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This paper presents evidence for a significantly positive link between the dynamic conditional beta and the cross section of daily stock returns. An investment strategy that takes a long position in stocks in the highest conditional beta decile and a short position in stocks in the lowest conditional beta decile produces average returns and alphas in the range of 0.60%–0.80% per month. We provide an investor attention-based explanation of this finding. We show that stocks with high conditional beta have strong attention-grabbing characteristics, leading to a higher fraction of buyer-initiated trades for these stocks. We also find that stocks recently bought perform significantly better than stocks recently sold. Hence, the high beta stocks that investors are more likely to buy have higher expected returns than the low beta stocks that investors are more likely to sell. This paper was accepted by Lauren Cohen, finance .

Ten years ago, the financial crisis spurred research focused on systemic risk. This article examines the history and application of the SRISK measure, which was developed at that time and is now widely used in monitoring systemic risk around the globe. The concept is explained and a variety of ways to measure SRISK are developed. In this article, new results are presented on the uncertainty associated with the SRISK measure and on how it compares with other related measures from both academics and regulators. By focusing on the mechanism by which undercapitalization of the financial sector initiates a financial crisis, new research examines how the probability of a financial crisis is affected by the level of SRISK and, consequently, how much SRISK a country can stand without having a high probability of crisis. The model used to evaluate this probability recognizes the externalities between financial institutions that make an undercapitalized firm or country more fragile if other firms and countries are also undercapitalized.

An extension of the Generalized Autoregressive Score (GAS) model is presented for time series with excess null observations to include explanatory variables. An extension of the GAS model proposed by Harvey and Ito is suggested, and it is applied to precipitation data from a city in Chile. It is concluded that the model provides adequate prediction, and furthermore, an analysis of the relationship between the precipitation variable and the explanatory variables is shown. This relationship is compared with the meteorology literature, demonstrating concurrence.

We introduce a new joint model of expected return and volatility forecasting, namely the two-component Beta-t-QVAR-M-lev (quasi-vector autoregression in-mean with leverage). The maximum likelihood estimator for the two-component Beta-t-QVAR-M-lev is an extension of theoretical results of the one-component Beta-t-QVAR-M. We compare the volatility forecasting performance of the two-component Beta-t-QVAR-M-lev and two-component GARCH-M (generalized autoregressive conditional heteroscedasticity), also considering their one-component frameworks. The results for G20 stock market indices indicate that the forecasting performance of the two-component Beta-t-QVAR-M-lev is superior compared with the two-component GARCH-M and their one-component versions.

This paper derives and tests an intertemporal capital asset pricing model (ICAPM) based on a conditional version of the Campbell-Vuolteenaho two-beta ICAPM (bad beta, good beta (BBGB)). The novel factor is a scaled cash-flow factor that results from the interaction between cash-flow news and a lagged state variable (market dividend yield or consumer price index inflation). The cross-sectional tests over 10 portfolios sorted on size, 10 portfolios sorted on book-to-market, and 10 portfolios sorted on momentum show that the scaled ICAPM explains relatively well the dispersion in excess returns on the 30 portfolios. The results for an alternative set of equity portfolios (25 portfolios sorted on size and momentum) show that the scaled ICAPM prices particularly well the momentum portfolios. Moreover, the scaled ICAPM compares favorably with alternative asset pricing models in pricing both sets of equity portfolios. The scaled factor is decisive to account for the dispersion in average excess returns between past winner and past loser stocks. More specifically, past winners are riskier than past losers in times of high price of risk. Therefore, a time-varying cash-flow beta/price of risk provides a rational explanation for momentum. This paper was accepted by Wei Xiong, finance.

This study empirically tests the contagion effects in stock and real estate investment trust (REIT) markets during the subprime mortgage crisis by using daily stock- and REIT-markets data from the following countries and international bodies: the United States, the European Union, Japan, Hong Kong, Singapore, Australia, and the global REIT market. We found a significant and positive dynamic conditional correlation (DCC) coefficient between stock returns and REIT returns. The results revealed that the REIT markets responded early to market shocks and that the variances were higher in the post-crisis period than in the pre-crisis period. Evidence supporting the contagion effects includes increases in the means of the DCC coefficients during the post-crisis period. The Japanese and Australian REIT markets possess the lowest time-varying downside systematic risks. We also demonstrated that the “DCC E-beta” captures more significant downside linkages between market portfolios and expected REIT returns than does the standard CAPM beta.

We study the issue of identifiability of mixture models in the context of capture-recapture abundance estimation for closed populations. Such models are used to take account of individual heterogeneity in capture probabilities, but their validity was recently questioned by Link (2003, Biometrics 59, 1123-1130) on the basis of their nonidentifiability. We give a general criterion for identifiability of the mixing distribution, and apply it to establish identifiability within families of mixing distributions that are commonly used in this context, including finite and beta mixtures. Our analysis covers binomial and geometrically distributed outcomes. In an example we highlight the difference between the identifiability issue considered here and that in classical binomial mixture models.

Risk involves choices that one makes in a world in which outcomes are random but their probabilities are known in advance. Uncertainty, rather, deals with unknown risks. Therefore, risk is amplified by uncertainty. In addition, uncertainty tends to operate in a feedback loop during crises in which peoples’ decisions are not self‐regulating. As a result, one gets extreme outcomes that are generated by increased uncertainty and hyperelevated risk. Similarly, moving from exogenous to endogenous risk is a very difficult transition to make in modelling—one from a world of known unknown risks to one in which risk is itself a product of the system. Exogenous risks are always present—they are the roll of the die and the forces of nature independent of the behavior that affect outcomes. Moreover, endogenous risks arise because of uncertainty and are intimately related to how one's behavior is affected by the behavior of others. Manias, bubbles, panics, and cascades all occur because of the dynamics of human behavior. Thus, not all risk is adequately captured by returns volatility, and the credit crisis taught risk managers to look beyond returns to liquidity, leverage, and counterparty risk as well. This chapter focuses on adjusting risk estimates in the presence of these sources of risk.