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This study investigates the relationship between financial liberalization, democratic institutions, and income inequality in Pakistan by using a two-state Markov switching (MS) methodology for the period 1972–2017. The dynamic factor modelling approach is used to construct the financial liberalization index. The impact of the Global Financial Crisis (GFC) of 2007–2008 on financial liberalization–institutions–inequality nexus was checked using the Chow breakpoint test. The result reveals no significant effect of GFC on Pakistan’s economy. Results from the MS model divulge that financial liberalization lessens income inequality in the high-income inequality regime as well as in the low-income inequality regime. The result further discloses that democratic institutions enhance income inequality in high-income inequality regimes while reduce income inequality in the low-inequality regime. While studying the joint role of democratic institutions and financial liberalization, it is observed that the weak role of democratic institutions offsets the benefits of financial liberalization in the high-income inequality regime; however, institutions and financial liberalization are supportive in reducing income inequality in the low-inequality regime. Besides, the results also confirm the existence of Kuznets hypothesis in Pakistan. Therefore, there is a need for further reforms to revamp institutions to reduce income inequality in Pakistan.

In India, the first official estimate of quarterly gross domestic product (GDP) is released approximately 7–8 weeks after the end of the reference quarter. To provide an early estimate of current quarter GDP growth, we construct Coincident Economic Indicators for India (CEIIs) using a sequentially expanding list of 6, 9, and 12 high-frequency indicators. These indicators represent various sectors, display high contemporaneous correlation with GDP, and track GDP turning points well. CEII-6 includes domestic economic activity indicators, while CEII-9 incorporates indicators of trade and services and CEII-12 adds financial indicators in the model. We include a financial block in CEII-12 to reflect the growing influence of the financial sector on economic activity. CEIIs are estimated using a dynamic factor model which extracts a common trend underlying the high-frequency indicators. The extracted trend provides a real-time assessment of the state of the economy and helps identify sectors contributing to economic fluctuations. Furthermore, GDP nowcasts using CEIIs show considerable gains in both in-sample and out-of-sample accuracy. In particular, we observe that our GDP growth nowcast closely tracks the recent slowdown in the Indian economy.

We characterize the debt risk and bank risk of local governments in China with a dynamic factor model that decomposes 31 provincial data of China into country, regional, and idiosyncratic components. The result shows the presence of co-movement between local government debt risk and bank risk. The country, regional, and province factors explain 62%, 16%, and 22% of local government debt risk, respectively, and 28%, 8%, and 64% of commercial bank risk, respectively. Sub-sample analysis reveals that the country factor has become less volatile and its importance typically increases across all provinces after the reform of local government debt financing pattern (e.g., the introduction of Document No. 43).

A class of dynamic factor and dynamic panel models is proposed for daily high dimensional correlation matrices of asset returns. These flexible semiparametric predictors process ultra high frequency information and allow to exploit both realized correlation matrices and exogenous factors for forecasting purposes. The Fisher-z transformation offers the transmission from (factor and panel) time series models operating on unrestricted random variables to bounded correlation forecasts. Our methodology is contrasted with prominent alternative correlation models. Based on economic performance criteria dynamic factor models turn out to carry the highest predictive content.

In many spatio-temporal data, their spatial variations have inherent global and local structures. The spatially continuous dynamic factor model (SCDFM) decomposes the spatio-temporal data into a small number of spatial and temporal variations, where the spatial variations are represented by the factor loading (FL) functions. However, the FL functions estimated by the maximum likelihood or maximum $L_2$ penalized likelihood capture global structures but do not capture local structures. We propose a method for estimating the spatially multiscale FL functions using a sparse penalty. To overcome the problems of existing sparse penalties, we propose the adaptive graph lasso (AGL) penalty. The method with the AGL penalty eliminates redundant basis functions contained in the FL functions, and leads to the FL functions having global and localized structures. We derive the EM algorithm with block coordinate descent that enables us to maximize the AGL penalized log-likelihood stably. Applications to synthetic and real data show that the proposed modeling procedure accurately extract not only the spatially global structures but also spatially local structures, which the $L_2$ penalized estimation do not extract.

Economic indices, such as the consumer price index, gross domestic product, and purchasing managers’ index, are typically computed based on economic data (e.g., prices of goods and incomes) and surveys. Recently, there has been considerable research to predict these indices using alternative data. One such approach is to utilize textual data, specifically newspaper articles, to predict a business sentiment index (BSI). However, newspapers often exhibit biases, which may impact the resulting index. This potential issue has not received adequate attention. Thus, this work first investigates whether these biases do influence the predicted indices and then proposes an approach to obtaining an unbiased business sentiment index. For this purpose, we analyze multiple Japanese newspapers, including one financial and three general papers with varied political orientations. We then predict a monthly BSI for each newspaper based on its content using a Transformer-based sentiment analysis model. To achieve an unbiased BSI, we employ the dynamic factor model. This model decomposes the time series of BSIs into a common dynamic factor shared across different newspapers and specific dynamic factors unique to individual newspapers. Our experiment indicates that the magnitudes of the predicted BSIs and the specific dynamic factors closely align with the political stances of the newspapers. Furthermore, the common dynamic factor exhibits a stronger and more positive correlation with composite indices and a survey-based BSI, namely the Tankan index.

In credit risk modeling, factor models, either static or dynamic, are often used to account for correlated defaults among a set of financial assets. Within the realm of factor models, default dependence is due to a set of common systematic risk factors. By coupling with a copula function, e.g., the normal, t-, Clayton, Frank, and Gumbel copula functions, an analytic formulation of the joint distribution of assets’ default times can be derived. On the other hand, factor models fail to account for the contagion mechanism of defaults in which a firm’s default risk increases due to their commercial or financial counterparties’ defaults. This study considers the dynamic factor model of Duffee (1999) coupling with a Hawkes process, a class of counting processes allowing intensities to depend on the timing of previous events (Hawkes, 1971) for the contagious effect. Using the factor- contagious-effect model, Monte Carlo simulation is performed to generate default times of two hypothesized firms. It is demonstrated that as the contagious effect increases, the goodness of fit of the joint distribution of assets’ default times based on copula functions decreases, which highlights the deficiency of the copula function approach.

This chapter studies how a well-defined environmental concerns factor influences the equity performance of US energy firms and whether or not this influence differs for renewable and non-renewable energy firms. We construct the environmental sentiment factor and seven sub-group environment-related risk factors using the Dynamic Factor Model econometric method. Then, we estimate stock abnormal returns from well-established asset pricing models to measure firm equity performance. We use an unbalanced panel of 448 US energy firms at the monthly frequency from January 2004 to October 2016. First, we find that all US energy companies show negative abnormal returns in the sampled period, that is the actual returns were lower than expected. Renewable energy firms had higher company value, since renewable energy stocks had lower abnormal returns. We also find that the abnormal returns of renewable energy stocks mainly come from environment regulation risks, while the abnormal returns of non-renewable energy stocks are more sensitive to the measure of public environmental concerns and extreme weather conditions. Consequently, we find that holding the level of environmental related risks constant, non-renewable energy stocks command relative lower abnormal returns.