case of emerging markets, the economy as a whole. We analyze the complex dynamics of the daily variation of two indices of stock and commodity exchange respectively of India. To understand whether there is any difference between emerging markets and developed markets in terms of a dynamic correlation between the two market indices, we also examine the complex dynamics of stock and commodity indices of the US market. We compare the daily variation of the commodity and stock prices in the two countries separately. For this purpose we have considered commodity India along with Dow Jones Industrial Average (DJIA) and Dow Jones-AIG Commodity (DJ-AIGCI) indices for stock and commodities, USA, from June 2005 to August 2008. To analyse the dynamics of the time variation of the indices we use a set of analytical methods based on recurrence plots. Our studies show that the dynamics of the Indian stock and commodity exchanges have a lagged correlation while those of US market have a lead correlation and a weaker correlation.

Testing Non-linearity in Emerging and Developed Markets

Behaviour of non-linearity in stock markets of developed countries has been empirically evidenced for quite some time. Some few studies examine non-linearity of emerging stock markets as well. A comprehensive overview of stock market behaviour in terms of non-linearity is undertaken in this study where several non-linearity test approaches have been applied on both the developed and emerging country groups. For the purpose we have selected seven stock exchanges from developed markets (UK, Germany, Australia, New Zealand, Hong Kong, France and Japan) and seven from emerging markets (Hungary, Chile, Mexico, Malaysia, Taiwan, China and India). Taking their daily close data covering a period from January 2005 to July 2011, we have investigated the presence of nonlinearity in the data sets and tried to find out whether there is any difference amongst them in this regard. We have first used the test method developed by Brock, Dechert and Scheinkman (BDS) and examine the non-linearity feature in each of the time series. To reinforce our findings, we have conducted the White’s Neural Network tests on the same data set. Additionally, we have also performed the Keenan’s test for non-linearity. Another popular non-linear test is the Hinich bi-spectrum test, which involves estimating the bi-spectrum of the observed time series. We have further used this test to find out whether it detects non-linearity in these time series. Another linearity test for time series was introduced based on concepts from the theory of neural networks. Terasvirta et al. developed its power fully. We have applied this Terasvirta Neural Network test as a final reinforcement of our findings.

Comparative Analysis of Asset Pricing Models Based on Log-Normal Distribution and Tsallis Distribution using Recurrence Plot in an Emerging Market

Abstract It has long been challenged that the distributions of empirical returns do not follow the log-normal distribution upon which many celebrated results of finance are based including the Black–Scholes Option-Pricing model. Borland (2002) succeeds in obtaining alternate closed form solutions for European options based on Tsallis distribution, which allow for statistical feedback as a model of the underlying stock returns. Motivated by this, we simulate two distinct time series based on initial data from NIFTY daily close values, one based on the Gaussian return distribution and the other on non-Gaussian distribution. Using techniques of non-linear dynamics, we examine the underlying dynamic characteristics of both the simulated time series and compare them with the characteristics of actual data. Our findings give a definite edge to the non-Gaussian model over the Gaussian one.

Indicators of Banking Fragility in India: An Empirical Test

The study presents an early warning system for predicting banking fragility in India. Using the index method, distress episodes in the banking system are identified during 1994–2007. On the basis of standard tools of probit regression models, the results indicate growing interlinkages of economic liberalization with the Indian banking sector. Slowdown in real output, increase in headline inflation rate, increase in spread between the central bank policy rate and short-term risk-free rate, increase in proportion of broad money supply to foreign exchange reserves, REER overvaluation from trend and decrease in proportion of trade balance to GDP enhance the probability of banking fragility in India. The behaviour of the identified crucial indicators, cross-checked by the signal extraction approach, reveals adequate signalling power due to their low Noise-to-Signal Ratio. The estimated ‘lead time’ of the indicator variables in signalling banking distress also offers a modest time period to the government to initiate pre-emptive policy action to strengthen the banks.