Time‐varying risk of rare disasters, investment, and asset pricing

Liu, Bo; Niu, Yingjie; Yang, Jinqiang; Zou, Zhentao

doi:10.1111/fire.12226

Cited by 9 publications

(4 citation statements)

References 30 publications

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“…We use the 14 macroeconomic variables suggested by Goyal and Welch (2008) to predict stock returns. There are many alternative predictors for stock returns such as political risk (Gu & Hibbert, 2021), market technicals (Flugum, 2021), rare disaster (Liu et al., 2020), and some specific industry returns (Högholm et al., 2021). We use these 14 predictors because they are the most commonly used in recent literature.…”

Section: Datamentioning

confidence: 99%

Shrinking return forecasts

Liu

Pan

Wang

2022

The Financial Review

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We develop a new approach that shrinks a given model forecast to the benchmark model forecast in order to improve forecasting performance. Simulation results show the superior performance of our approach, relative to popular methods such as forecast combination and the robustness to model misspecification. We apply our method to forecasting the returns on the S&P 500 index and find significant predictability when shrinking the principal component (PC) regression forecasts based on statistical and economic evaluation criteria. The forecast improvement from our shrinkage approach can be explained by the ability of its hyperparameters to be better predict real economic changes.

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Section: Datamentioning

confidence: 99%

Shrinking return forecasts

Liu

Pan

Wang

2022

The Financial Review

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“…Interestingly, ref. [49] suggests a business cycle model of production and capital accumulation that includes a time-varying risk of rare disasters. Based on the results of the investment-output ratio, the authors estimate the jump intensity implicit in the historical data, finding that recession periods coincide with a rapid increase in the probability of a disaster.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Nuclear Hazard and Asset Prices: Implications of Nuclear Disasters in the Cross-Sectional Behavior of Stock Returns

Alonso‐Conde

Rojo-Suárez

2020

Sustainability

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Using stock return data for the Japanese equity market, for the period from July 1983 to June 2018, we analyze the effect of major nuclear disasters worldwide on Japanese discount rates. For that purpose, we compare the performance of the capital asset pricing model (CAPM) conditional on the event of nuclear disasters with that of the classic CAPM and the Fama–French three- and five-factor models. In order to control for nuclear disasters, we use an instrument that allows us to parameterize the linear stochastic discount factor of the conditional CAPM and transform the classic CAPM into a three-factor model. In this regard, the use of nuclear disasters as an explanatory variable for the cross-sectional behavior of stock returns is a novel contribution of this research. Our results suggest that nuclear disasters account for a large fraction of the variation of stock returns, allowing the CAPM to perform similarly to the Fama–French three- and five-factor models. Furthermore, our results show that, in general, nuclear disasters are positively related to the expected returns of a large number of assets under study. Our results have important implications for the task of estimating the cost of equity and constitute a step forward in understanding the relationship between equity risk premiums and nuclear disasters.

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“…Traditional rare disaster models play an essential role in explaining asset returns [1][2][3][4][5][6][7][8][9][10]. At present, there are three main methods for modeling disaster risk: (i) the static disaster probability (a constant) [2,4,6,11]; (ii) the exogenous time-varying disaster probability (e.g., a square-root process) [8,[12][13][14][15]; (iii) the endogenous time-varying disaster probability (e.g., a Bayesian learning process) [16][17][18][19][20][21][22][23][24]. These studies have shown that the time-varying disaster risk, rather than the static disaster risk, is the key to successfully interpreting asset returns by traditional disaster models.…”

Section: Introductionmentioning

confidence: 99%

Asset pricing with long-run disaster risk

FAN

Xiao

2023

PLoS ONE

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Traditional disaster models with time-varying disaster risk are not perfect in explaining asset returns. We redefine rare economic disasters and develop a novel disaster model with long-run disaster risk to match the asset return moments observed in the U.S. data. The difference from traditional disaster models is that our model contains the long-run disaster risk by treating the long-run ingredient of consumption growth as a function of time-varying disaster probability. Our model matches the U.S. data better than the traditional disaster model with time-varying disaster risk. This study uncovers an additional channel through which disaster risk affects asset returns and bridges the gap between long-run risk models and rare disaster models.

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Time‐varying risk of rare disasters, investment, and asset pricing

Cited by 9 publications

References 30 publications

Shrinking return forecasts

Shrinking return forecasts

Nuclear Hazard and Asset Prices: Implications of Nuclear Disasters in the Cross-Sectional Behavior of Stock Returns

Asset pricing with long-run disaster risk

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