Indian economy has been facing the twin issues of mounting trade imbalance and persisting inflation. Oil constitutes one-third of the country's total imports and is considered to have wideranging impact on its economy. This paper empirically examines how oil price fluctuations impact Indian economy through various channels, viz. real sector, monetary policy, external trade, exchange rate and investment. The results of cyclical correlation analysis suggest that oil is procyclical to output, price level, stock market, gold, interest rate and foreign exchange reserves, while it is counter-cyclical to money supply, net exports and exchange rate. Also, it is found that oil Granger causes output, general price level and net exports. The study employs vector autoregression (VAR) analysis and examines variance decomposition to capture the linear interdependencies among the variables. The structural stability tests demonstrate that there is no evidence of structural break in the VAR model, confirming the reliability of estimated relationships under the VAR model.
In this paper, we examine the stock market integration process amongst 17 Economic and Monetary Union (EMU) countries from January 2002 to June 2013 over a normal period as well as for the Global Financial Crisis (GFC) and Eurozone Debt Crisis (EDC) periods. We classify the economies in three groups (A, B and C) based on their GDP to examine whether the economic size influences financial integration. Seven indicators are used for the purpose, namely, beta convergence, sigma convergence, variance ratio, asymmetric DCC, dynamic cointegration, market synchronisation measure and common components approach. The results suggest that large-sized EMU economies (termed as Group A) exhibit strong stock market integration. Moderate integration is observed for middle-sized EMU economies with old membership (termed as Group B). Small-sized economies (termed as Group C) economies seemed to be least integrated within the EMU stock market system. The findings further suggest presence of contagion effects as one moves from normal to crisis periods, which are specifically stronger for more integrated economies of Group A. We recommend institutional, regulatory and other policy reforms for Group B and especially Group C to achieve higher level of integration.
This paper investigates the integration process within the European Economic and Monetary Union's retail banking industry by analyzing deposit and lending rates to nonfinancial corporations. The investigation covers the 2003~2014 period, examining the normal period, the global crisis period, and the European debt crisis period. The paper classifies sampled countries into three groups on the basis of their Gross Domestic Product to investigate the relationship between economic size and degree of integration. We employ five different indicators to assess various dimensions of integration: beta convergence, sigma convergence, variance ratio, asymmetric dynamic conditional correlation, and dynamic co-integration. The results point toward a weak degree of integration, which was worsened by the twin crises. In addition, results indicate that more heterogeneity exists in the credit market than in the deposits market. Furthermore,
Financial markets are inherently unpredictable. They continue to change based on the performance of the company, past records, market value and are also dependent on news & timings. By carrying out trend analysis, one has the ability to prejudge stock prices. Machine Learning Techniques that are available, have the potential to forecast future stock prices. Each stock represents a different trend, so a singular machine learning model can’t be applicable to other stocks. Thus, one model giving a high degree of precision can’t guarantee working on another. Too many variables are involved while predicting stocks-physical factors vs. psychological, irrational and rational behaviour, etc. All of these factors combined indicate stock prices as capricious and difficult to foresee. In order to resolve this challenge, a comprehensive study with historical data on stock prices of listed firms was performed. The main premise behind this research was to illustrate how to apply machine learning algorithms such as Averaging, Linear Regression including advanced deep learning techniques such as Long-Term Short Memory and applying technical tools like the Modern Portfolio Theory and Bollinger bands.
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