This article investigates empirically how returns and volatilities of stock indices are correlated between the Tokyo and New York markets. Using intradaily data that define daytime and overnight returns for both markets, we find that Tokyo (New York) daytime returns are correlated with New York (Tokyo) overnight returns. We interpret this result as evidence that information revealed during the trading hours of one market has a global impact on the returns of the other market. In order to extract the global factor from the daytime returns of one market, we propose and estimate a signal extraction model with GARCH processes.

We model the interrelations of equity market volatility in eight East Asian countries before, during, and after the Asian currency crisis. Using a new class of asymmetric volatility multiplicative error models based on the daily range, we find that dynamic propagation of volatility shocks occurs through a network of interdependencies, and shocks originating in Hong Kong may be amplified in their transmission throughout the system, posing greater risks to the region than shocks originating elsewhere. Although this partly explains the severity of the currency crisis, we also find evidence that parameters shifted, making the system more unstable during the crisis.

The financial crisis of 2007-2009 has given way to the sovereign debt crisis of 2010-2012, yet many of the banking issues remain the same. We discuss a method to estimate the capital that a financial firm would need to raise if we have another financial crisis. This measure of capital shortfall is based on publicly available information but is conceptually similar to the stress tests conducted by US and European regulators. We argue that this measure summarizes the major characteristics of systemic risk and provides a reliable interpretation of the past and current financial crises.

[Definitions are proposed for weak and strong exogeneity in terms of the distribution of observable variables. The objectives of the paper are to clarify the concepts involved, isolate the essential requirements for a variable to be exogenous, and relate them to notions of predeterminedness, strict exogeneity and causality in order to facilitate econometric modelling. Worlds of parameter change are considered and exogeneity is related to structural invariance leading to a definition of super exogeneity. Throughout the paper, illustrative models are used to exposit the analysis.]

Multivariate GARCH models do not perform well in large dimensions due to the so-called curse of dimensionality. The recent DCC-NL model of Engle et al. (2019) is able to overcome this curse via nonlinear shrinkage estimation of the unconditional correlation matrix. In this paper, we show how performance can be increased further by using open/high/low/close (OHLC) price data instead of simply using daily returns. A key innovation, for the improved modeling of not only dynamic variances but also of dynamic correlations, is the concept of a regularized return, obtained from a volatility proxy in conjunction with a smoothed sign of the observed return.

The capital asset pricing model provides a theoretical structure for the pricing of assets with uncertain returns. The premium to induc e risk-averse investors to bear risk is proportional to the nondivers ifiable risk, which is measured by the covariance of the asset return with the market portfolio return. In this paper, a multivariate, gen eralized-autoregressive, conditional, heteroscedastic process is esti mated for returns to bills, bonds, and stocks where the expected retu rn is proportional to the conditional covariance of each return with that of a fully diversified or market portfolio. It is found that the conditional covariances are quite variable over time and are a signi ficant determinant of the time-varying risk premia. The implied betas are also time varying and forecastable.

We propose and implement a procedure to dynamically hedge climate change risk. We extract innovations from climate news series that we construct through textual analysis of newspapers. We then use a mimicking portfolio approach to build climate change hedge portfolios. We discipline the exercise by using third-party ESG scores of firms to model their climate risk exposures. We show that this approach yields parsimonious and industry-balanced portfolios that perform well in hedging innovations in climate news both in sample and out of sample. We discuss multiple directions for future research on financial approaches to managing climate risk.