Publications

This study investigates the impact of currency mismatches on economic growth. We introduce novel indices constructed using a unique dataset on the currency composition of cross-border bank loans and international debt securities. Our empirical findings reveal that currency mismatches significantly hinder economic growth, with depreciation further amplifying this adverse effect. To mitigate these negative effects, policymakers should consider limiting foreign currency-denominated debt issuance and promoting domestic bond market development.

This study examines the effect of global value chains (GVCs) on the association between gross exports and the exchange rate. To do so, we quantify the composition of the GVCs using output-related measures for sixty-one countries from 2007 to 2020. We analyse the importance of GVCs on the link between gross exports and the exchange rate using an econometric technique – the generalised method of moments. Our results show that GVC participation disconnects the exchange rate elasticity of exports. The empirical findings are robust and consistent across the sectors and income groups.

The emerging market economies (EMEs) are experiencing signifcant fnancial distress due to the rapid accumulation of foreign currency-denominated debt in recent years. We develop the foreign exposure indicators such as original sin and currency mismatches using a novel data set. Our computations suggest that Latin American economies sufer from the original sin problem, followed by Central European countries. We fnd a higher degree of currency mismatches in Argentina, Chile, Colombia, Indonesia, Poland, Mexico, and Turkey.

The global financial crisis has sparked an interest in the association between financial crises and currency mismatches. This study investigates the role of external vulnerable indicators in crises prediction. We contribute to the literature by constructing a broader version of original sin that includes currency composition of international debt securities and cross-border banking loans for 165 countries from 1970 to 2018.

This paper examines the ability of econometric and machine learning techniques to predict the financial crises. Our findings show that the traditional econometric models underperform and unable to predict financial crises in out of sample. The machine learning algorithms such as extree, random forest of ensemble methods improves the accuracy. Moreover, prediction results confirm that the importance of external vulnerable indicators play crucial role in predicting financial crises using Shapley values.

We examine a theoretically robust but previously undocumented issue of what drives foreign portfolio investments into emerging markets. Foreign institutional investors (FIIs) are often blamed as fair-weather friends who pull out their investment at the frst sign of trouble.

We develop a currency mismatch index and examine the causes of currency mismatches in emerging market economies. This study is based on a unique dataset on 22 economies from 2008 to 2017. We also construct the original sin index using granular data on international debt securities.

The purpose of this paper is to examine whether the emotions and sentiments related to the outcome of the sporting event influence the investment making process.This study uses the data on stock prices of firms sponsoring the Indian premier league (IPL) teams and data on Indian stock market. The event-study frameworks along with autoregressive moving average and GMM regression are employed to empirical quantify the impacts of the performance of the IPL teams on the stock market returns of the sponsors’ stocks and response of Indian stock market to the outcome of T-20 international matches.The paper finds that the team winning IPL title in a season has a positive impact on the returns of the sponsors’ stocks of a particular team, whereas loss of team has a negative impact on returns.