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This chapter introduces the best linear predictor (BLP) with the asymptotic minimum mean squared forecasting error (MSFE) among linear predictors of variables in cointegrated systems. Accordingly, the authors show that (i) if the autocorrelation coefficient of the cointegration error between the prediction time and the predicted targeting time is larger than ½ (representing a short prediction period), then the BLP is deduced from the random walk model; and (ii) in other cases (representing a long prediction period), the BLP is deduced from the cointegration model. Under this scheme, we suggest a switching predictor that automatically selects the random walk or cointegration model according to the size of the estimated autocorrelation coefficient. These results effectively explain the superiority reversal in the short- and long-term prediction of the exchange rate between the random walk and the structural/cointegration model (known as the Meese–Rogoff or disconnect puzzle).

Financial market holds superior information that can give insights into the future trajectory of economic growth. Further, identifying sectors that hold the key to future economic growth is important for all economies, but particularly relevant to emerging markets. However, unlike existing studies, the paper provides new insights into the forward-oriented nexus between financial markets and economic growth.

This paper takes a forward-looking approach of using financial market information to predict future economic growth. The authors use ARDL modeling approach to predict economic growth using the information from stock market sectoral returns.

The authors find that sectoral stock returns significantly improve economic growth forecasts. However, the forecasting superiority is not uniform across sectors and horizons. Auto, consumers' spending, materials and realty sectors provide the most forecasting gains. In contrast, banking, capital goods and industrial sectors provide superior forecasts, but only at horizons beyond one year. The authors also find that the forecast superiority of sectors at longer horizons is inversely related to volatility.

Research highlights the need for sector-focused policy actions in driving economic growth. Further, the findings of the paper identify sectors that drive short-, medium- and long-term economic growth.

There is a significant heterogeneity among different sectors and across horizons in predicting economic growth. Results suggest that targeted policy actions in sectors like materials, metals, oil and gas, and realty are key in driving economic growth. Further, policies geared toward the grassroots industries are at least as beneficial as the large-scale industries. Evidence also suggests the need for an active fiscal policy to address infrastructural bottlenecks in primary industries like basic materials and energy. Evidence nevertheless does not undermine the role of monetary policy actions.

Unlike any paper till date, the innovation of the paper is that it takes an ARDL modeling approach to measure stock market sectoral returns' ability to forecast economic growth several months ahead in the future. Though the paper considers the Indian case, the innovation and contribution extents to the entire field of economic studies.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.

Existing empirical evidence suggests that episodes of financial stress (crises) can act as driver of growth of inequality. Consequently, in this study, the authors explore the time-varying predictive power of an index of financial stress for growth in income (and consumption) inequality in the UK. The authors focus on the UK since income (and consumption) inequality data are available at a high frequency, i.e. on a quarterly basis for over 40 years (June, 1975 to March, 2016).

The authors use Wang and Rossi's approach to analyze the time-varying impact of financial stress on inequality. Hence, the method provides a more appropriate inference of the effect rather than a constant parameter Granger causality method. Besides, understandably, the time-varying approach helps to depict the time-variation in the strength of predictability of financial stress on inequality.

This study’s findings point that financial distress correspond to subsequent increases in inequality, with the index of financial stress containing important information in predicting growth in income inequality for both in and out-of-sample periods. Interestingly, the strength of the in-sample predictive power is high post the period of the global financial crisis, as was observed in the early part of the sample. The authors believe these findings highlight an important role of financial stress for inequality – an area of investigation that has in general remained untouched.

Accurate prediction of inequality at a higher frequency should be more relevant to policymakers in designing appropriate policies to circumvent the wide-ranging negative impacts of inequality, compared to when predictions are only available at the lower annual frequency.

This study aims to explain how delinquency shocks in one type of debt contaminate the others. That is, the authors aim to shed light on the time pattern of delinquencies in different debt types.

This study analyzes the interdependencies between mortgage, credit card and auto loans delinquency rates in the USA from 2003 to 2019, using a panel VAR-X, the panel Granger causality tests and the Geweke linear dependence measures. The authors also compute the impulse response functions of a shock to one kind of debt on the others and decompose the variance of the forecast errors.

The authors find a statistically significant bidirectional Granger causality between the delinquencies. The Geweke measures of linear dependence and the Dumitrescu and Hurlin Granger non-causality tests support that mortgage predominantly causes credit card and auto loan delinquencies. Auto loans also cause credit card delinquencies. The impulse response functions confirm this pattern. This scenario aligns with a sequence where debtors consider rational first to default on credit cards, second on auto loans and only on mortgages in the last instance. Indeed, credit card delinquencies Granger-cause delinquencies in other debts when it occurs.

To the best of the authors’ knowledge, this is the first study to focus on the temporal pattern of delinquency rates for all the US states, using panel data. Furthermore, the results call for policymakers to design regulations to break the transmission channel from debt delinquencies.

This study seeks to build a financial uncertainty index for Saudi Arabia. This index serves as a leading indicator of Saudi economic activity and helps to describe economic fluctuations and forecast economic trends.

This study adopts an extension of the Jurado et al. (2015) procedure by combining financial uncertainty factors with their net spillover effects on GDP and inflation to construct an aggregate financial uncertainty index. The authors consider 13 monthly financial variables for Saudi Arabia from January 2010 to June 2021.

The empirical results show that the constructed financial uncertainty estimates are good leading indicators of economic activity. The robustness analysis suggests that the authors’ proposed financial uncertainty estimators outperform the alternative estimates used by other existing approaches to estimate the financial conditions index.

To the best of the authors’ knowledge, this is the first attempt at constructing a financial uncertainty index for Saudi Arabia. This study extends the empirical literature, from which the authors propose a novel conceptual framework for building a financial uncertainty index by combining the approach of Jurado et al. (2015) and the time-varying connectedness network approach proposed by Antonakakis et al. (2020)

The current study aims to analyze the linkage among bank competition, liquidity and loan price in an interconnected bank network system.

The study employs the Lerner index to estimate bank power; Granger non-causality for estimating competition, liquidity and loan price network structure; principal component for developing competition network index, liquidity network index and price network index; and panel VAR and LASSO-VAR for analyzing the dynamics of interactive network effect. Current work considers 33 Indian banks, and the duration of the study is from 2010 to 2020.

Network structures are concentrated during the economic upcycle and dispersed during the economic downcycle. A significant interaction among bank competition, liquidity and loan price networks exists in the Indian banking system.

The study meaningfully contributes to the existing literature by adding new insights concerning the interrelationship between bank competition, loan price and bank liquidity networks. While enhancing competition in the banking system, the regulator should also pay attention toward making liquidity provisions. The interactive network framework provides direction to the regulator to formulate appropriate policies for managing competition and liquidity while ensuring the solvency and stability of the banking system.

The study contributes to the limited literature concerning interactive relationship among bank competition, liquidity and loan price in the Indian banks.