We examine volatility of an Indian stock market in terms of aspects like participation, synchronization of stocks and quantification of volatility using the random matrix approach. Volatility pattern of the market is found using the BSE index for the three-year period 2000-2002. Random matrix analysis is carried out using daily returns of 70 stocks for several time windows of 85 days in 2001 to (i) do a brief comparative analysis with statistics of eigenvalues and eigenvectors of the matrix C of correlations between price fluctuations, in time regimes of different volatilities. While a bulk of eigenvalues falls within RMT bounds in all the time periods, we see that the largest (deviating) eigenvalue correlates well with the volatility of the index, the corresponding eigenvector clearly shows a shift in the distribution of its components from volatile to less volatile periods and verifies the qualitative association between participation and volatility (ii) observe that the Inverse participation ratio for the last eigenvector is sensitive to market fluctuations (the two quantities are observed to anti correlate significantly) (iii) set up a variability index, V whose temporal evolution is found to be significantly correlated with the volatility of the overall market index.

This paper amends the New Keynesian Phillips curve model to include inflation volatility. It provides results on the determinants of inflation volatility and expected inflation volatility for OLS and ARDL (1,1) models and for change in inflation volatility and change in expected inflation volatility using ECM models. Output gap affects change in expected inflation volatility alone (in the ECM model) and not in the other models. Major determinants of inflation volatility and expected inflation volatility are identified. To the best of our knowledge this is the first paper to augment the New Keynesian Phillips Curve to include inflation volatility.

So far most of the literature on poverty and inequality has focused on how overall inequality in income distribution (frequently measured by the Gini coefficient) undermines the 'trickle down' effect. In other words, the higher the inequality in the income distribution, the lower the growth elasticity of poverty. However, with the publication of Piketty's magnum opus in 2014, and a subsequent 2017 study by Chancel and Piketty of evolution of income inequality in India since 1922, the focus has shifted to the income disparity between the richest 1% (or 0.01 %) and the bottom 50%. Their central argument is that the rapid growth of income at the top end of millionaires and billionaires is a byproduct of growth. This study extends this argument by linking it to poverty indices in India. Using the India Human Development Survey 2005-12a nationwide panel surveywe examine the links between poverty and income inequality, especially in the upper tail relative to the bottom 50%, state affluence (measured in per capita income) and their interaction or their joint effect. We also analyse their effects on the FGT class of poverty indices. The results are similar in as much as direction of causality is concerned but the elasticities vary with the poverty index. The growth elasticities are negative and significant for all poverty indices. In all three cases, the disparity between the income share of the top 1% and the share of the bottom 50% is associated with greater poverty. These elasticities are much higher than the (absolute) income elasticities, except in the case of the poverty gap. The largest increase occurs in the poverty gap squared: a 1% greater income disparity is associated with a 1.24% higher value of this index. Thus the consequences of even a small increase in income disparity are alarming for the poorest.

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