Central Bank Communications and Professional Forecasts: Evidence From India

We analyze the influence of qualitative and quantitative communications of the Reserve Bank of India (RBI) on inflation expectations of professional forecasters and draw out implications for policy. Estimating Carroll-type epidemiological models of expectation formation under information rigidities, we get a large speed of adjustment of professional forecasters’ expectations. Analysis of the determinants of inflation forecasts, inflation surprises, and forecaster disagreement reveals significant influence of quantitative RBI communications in the form of inflation projections. This effect is prominent for shorter-horizon forecasts and after adoption of flexible inflation targeting. Macroeconomic fundamentals like lagged inflation and repo rate also significantly influence inflation forecasts. Choice of words in the RBI monetary policy statements has more impact after October 2016, when the monetary policy committee became the decision-making body. JEL Classification: E31, E52, E58

Competition and Banking Industry Stability: How Do BRICS and G7 Compare?

This study examines banking industry stability in BRICS and G7 from the period 2005 to 2014. The results show that stability level in a prior period affects stability in the subsequent period. Also, the study reveals that competition improves stability, which validates the competition-stability proposition. Economic growth enhances stability in BRICS but not in G7. Inefficiency weakens stability in BRICS; however, its impact in G7 is insignificant. Profitability, capitalization, and inflation enhance stability in G7; however, they show no meaningful impacts in BRICS. These findings contribute to literature and policy discussion on banking industry stability JEL Codes: G21, G28, G32, L11

Nonlinearity in Global Crude Oil Benchmarks: Disentangling the Effect of Time Aggregation

We model the first and second moments of global crude oil benchmarks, using iterative pre-whitened generalized autoregressive conditional heteroskedasticity (GARCH) models and, in doing so, validate the efficacy of such models in assimilating the neglected nonlinearities in the underlying data-generating processes. The benchmarks considered for this study are Brent, Dubai/Oman, and West Texas Intermediate (WTI) crude oil. While nonlinear serial dependence happens to be a stylized fact across different asset classes, it is our view that prior scholarly contributions have not adequately untangled the effect of data aggregation (in time) in the examination of nonlinear dependencies. In this context, the present study strives to untangle the critical role that time aggregation plays in the examination of nonlinearity in global crude oil benchmarks using data at daily, weekly as well as monthly time frequencies. Our findings are as follows: the optimum GARCH models perform well in capturing all of the neglected nonlinearity in monthly returns of the crude benchmarks. When it comes to daily and weekly returns, our study reveals traces of neglected nonlinearities that are not completely captured by GARCH models. Moreover, such residual traces of neglected nonlinear dependencies are relatively more pronounced at the granular levels and become more and more elusory as the data get aggregated in time. JEL codes: C22, C53, C58, G1, Q47

Antecedents of Stage-wise Investment Preferences of Venture Capital and Private Equity Firms in India: An Empirical Exploration

This article is a maiden attempt at exploring determinants of stage-specific investment choices of Indian venture capital and private equity (VCPE) firms. Analysis of 5,782 VCPE investment deals during 1998–2016 shows that firms’ preferences to invest in various stages (early vs. late) are significantly affected by the characteristics of the VCPE firms, features of the deal, and characteristics of the investee firms. More specifically, experience and ownership (foreign vs. domestic) of VCPE firm, type of deal (syndicated or otherwise), investment size of the deal, and location and industry of the investee firm influence the stage of investment. Detailed empirical analysis shows that younger VCPE firms and those with domestic investors prefer to invest in early stages, presumably because they wish to build a reputation and also leverage their proximity with investee firms to manage high market and technological risks associated with early-stage investments. Syndication is another mechanism used to manage the risks associated with early-stage deals. Investee firms in industries that have lower investment requirements or shorter gestation periods and those located in regions with a mature entrepreneurial ecosystems are more likely to attract early-stage investments. JEL Classification: G24, L26, D81

Financial Access of Latin America and Caribbean Firms: What Are the Roles of Institutional, Financial, and Economic Development?

This article examines the impact of institutional, financial, and economic development on firms’ access to finance in Latin America and Caribbean region. Based on firm- and country-level data from the World Bank databases, we employ an ordered logit model to understand the direct and moderating role of institutional, financial, and economic development in determining firms’ financial obstacles. The results show that older, larger, facing less competition and regulation burden, foreign owned, and affiliated firms report lower obstacles to finance. Second, better macro-fundamentals help to lessen the level of obstacles substantially. Third, the role of institutions in promoting firms’ inclusive finance is quite different to the role of financial development and economic growth. JEL classification: E02; G10; O16; P48

The Interplay Between Sentiment and MAX: Evidence from an Emerging Market

Investors with lottery preferences are known to concentrate on stocks with rare but extreme past returns. We investigate the extent to which lottery preference, measured by the MAX variable, varies with the market-wide irrational sentiment. We find that the high-MAX stocks have higher overpricing in a high-sentiment market and earn a lower alpha, compared to the low-sentiment market. Accordingly, the poor returns earned by a long-short portfolio of stocks with extreme MAX values are primarily due to the overvaluation of the high MAX-portfolio during the high sentiment phase. The higher stock volatility in India also magnifies the lottery preference of investors. JEL Classification: G4, G12, G41, G11

Do Investors Overreact for Property and Financial Service Sectors?

There are limitations in the understandings of investors’ overreaction to the volatility in less transparent industrial sectors. Investors investing in a less transparent sector are likely to over-interpret available market information. This article compares investors’ reaction to market shocks across different industrial sectors, through analyzing the information content in implied volatility using financial derivatives of individual companies in Singapore. Investors in the less transparent property and financial service sector are found to overreact on market shocks, further destabilizing the market. The findings imply that regulatory measures that increase the level of transparency could aid the stabilization of markets. JEL Classification: G13, G14, G18

Exploring the Yield Spread Between Sukuk and Conventional Bonds in Malaysia

This article aims to shed light on the differences in yield rate between conventional bond and sukuk in the Malaysian market. We find that the historical yield rates for the government-issued sukuk is significantly higher than the conventional bond. Conversely, there is a slight yield spread discount between the corporate-issued sukuk and bonds for all rating classes. We conclude that liquidity factor can mainly explain the positive yield spread on the government-issued sukuk. We also illustrate the effect of tax and expenses on asset pricing, which may contribute to the yield spread discount for corporate issuance. JEL Classification: E43, G12, G13

The Journal of Emerging Market Finance: A Bibliometric Overview (2002–2019)

This study provides a comprehensive overview of the prominent trends and thematic structure of the Journal of Emerging Market Finance ( JEMF). The article uses bibliometric methodology and in doing so, considers measures such as, but not limited to, h-index, annual publications and citation structure, total citations, citation per publication ratio, most productive authors, institutions and countries, and keyword analysis. The thematic structure of the journal is identified using bibliometric coupling analysis of JEMF articles. Findings suggest that there is an increasing trend in JEMF’s count of publication and citation per year. Researchers from India, UK and the USA are frequent contributors to the journal. Issues mostly addressed in the journal include bank penetration, stock price volatility, calendar anomalies, credit default swaps, market efficiency, asset pricing models, and enterprise risk management. This study will be useful for the readers to gain a quick snapshot of the leading trends of the journal and its recent areas of interest. Finally, the study’s findings would aid the editorial team in taking stock of the journal, its past trajectory, and the road ahead, keeping in view contemporary developments in financial markets in general and emerging markets in particular. JEL Codes: G01, G10, G20

Estimation of Macro-financial Linkages for the Indian Economy

The widespread impacts of global financial crisis (2008-09) reinstate the need for better assessment of the macro-financial linkages for forecasting and policy evaluation. Our paper contributes to the relevant literature with evidence from the Indian financial sector. Following Castelnuovo (2013), a New Keynesian model with macro-financial linkages is estimated by the Bayesian technique for the sample period 2004: Q3 to 2019: Q1. We find that, in an Emerging Market Economy like India, business cycle leads financial cycle through the channel of expectations. Further, our results show that the linkages are heterogeneous in size depending on the financial market segment and market-specific shocks. JEL Codes: C11, E44, G10