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This chapter seeks to analyze the development across the length and breadth of the Indian financial system in the post-reform period, based on the “flow of funds” accounts estimates by RBI. Besides, this chapter also analyzes the integration of the Indian capital market with the stock markets of the United States, the United Kingdom, Japan, China, Hong Kong, and Singapore using the movements in their stock prices during 1998–2015. Moreover, this chapter is intended for examining the potential implication financial integration, particularly the financial openness of India, on volatility spillover and financial contagion in as much as these two issues have emphatic significance in the determination of the relevant policy roadmap. Our findings broadly confirms the expectations by revealing significantly positive correlations in stock prices, in returns to investments in stock markets, and in mean returns and risk. The integration of the capital markets is also manifested in the cyclical fluctuations of the stock price indices, signifying the underlying sensitivity to random shocks.

I find that median wealth plummeted over the years 2007–2010, and by 2010 was at its lowest level since 1969. The inequality of net worth, after almost two decades of little movement, was up sharply from 2007 to 2010. Relative indebtedness continued to expand from 2007 to 2010, particularly for the middle class, though the proximate causes were declining net worth and income rather than an increase in absolute indebtedness. In fact, the average debt of the middle class actually fell in real terms by 25 percent. The sharp fall in median wealth and the rise in inequality in the late 2000s are traceable to the high leverage of middle-class families in 2007 and the high share of homes in their portfolio. The racial and ethnic disparity in wealth holdings, after remaining more or less stable from 1983 to 2007, widened considerably between 2007 and 2010. Hispanics, in particular, got hammered by the Great Recession in terms of net worth and net equity in their homes. Households under age 45 also got pummeled by the Great Recession, as their relative and absolute wealth declined sharply from 2007 to 2010.

The chapter contains a methodology for formalized evaluation of the role of the Central Bank of the Russian Federation (Bank of Russia) in ensuring monetary and financial sustainability with the help of the monetary policy transmission mechanism and its inflation target regime. The significance of the research of the Bank of Russia operations to ensure financial sustainability is due to a number of circumstances: the uniqueness of the Bank of Russia that appeared only 27 years ago and experienced several devastating events related to the 1998 financial crisis, the global financial crisis of 2008–2009, and the stagnation of the Russian economy in 2014–2016, as well as high volatility of world prices for Russian commodity exports and the latest contra-Russian sanctions that significantly affected the volatility of the Russian ruble. Taking into account all the above, the issue of the Bank of Russia’s effective activities in the long run is aggravated by the fact that there are still more open questions than proven relationships of causes and effects regarding the potential of specific monetary policy instruments in the context of low-growth and high-volatility environment. The modeling of the Bank of Russia strategic and operational targets has been based on the parameters’ dependencies presented by the money (credit) multiplier in the interpretation of G. Schinasi (2006) and on the instability of stable economy hypothesis of H. Minsky (2008). As a result, there have been established the marginal levels of definite indicators of the banking system performance that could allow the Bank of Russia to ensure financial sustainability in the low-growth and high-volatility environment.

Choice under risk has a large stochastic (unpredictable) component. This chapter examines five stochastic models for binary discrete choice under risk and how they combine with “structural” theories of choice under risk. Stochastic models are substantive theoretical hypotheses that are frequently testable in and of themselves, and also identifying restrictions for hypothesis tests, estimation and prediction. Econometric comparisons suggest that for the purpose of prediction (as opposed to explanation), choices of stochastic models may be far more consequential than choices of structures such as expected utility or rank-dependent utility.

Nonperforming assets in any banking system have stressed the economic health of nations. Resultantly, literature has given considerable impetus to predict failures and bankruptcy. Past studies have focused on the outcome of failures, while, there is a dearth of studies focusing on ongoing firms in bad shape. We plug this gap and attempt to identify underlying communication patterns for firms witnessing prolonged underperformance. Using text mining, we extract and analyze semantic, linguistic, emotional, and sentiment-based features in non-numeric communication channels of these poor-performing firms and their peers. These uncovered patterns highlight the use of vocabulary and tone of communication, in correspondence to their financial well-being. Furthermore, using such patterns, we deploy various Machine Learning algorithms to identify loser firm(s) way ahead in time. We observe promising accuracy over a time window of five years. Such early warning signals can be of critical importance to various stakeholders of a firm. Exploration of writing style-related features for any firm would help its investors, lending agencies to assess the likelihood of future underperformance. Firm management can use them to take suitable precautionary measures and preempt the future possibility of distress. While investors and lenders can be benefitted from this incremental information to identify the likelihood of future failures.

Air carriers and aircraft manufacturers are investing in technologies and strategies to reduce fuel consumption and associated emissions. This chapter reviews related issues to assess airline fuel efficiency and offers various empirical evidences from our recent work that focuses on the U.S. domestic passenger air transportation system. We begin with a general presentation of four methods (ratio-based, deterministic frontier, stochastic frontier, and data envelopment analysis) and three perspectives for assessing airline fuel efficiencies, the latter covering consideration of only mainline carrier operations, mainline–subsidiary relations, and airline routing circuity. Airline fuel efficiency results in the short run, in particular the correlations of the results from using different methods and considering different perspectives, are discussed. For the long-term efficiency, we present the development of a stochastic frontier model to investigate individual airline fuel efficiency and system overall evolution between 1990 and 2012. Insight about the association of fuel efficiency with market entry, exit, and airline mergers is also obtained.