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This paper studies whether bilateral international financial connection data help predict bilateral stock return comovement. It is shown that, when the United States is chosen as the benchmark, a larger U.S. portfolio investment asset position on the destination economy predicts a stronger stock return comovement between them. For large economies such as the United States and Germany, the portfolio investment position is also the best predictor among other connection variables. The paper discusses with a simple general equilibrium portfolio model that the empirical pattern is consistent with the behavior of index investors who trade in response to risk-on/risk-off shocks.
This paper studies whether bilateral international financial connection data help predict bilateral stock return comovement. It is shown that, when the United States is chosen as the benchmark, a larger U.S. portfolio investment asset position on the destination economy predicts a stronger stock return comovement between them. For large economies such as the United States and Germany, the portfolio investment position is also the best predictor among other connection variables. The paper discusses with a simple general equilibrium portfolio model that the empirical pattern is consistent with the behavior of index investors who trade in response to risk-on/risk-off shocks.
This paper studies whether bilateral international financial connection data help predict bilateral stock return comovement. It is shown that, when the United States is chosen as the benchmark, a larger U.S. portfolio investment asset position on the destination economy predicts a stronger stock return comovement between them. For large economies such as the United States and Germany, the portfolio investment position is also the best predictor among other connection variables. The paper discusses with a simple general equilibrium portfolio model that the empirical pattern is consistent with the behavior of index investors who trade in response to risk-on/risk-off shocks.
We investigate how corporate stock returns respond to geopolitical risk in the case of South Korea, which has experienced large and unpredictable geopolitical swings that originate from North Korea. To do so, a monthly index of geopolitical risk from North Korea (the GPRNK index) is constructed using automated keyword searches in South Korean media. The GPRNK index, designed to capture both upside and downside risk, corroborates that geopolitical risk sharply increases with the occurrence of nuclear tests, missile launches, or military confrontations, and decreases significantly around the times of summit meetings or multilateral talks. Using firm-level data, we find that heightened geopolitical risk reduces stock returns, and that the reductions in stock returns are greater especially for large firms, firms with a higher share of domestic investors, and for firms with a higher ratio of fixed assets to total assets. These results suggest that international portfolio diversification and investment irreversibility are important channels through which geopolitical risk affects stock returns.
In recent decades, the foreign assets and liabilities of advanced economies have grown rapidly relative to GDP, with the increase in gross cross-holdings far exceeding changes in the size of net positions. Moreover, the portfolio equity and FDI categories have grown in importance relative to international debt stocks. This paper describes the broad trends in international financial integration for a sample of industrial countries and seeks to explain the cross-country and time-series variation in the size of international balance sheets. It also examines the behavior of the rates of return on foreign assets and liabilities, relating them to "market" returns.
This timely volume addresses three important recent trends in the internationalization of United States equity markets: extensive market integration through foreign investment and links among stock prices around the world; increasing securitization as countries such as Japan come to rely more than ever before on markets in equities and bonds at the expense of banks; and the opening of national financial systems of newly industrializing countries to international financial flows and institutions, as governments remove capital controls and other barriers. Eight essays examine such issues as the current extent of international market integration, gains to U.S. investors through international diversification, home-country bias in investing, the role of time and location around the world in stock trading, and the behavior of country funds. Other, long-standing questions about equity markets are also addressed, including market efficiency and the accuracy of models of expected returns, with a particular focus on variances, covariances, and the price of risk according to the Capital Asset Pricing Model.
This four-volume handbook covers important concepts and tools used in the fields of financial econometrics, mathematics, statistics, and machine learning. Econometric methods have been applied in asset pricing, corporate finance, international finance, options and futures, risk management, and in stress testing for financial institutions. This handbook discusses a variety of econometric methods, including single equation multiple regression, simultaneous equation regression, and panel data analysis, among others. It also covers statistical distributions, such as the binomial and log normal distributions, in light of their applications to portfolio theory and asset management in addition to their use in research regarding options and futures contracts.In both theory and methodology, we need to rely upon mathematics, which includes linear algebra, geometry, differential equations, Stochastic differential equation (Ito calculus), optimization, constrained optimization, and others. These forms of mathematics have been used to derive capital market line, security market line (capital asset pricing model), option pricing model, portfolio analysis, and others.In recent times, an increased importance has been given to computer technology in financial research. Different computer languages and programming techniques are important tools for empirical research in finance. Hence, simulation, machine learning, big data, and financial payments are explored in this handbook.Led by Distinguished Professor Cheng Few Lee from Rutgers University, this multi-volume work integrates theoretical, methodological, and practical issues based on his years of academic and industry experience.
The current Global Financial Stability Report (April 2016) finds that global financial stability risks have risen since the last report in October 2015. The new report finds that the outlook has deteriorated in advanced economies because of heightened uncertainty and setbacks to growth and confidence, while declines in oil and commodity prices and slower growth have kept risks elevated in emerging markets. These developments have tightened financial conditions, reduced risk appetite, raised credit risks, and stymied balance sheet repair. A broad-based policy response is needed to secure financial stability. Advanced economies must deal with crisis legacy issues, emerging markets need to bolster their resilience to global headwinds, and the resilience of market liquidity should be enhanced. The report also examines financial spillovers from emerging market economies and finds that they have risen substantially. This implies that when assessing macro-financial conditions, policymakers may need to increasingly take into account economic developments in emerging market economies. Finally, the report assesses changes in the systemic importance of insurers, finding that across advanced economies the contribution of life insurers to systemic risk has increased in recent years. The results suggest that supervisors and regulators should take a more macroprudential approach to the sector.
This paper examines investment allocations in emerging markets by actively-managed U.S. mutual funds. We analyze both country- and firm-level characteristics and policies that influence these investment allocations. At the country-level, we find that U.S. funds invest more in open emerging markets with stronger shareholder rights, legal frameworks and accounting policies. After controlling for country characteristics, U.S. funds are found to invest more in large growing firms with high analyst following and policies such as ADR listing and more transparent accounting policies. The impact of ADR listing and better accounting policies is most pronounced in countries with weaker investor protection. Our results suggest that steps can be taken both at the country- and the firm-level to create an environment conducive to foreign institutional investment.