An Analysis of India’s Trade Flow with BIMSTEC nations- A Gravity Model Approach

At present, the Bay of Bengal Initiative for Multi Sectoral Technical and Economic Cooperation (BIMSTEC) represent 22 percent of the global population and carries immense economic promise, with a combined GDP worth $ 3.5 trillion (2018). BIMSTEC have India as the largest economy of the group followed by Bangladesh, Thailand, Sri Lanka, Nepal, Myanmar and Bhutan. The paper tries to examine the impact of determinants of trade, on trading pattern of India with the BIMSTEC nations, by employing an augmented gravity model on panel data, since its formation for the period of 22 years i.e., from 1997-2018. The paper tries to examine the India’s trade flow within BIMSTEC trading bloc by implying augmented gravity model followed by Egger (2000,2002), Baltagi et al. (2003) and Serlenga and Shin (2007). Several checks have been performed to imply the presence of serial correlation, heteroscedasticity and contemporaneous correlation in the panels. Many other preliminary tests have also been performed to know the crosssectional dependency, stationarity, panel co-integration and normality of variables. The simple panel OLS estimation technique has been used conduct Regression. The study finds out that Heckscher-Ohlin- Samuelson theorem explain the India’s pattern of trade with the bloc. The variables GDP, per capita GDP, Trade GDP ratio, common border and belonging to BIMSTEC has positive impact on the trade between the India and country j. While tax and distance are negatively correlated with total trade of the nations as per our expectations.

Constructing a Coincident Economic Indicator for India: How Well Does It Track Gross Domestic Product?

In India, the first official estimate of quarterly gross domestic product (GDP) is released approximately 7–8 weeks after the end of the reference quarter. To provide an early estimate of current quarter GDP growth, we construct Coincident Economic Indicators for India (CEIIs) using a sequentially expanding list of 6, 9, and 12 high-frequency indicators. These indicators represent various sectors, display high contemporaneous correlation with GDP, and track GDP turning points well. CEII-6 includes domestic economic activity indicators, while CEII-9 incorporates indicators of trade and services and CEII-12 adds financial indicators in the model. We include a financial block in CEII-12 to reflect the growing influence of the financial sector on economic activity. CEIIs are estimated using a dynamic factor model which extracts a common trend underlying the high-frequency indicators. The extracted trend provides a real-time assessment of the state of the economy and helps identify sectors contributing to economic fluctuations. Furthermore, GDP nowcasts using CEIIs show considerable gains in both in-sample and out-of-sample accuracy. In particular, we observe that our GDP growth nowcast closely tracks the recent slowdown in the Indian economy.

The short-term mean reversion of stock price and the change in trading volume

This study aims to analyze the effect of change in trading volume on the short-term mean reversion of the stock price in the Korean stock market. Through the variance ratio test, this paper finds that the market shows the mean reversion pattern after 2000, but not before. This study also confirms that the mean reversion property is significantly reduced if the effect of change in trading volume is excluded from the return of a stock with a significant contemporaneous correlation between return and change in trading volume in the post-2000 market. The results appear in both the Korea Composite Stock Price Index and Korea Securities Dealers Automated Quotation. This phenomenon stems from the significance of the return response to change in trading volume per se and not the sign of the response. Additionally, the findings imply that the trading volume has a term structure because of the mean reversion of the trading volume and the return also has a partial term structure because of the contemporaneous correlation between return and change in trading volume. This conclusion suggests that considering the short-term impact of change in trading volume enables a more efficient observation of the market and avoidance of asset misallocation.

Does tourism promote economic growth? A fractionally integrated heterogeneous panel data analysis

In this article, we analyse whether tourism promotes economic growth using a general dynamic panel data model that incorporates individual and interactive fixed effects and allows for contemporaneous correlation in model innovations. The empirical study is based on quarterly series of GDP and tourist arrivals for 14 European countries covering the period from 1995 to 2019. Results indicate that the case for a positive long-run relationship between tourism and economic growth is rather weak, being slightly stronger for the period prior to the global economic and financial crisis from 2007 to 2010. When applying panel fractional cointegration techniques, we find evidence in favour of the tourism-led growth hypothesis (TLGH) for the full sample mainly for North European countries. For the pre-crisis period, on the other hand, we find evidence in favour of the TLGH for the relevant tourist destinations Spain and France.

On Seemingly Unrelated Regression and Single Equation Estimators Under Heteroscedastic Error and Non-Gaussian Responses

This study investigated the efficiency of Seemingly Unrelated Regression (SUR) estimator of Feasible Generalized Least Square (FGLS) compared to robust MM-BISQ, M-Huber, and Ordinary Least Squares (OLS) estimators when the variances of the error terms are non-constant and the distribution of the response variables is not Gaussian. The finite properties and relative performance of these other estimators to OLS were examined under four forms of heteroscedasticity of the error terms, levels of Contemporaneous Correlation (Cc) with gamma responses. The efficiency of four estimation techniques for the SUR model was examined using the Root Mean Square Error (RMSE) criterion to determine the best estimator(s) under different conditions at various sample sizes. The simulation results revealed that the SUR estimator (FGLS) showed superior performance in the small sample situations when the contemporaneous correlation ( ) is almost perfect ( =0.95) with the gamma response model while MM-BISQ was the best under low contemporaneous correlation. The relative efficiencies of MM-BISQ, M-Huber and FGLS estimators over the OLS are respectively 89%, 71%, and 14% in a small sample 30) and 49%, 32% and 1% in large sample sizes under gamma response model. The study concluded that MM-BISQ and M-Huber estimators are the most efficient estimators for modeling systems of simultaneous equations with non-Gaussian responses under either homoscedastic or multiplicative heteroscedastic error terms irrespective of the sample size.Keywords—, Contemporaneous correlation, Feasible Generalized Least Square, Heteroscedasticity, Homoscedasticity, Seemingly unrelated Regression.

Testing for a common Phillips curve in common monetary area of Southern Africa

PurposeThis paper sets out to investigate whether the four members of the common monetary area (CMA) regime experience similar inflation-unemployment dynamics as explained by the Phillips Curve phenomenon.Design/methodology/approachThis study uses a combination of seemingly unrelated regression (SUR) and Copula based marginal regression techniques to investigate existence of a common Phillips curve (PC) between members of the CMA. Model estimation was done using country specific annual time series data for inflation, unemployment and imports spanning from 1980 to 2014.FindingsWe find evidence of contemporaneous correlation between the residuals of individual CMA PC equations and a statistically significant trade-off between inflation and unemployment for all CMA countries. Wald test results of cross-equation restrictions reveal a 9.94% chance of a common unemployment coefficient for CMA countries.Originality/valueTogether, the results of the SUR and Gaussian Copula techniques provide mixed and inconclusive evidence to support the existence of a common PC among CMA member states. This study is the first of its kind in examining this phenomenon for currency board regimes like CMA, and one of the very few among emerging market economies.

Price Discovery in a Continuous-Time Setting

We formulate a continuous-time price discovery model and investigate how the standard price discovery measures vary with respect to the sampling interval. We find that the component share (CS) measure is invariant to the sampling interval, and hence, discrete-sampled prices suffice to identify the continuous-time CS. In contrast, information share (IS) estimates are not comparable across different sampling intervals because the contemporaneous correlation between markets increases in magnitude as the sampling interval grows. We show how to back out the continuous-time IS from discrete-sampled prices under certain assumptions on the contemporaneous correlation. We assess our continuous-time model by comparing the estimates of the (continuous-time) CS and IS at different sampling intervals for 30 stocks in the United States. We find that both price discovery measures are typically stable across the different sampling intervals, suggesting that our continuous-time price discovery model fits the data very well.

Contemporaneous Correlation between the Return and the Change of Trading Volume

This study demonstrates the contemporaneous correlation between return and the change of trading volume (CCRV) in the Korean stock market and analyzes the effect of trading volume change on the return and its volatility of individual stocks. Also, we examine the underlying reasons for CCRV in the Korean stock market. The empirical analysis covers individual stocks listed in KOSPI and KOSDAQ and their portfolios from 1989 to 2015. The main results are as follows. First, the CCRV in the Korean stock market dominantly appears positive. Second, at the individual stock level, the daily return volatility induced by CCRV accounts for 4.22% of the total daily return volatility. Third, the return volatility induced by CCRV is largely offset by well-diversified portfolios. Lastly, the ratio of positive CCRV decreases in stocks with very high or very low liquidity. The above result suggests that the illiquidity premium hypothesis is appropriate in explaining CCRV in the Korean stock market. In addition, the above results also indicate that the changes of trading volume can act as an idiosyncratic risk factor that can additionally intensify or weaken the response of the return toward the news. Furthermore, these results suggest that a strategy with sufficient consideration for CCRV is essential for the stock price prediction, the valuation of derivatives, and portfolio management.