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The Factor-augmented Error-Correction Model (FECM) generalizes the factor-augmented VAR (FAVAR) and the Error-Correction Model (ECM), combining error-correction, cointegration and dynamic factor models. It uses a larger set of variables compared to the ECM and incorporates the long-run information lacking from the FAVAR because of the latter’s specification in differences. In this paper, we review the specification and estimation of the FECM, and illustrate its use for forecasting and structural analysis by means of empirical applications based on Euro Area and US data.

The authors introduce a new approach to estimate high-dimensional factor-augmented vector autoregressive models (FAVAR) where the loadings are subject to idiosyncratic regime-switching dynamics. Our Bayesian estimation method alleviates computational challenges and makes the estimation of high-dimensional FAVAR with heterogeneous regime-switching straightforward to implement. The authors perform extensive simulation experiments to study the finite sample performance of our estimation method, demonstrating its relevance in high-dimensional settings. Next, the authors illustrate the performance of the proposed framework for studying the impact of credit market disruptions on a large set of macroeconomic variables. The results of this study underline the importance of accounting for non-linearities in factor loadings when evaluating the propagation of aggregate shocks.

This chapter investigates the performance of different dimension reduction approaches for large vector autoregressions in multi-step ahead forecasts. The authors consider factor augmented VAR models using principal components and partial least squares, random subset regression, random projection, random compression, and estimation via LASSO and Bayesian VAR. The authors compare the accuracy of iterated and direct multi-step point and density forecasts. The comparison is based on macroeconomic and financial variables from the FRED-MD data base. Our findings suggest that random subspace methods and LASSO estimation deliver the most precise forecasts.

This paper seeks to assess the impact of monetary policy on house price inflation for the nine census divisions of the US economy.

A factor‐augmented VAR (FAVAR) model is estimated using a large data set comprising of 126 quarterly series over the period 1976:01 to 2005:02.

Overall, the results of this investigation show that house price inflation responds negatively to a positive monetary policy shock, suggesting that the framework does not experience the widely observed price puzzle encountered while analyzing monetary policy shocks with standard sized VARs.

The paper only considers house price inflation and ignores other housing market variables. Moreover, given the recent economy‐wide decline in the house price growth rates, it would be worthwhile to update the data set to a more recent period, to capture the possible breakdown in the relationship of house prices with fundamentals driving the market.

The results based on the impulse response functions indicate that, in general, house price inflation responds negatively to monetary policy shock, but the responses are heterogeneous across the census divisions. In addition, the findings suggest, in particular, the importance of South Atlantic, East South Central, West South Central, Mountain and the Pacific divisions in shaping the dynamics of US house price inflation.

To the best of one's knowledge, this is the first paper to analyze the effect of monetary policy on house price inflation in the nine census divisions of the US economy using a FAVAR model.

The purpose of this paper is to examine the effects of exchange rate shock on the broad spectrum of the US economy using a factor-augmented VAR model (FAVAR).

The authors developed a two-factor FAVAR model and estimated it with the single-step Bayesian likelihood approach using the Gibbs sampling technique. The two factors represented, respectively, the economic activity and price pressures. The exchange rate shock was identified with the Choleski decomposition method for VARs. The authors used the data of 117 time series for the period from 1973:02 to 2007:12. Impulse responses and variance decompositions were computed as the main results.

The authors found that exchange rate shock has pervasive effects on the US economy as the following: depreciation does not appear to help reduce the US trade deficit as both import and export rise with the depreciation shock; in the short run, depreciation appears expansionary as industrial production, manufacturing and employment all increase within a year; in the medium run, depreciation appears inflationary, as consumer price, producer price, import price and export price all increase; and in the medium run, depreciation appears contractionary as personal consumption, consumer confidence, stock price and housing start tend to fall.

Some caveats remain: first, our simple model symmetrically estimates depreciation shock and appreciation shock and, hence, cannot draw inferences for how exchange rate appreciation and depreciation may affect the US economy asymmetrically. Second, the simple model used did not distinguish the different possible sources of exchange rate depreciation shock, the knowledge of which may lead to richer policy implications and is the direction of research for the future.

This research contributes to the literature of whether exchange rate is expansionary or contractionary to the US economy using the FAVAR model. This is the first comprehensive study in the literature studying the pervasive effects of the exchange rate on the broad spectrum of the US economy in one integrated model.

This paper investigates the impact of a monetary policy shock on the production of a sample of 312 industries in manufacturing, mining and utilities in the United States using a factor-augmented vector autoregression (FAVAR) model.

The authors use a FAVAR model that builds on Bernanke et al. (2005) and Boivin et al. (2009). The main assumption in this model is that the dynamics of a large set of macro variables are captured by some observed and unobserved common factors. The unobserved factors are extracted from a large set of macroeconomic data. The key advantage of using this model is that it allows extracting the impulse responses of a wide range of macroeconomic variables to structural shocks in the federal funds rate.

The results indicate that industries exhibit differential responses to an unanticipated monetary policy tightening. In general, manufacturing industries appear to be more sensitive compared to mining, and utility industries and durable manufacturing industries are found to be more sensitive than those within nondurable and other manufacturing industries to a monetary policy shock. While all industries respond to the policy shock, most of the responses are reversed between 12 and 22 months.

The implication of our results is that monetary policy can be used to impact most US industries for four years and beyond. The existence of disparate responses across industries underscores the difficulty of implementing a monetary policy that will generate the same impact across industries. As the effects of the policy are distinct, policymakers may want to attend to the unique impacts and implement industry-specific policy.

The study is important in the context of the current challenges in the US economy caused by the spread of coronavirus. For example, to tackle the current pandemic, the researchers are trying to come up with cures for COVID-19. A considerable response of the chemical industry that provides materials to pharmaceutical and medicine manufacturing to the monetary policy shock implies that an expansionary monetary policy may facilitate an invention and adequate supply of the cure later on. The same policy may not effectively stimulate production in apparel or leather product industries that are being hard hit by the pandemic.

The study contributes to the literature in broadly two aspects. First, to the best of our knowledge, this is the first paper that investigates the impact of a monetary policy shock on a sample of 312 industries in manufacturing, mining and utilities in the US. Second, to identify structural shocks and investigate the effects of monetary policy shocks on economic activity, the authors diverge from the literature's traditional approach, i.e. the vector autoregression (VAR) method and use a FAVAR method. The FAVAR provides a comprehensive description of the impact of a monetary policy innovation on different industries.

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)

We aim to clarify the effect of economic uncertainty on Chinese stock market fluctuations. We extend the understanding of the asymmetric connectedness between economic uncertainty and stock market risk and provide different characteristics of spillovers from economic uncertainty to both upside and downside risk. Furthermore, we aim to provide the different impact patterns of stock market volatility following several exogenous shocks.

We construct a Chinese economic uncertainty index using a Factor-Augmented Variable Auto-Regressive Stochastic Volatility (FAVAR-SV) model for high-dimensional data. We then examine the asymmetric impact of realized volatility and economic uncertainty on the long-term volatility components of the stock market through the asymmetric Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling (GARCH-MIDAS) model.

Negative news, including negative return-related volatility and higher economic uncertainty, has a greater impact on the long-term volatility components than positive news. During the financial crisis of 2008, economic uncertainty and realized volatility had a significant impact on long-term volatility components but did not constitute long-term volatility components during the 2015 A-share stock market crash and the 2020 COVID-19 pandemic. The two-factor asymmetric GARCH-MIDAS model outperformed the other two models in terms of explanatory power, fitting ability and out-of-sample forecasting ability for the long-term volatility component.

Many GARCH series models can also combine the GARCH series model with the MIDAS method, including but not limited to Exponential GARCH (EGARCH) and Threshold GARCH (TGARCH). These diverse models may exhibit distinct reactions to economic uncertainty. Consequently, further research should be undertaken to juxtapose alternative models for assessing the stock market response.

Our conclusions have important implications for stakeholders, including policymakers, market regulators and investors, to promote market stability. Understanding the asymmetric shock arising from economic uncertainty on volatility enables market participants to assess the potential repercussions of negative news, engage in timely and effective volatility prediction, implement risk management strategies and offer a reference for financial regulators to preemptively address and mitigate systemic financial risks.

First, in the face of domestic and international uncertainties and challenges, policymakers must increase communication with the market and improve policy transparency to effectively guide market expectations. Second, stock market authorities should improve the basic regulatory system of the capital market and optimize investor structure. Third, investors should gradually shift to long-term value investment concepts and jointly promote market stability.

This study offers a novel perspective on incorporating a Chinese economic uncertainty index constructed by a high-dimensional FAVAR-SV model into the asymmetric GARCH-MIDAS model.

The study focuses on examining the impact of the supply shock on the Indian macroeconomic variables during the COVID-19 period.

Time-varying factor augmented vector autoregressive model has been employed to study the asymmetry in transmission of supply shock on Indian economy during pre- and post-COVID-19 times.

The authors find that with supply shock, retail food inflation outpaced in COVID-19 times. Production levels reported by IIP fell to abysmally low levels in the post-COVID-19 times when the economy stalled. The liquidity stimulus provided by the central bank led to the negative response of policy rates to the supply shocks during the COVID-19 times.

The study stands novel in examining the impact of COVID-19 pandemic on Indian economy through the lenses of asymmetric transmission of supply shock during pre- and post-COVID-19 times.

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.