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The study intends to evaluate the impact of inflation expectation on the performance of listed commercial banks in India during 2005–2021. Inflation expectation is considered as a direct policy tool by the policymakers for stability of the economy. The study explores how inflation expectation affects the performance indicators of the Indian banking industry while controlling for a wide range of bank-specific factors.

The study applies the generalized method of moments (GMM) on a panel sample of 27 listed bank to analyse the impact of inflation expectation on banking sector performance. The data on inflation expectation are obtained from the household inflation expectation survey introduced in India by the Reserve Bank of India in 2005. Return on assets (ROA), return on equity (ROE) and Tobin's Q have been considered as the banking performance indicators in this study.

Empirical results exhibit that inflation expectation is instrumental in deciding the banking sector's performance. Inflation expectation has been found to have a significant and positive impact on accounting-based measures of banking performance. At the same time, it shows negative impact on the marketing-based measure.

The study gives a clear picture about how inflation expectation affects the banking performance and the monetary policy of the country. The study provides crucial insights to develop strategic decisions for the Indian banking sector. The adoption of proper macroeconomic policies, taking into account inflation expectation levels, is instrumental in enhancing bank's performance and in achieving economic growth.

This study contributes to the growing body of literature on the impact of inflationary conditions on banking performance. The originality lies in capturing the role of inflation expectation solely in determining banking sector performance.

This paper aims to contribute to the analysis concerning how inflation forecasts from different economic agents (professional forecasters and consumers) lead to varying levels of central bank credibility and how it affects the monetary policy interest rate and its expectations.

Based on the Brazilian economy data from June 2007 to May 2022, the authors provide evidence that is useful for search mechanisms that improve the conduct of monetary policy through the management of inflation expectations. The authors perform several ordinary least squares and generalized method of moments regressions inspired by the Taylor rule principle. In brief, the benchmark model considers that the monetary policy interest rate and its expectations respond to departures of inflation expectations to the target (a proxy for central bank credibility) and the level of economic activity.

The main result of the analysis is that inflation expectations from professional forecasters and consumers imply different perceptions of central bank credibility that affect the monetary policy interest rate and expectations for horizons until one year ahead.

The novelty that the authors bring from the analysis is that the authors calculate central bank credibility by taking into account the “public beliefs” of different economic agents. Furthermore, the authors analyze the effect of central bank credibility from professional forecasters and consumers on the monetary policy interest rate and its expectations.

Driven by the anticipated global stagflation, this straightforward yet novel study examines the cost of inflation as a macroeconomic factor by investigating its influence on stock market growth. Thus, this paper aims to examine the impact of inflation on the probability of initial public offering (IPO) withdrawal decision.

The paper employs a large dataset that covers the period January 1995–December 2019 and comprises 33,536 successful or withdrawn IPOs from 22 nations with various legal and cultural systems. This study applies a probit model utilizing version 15 of Stata statistical software.

This study finds that inflation is substantially and positively correlated with the likelihood of IPO withdrawal. Results of this study show that the IPO withdrawal decision increases up to 90% when the inflation rate climbs by 10%. Multiple robustness tests provide consistent findings.

This study's implications are important for researchers, investment banks, underwriters, issuers, regulators and stock exchanges. When processing IPO proposals, investment banks, underwriters and issuers must consider inflation projections to avoid negative effects, as demonstrated by the findings. In addition, regulators and stock exchanges must be aware of the detrimental impact of inflation on competitiveness in attracting new listings.

To the best of the authors’ knowledge, this study is the first to present convincing evidence of a major relationship between IPO withdrawal decision and inflation.

Most research on tax evasion focused on microeconomic variables revolving around perceptions and decisions of individual taxpayers. However, a new wave of research is now investigating the role of macroeconomic variables in inducing tax evasion. This study adds to the limited studies in this new direction of research. Previous studies found that inflation, low gross domestic product (GDP) growth and gross fixed capital formation causes recession, increases unemployment, raise interest rates, hurts both domestic and foreign direct investments. This study examined the relationship between these variables and estimated tax evasion in Sub-Saharan Africa.

The study adopts a correlation research design with 2,300 data points collected from 23 countries in Sub-Saharan Africa. Specifically, tax to GDP ratio, gross fixed capital formation per GDP and the GDP annual growth report from each country for the period 2011–2020 was retrieved. Generalised least square regression technique was employed to analyse the data due to the presence of heteroskedasticity in the model and random effect was utilized based on the Hausman test. To avoid misspecification and biased result; therefore, all relevant test was conducted including the multicollinearity test.

The results indicate that GDP annual growth and gross fixed capital formation have a significant negative impact on estimated tax evasion in Sub-Saharan Africa. The findings further indicate a negative but insignificant relationship between inflation and estimated tax evasion in Sub-Saharan Africa. The study concludes that both GDP annual growth rate and gross fixed capital formation negatively influence estimated tax evasion and the policy implications in the African continent were discussed.

The new findings on the effects of GDP annual growth, growth fixed capital formation and inflation on estimated tax evasion provide novel knowledge that is currently lacking in the current literature, specifically Sub-Saharan African continent.

The main aim of the study is to explore the volatility spillover effect of cryptocurrencies (Bitcoin, Ethereum and Litecoin) on inflation volatility in India.

A popular tool, the Bivariate GARCH model (BEKK-GARCH), to study the volatility spillover effect, is applied in the study. Monthly data of cryptocurrencies and inflation (WPI and CPI indices) are gathered from 2015 to 2021.

Significant short-term responsiveness of volatility of cryptocurrencies on the inflation volatility is found. In addition to this, the significant volatility spillover effect from the cryptocurrencies to the inflation volatility is found.

The findings of the current paper can be of use for inflation management, target inflation policies and policies to contain the volatility of cryptocurrencies. The significance of the current paper is relevant as governments worldwide are officially recognizing cryptocurrencies and starting the process of launching their official virtual currency.

No other study is observed on the topic. Hence, the contribution and novelty of the findings of the current paper are very high and add value to the nonexistent literature on the topic. Lack of the number of inflation observations (data of CPI and WPI are available only in monthly frequency) crimps the model estimation. As the cryptocurrencies become old, more data points will be available by design, and such problems can be resolved, and better model estimation may be possible.

This study aims to provide a comprehensive analysis of various approaches to the residential property market evolution modelling and to examine the macroeconomic fundamentals that have shaped this market development in Ukraine in recent years.

The study uses a comprehensive data set encompassing relevant macroeconomic indicators and historical apartment prices. Multifactor linear regression (MLR) and ridge regression (RR) models are constructed to identify the impact of multiple predictors on apartment prices. Additionally, the ARIMAX model integrates time series analysis and external factors to enhance modelling and forecasting accuracy.

The investigation reveals that MLR and RR yield accurate predictions by considering a range of influential variables. The hybrid ARIMAX model further enhances predictive performance by fusing external indicators with time series analysis. These findings underscore the effectiveness of a multidimensional approach in capturing the complexity of housing price dynamics.

This research contributes to the real estate modelling and forecasting literature by providing an analysis of multiple linear regression, RR and ARIMAX models within the specific context of property price prediction in the turbulent Ukrainian real estate market. This comprehensive analysis not only offers insights into the performance of these methodologies but also explores their adaptability and robustness in a market characterized by evolving dynamics, including the significant influence of external geopolitical factors.

From these data, the authors construct an uncertainty index through the use of a vector autoregressive (VAR) model to measure the impact of uncertainty on GDP, controlling for inflation, which may affect macroeconomic performance. Results indicate that uncertainty is negatively correlated with the economic cycle and the inter-annual variation of the biannual average product.

This study empirically explores the dynamics of expectations of the Uruguayan manufacturing firms about industrial economic growth. This study explores the dynamics of the industrial economic growth expectations of Uruguayan manufacturing firms. The empirical research is based on firms' expectations data collected through a monthly survey carried out by the Chamber of Industries of Uruguay (CIU) in 2003–2018.

Granger causality tests show that uncertainty Granger-causes industrial production growth and a one standard deviation shock on uncertainty generates a contraction in the industrial production growth rate. Finally, the authors use statistical and network tools to identify groups of firms with similar performance on expectations. Results show that higher uncertainty is associated with smaller, more interconnected groups of firms, and that the number of homogeneous groups and the distance between groups increases with uncertainty. These findings suggest that policies focused on the coordination of expectations can lead to the development of stable opinion groups.

The paper introduces new data and new methodologies to analyze the dynamics of expectations of manufacturing firms about industrial economic growth.

1. An empirical approach to compare expectations of firms is introduced.

2. The occurrence of groups of opinion is tested.

3. Central companies in the network of expectations are detected.

4. More uncertainty implies a higher degree of discrepancy between the overall firm’s opinions and more compact opinion groups.

An empirical approach to compare expectations of firms is introduced.

The occurrence of groups of opinion is tested.

Central companies in the network of expectations are detected.

More uncertainty implies a higher degree of discrepancy between the overall firm’s opinions and more compact opinion groups.

This paper aims to examine the impact of COVID-19 on inflation in Indonesia. There are two questions in this study: (1) Is there an impact of COVID-19 on inflation in Indonesia? and (2) whether there are differences in the impact of COVID-19 on regional inflation in Indonesia, considering the different intensities associated with COVID-19.

The estimation technique showing the impact of the COVID-19 pandemic on inflation uses the difference-in-differences (DID) method described by Pischke (2008). The core idea of the estimation above is continuous DID using panel data. No province was affected by COVID-19 before 2020:Q1. Once COVID-19 hits the economy, the effects vary from one district to the other.

The authors find that the severity of the COVID-19 pandemic negatively affects inflation – the more severe the pandemic, the lower the inflation. This finding conforms with several studies suggesting higher demand pressures than supply during the pandemic. Compared with supply-side indicators such as production index, demand-side indicators – such as consumer confidence index and real sales index – fell more sharply.

In the Introduction section, the authors have added a discussion that indeed the COVID-19 pandemic affects inflation through both the demand- and supply-side shocks. While factors driving regional differences in inflation rate are important research and policy questions, the analysis of these factors is outside the scope of this study. The study focuses on the COVID-19 impact on inflation and whether the pandemic disproportionately affects some regions than the others.

This research is important to provide an understanding of the nature of the pandemic on inflation in the context of the Indonesian economy, which is essential to policy formulation, especially for the Central Bank in carrying out the mandate to maintain rupiah stability. This issue is due to the implications of different policy responses between demand- and supply-side shocks.

As a novelty in this study and research gap, the authors use a continuous DID method to account for the varying intensity of COVID-19 across the provinces. In particular, the authors use the number of positive cases of COVID-19 per 1,000 population as opposed to just a binary indicator of before-and-during COVID-19 across provinces.

This paper analyzes the effects of the monetary stance on the media's favorable (or otherwise) attitude to the State Bank of Vietnam's (SBV) monetary policy using monthly data from 2011 to 2021. Monetary stance is a multivariate index based on the growth rates of money supply and domestic credit. A large set of articles published in five Vietnam daily newspapers are utilized to construct a view of the media's favorableness to the monetary policy.

This paper uses hand-collected data from 211 articles published in five newspapers from December 2011 to September 2021 in order to examine the relationship between the monetary stance and the media's favorableness to monetary policy. Following the studies of He and Pauwels (2008) and Xiong (2012), the authors constructed a multivariate stance index to capture most of the important changes in the SBV's monetary policy stance.

The study's main findings are that a change in monetary stance from easing to neutral/tightening, or from neutral to tightening, is greatly appreciated by the media. The study's findings are robust, especially in terms of alternative measures of the media's favorableness and monetary policy variables.

These findings have important policy implications for implementing SBV's monetary policy.

The main contribution of this paper is that the authors are the first to study the nexus of multivariate monetary stance and the media's favorableness to a central bank's non-inflation-targeting mandate. In particular, the study’s findings confirm that the SBV's multivariate monetary stance affects the media's favorableness, whereas the effect of inflation is statistically insignificant.

Over the last few years, the European Central Bank (ECB) has adopted unconventional monetary policies. These measures aim to boost economic growth and increase inflation through the bond market. The purpose of this paper is to study the impact of the ECB’s quantitative easing (QE) on the investor’s behavior in the stock market.

First, the authors theoretically identify the transmission channels of the QE shocks to the stock market. Then, the authors empirically assess the financial market’s responses to QE shocks in a data-rich environment using a factor augmented VAR (FAVAR).

The results show that the ECB’s unconventional monetary policy positively affects the stock market. A QE shock leads to an increase in stock prices and a drop in the realized volatility and the implied risk premium. The authors also suggest that the ECB’s QE is transmitted to the stock market through five main channels: the liquidity, the expectation, the portfolio reallocation, the interest rates and the risk premium channels.

The findings help to better understand the behavior of stock market assets in a data-rich economic context and guide investors and policymakers in the presence of unconventional monetary tools. For instance, decision-makers and investors should consider the short-term effect of the QE interventions and the changing behavior of the financial actors over time. In addition, high stock market returns can increase risk appetite. This can lead investors to underestimate the market risk. Decision-makers and market participants should take into consideration the impact of the large injection of money through the QE, which may raise the risk of a speculative bubble in the financial market.

To the best of the authors’ knowledge, this is the first study that incorporates a theoretical and empirical analysis to explore QE transmission to the stock market in the European context. Unlike previous studies, the authors use the shadow rate proposed by Wu and Xia (2017) to quantify the effect of the ECB’s QE in a data-rich environment. The authors also include two key risk indicators – the stock market risk premium and the realized volatility – to capture investors’ behavior in the stock market following QE shocks.