Rare Disasters Research Articles

The role of time‐varying rare disaster risks in predicting bond returns and volatility

AbstractThis paper aims to provide empirical evidence to the theoretical claim that rare disaster risks affect government bond market movements. Using a nonparametric quantiles‐based methodology, we show that rare disaster‐risks affect only volatility, but not returns, of 10‐year government bond of the United States over the monthly period of 1918:01 to 2013:12. In addition, the predictability of volatility holds for the majority of the conditional distribution of the volatility, with the exception of the extreme ends. Moreover, in general, similar results are also obtained for long‐term government bonds of an alternative developed country (UK) and an emerging market (South Africa).

Flood Risk Perceptions and Insurance Choice: Do Decisions in the Floodplain Reflect Overoptimism?

In the presence of rare disasters, risk perceptions may not always align with actual risks. These perceptions can nevertheless influence an individual's willingness to mitigate risks through activities such as purchasing flood insurance. In a survey of Maryland floodplain residents, we find that stated risk perceptions predict voluntary flood insurance take-up, while perceptions themselves varied widely among surveyed residents, owing in large part to differences in past flood experience. We use a formal test for overoptimism in risk perceptions and find that, on aggregate, floodplain residents are overly optimistic about flood risks.

Asset pricing with time varying pessimism and rare disasters

We incorporate time-varying consumption volatility in the representative-agent asset pricing model with generalized recursive smooth ambiguity preferences developed by Ju and Miao (2012). We calibrate the model to data on consumption and asset returns since the Great Depression period. Uncertainty aversion amplifies the perceived probability of the disastrous state coupled with high consumption volatility. We find that the model with time-varying volatility generates a high equity risk premium. When we impose the condition that no consumption disasters ever realized in simulated samples, the model with time-varying volatility can reproduce predictability of returns and non-predictability of consumption growth simultaneously, which are consistent with empirical findings.

Reporting (or Not) of Precursors to Disasters

Problem definition: Firms can reduce the risk of rare disasters by accounting for more frequent near misses: precursor events that could have escalated to a disaster but did not. Investigating a near miss reveals its root cause, allowing the firm to improve process safety and reduce disaster risk. A managing firm, however, usually does not directly observe the occurrence of a near miss, but instead relies on the report of a contractor who is also responsible for preventing such incidents by maintaining process safety. This paper explains why near miss reporting may not take place in such a decentralized setting. Academic/Practical Relevance:Literature and practitioners have acknowledged the crucial role of near miss reports in improving process safety. Nevertheless, even at sophisticated and experienced organizations, disaster inquiries invariably uncover a history of unreported or ignored near misses preceding an accident. We provide an explanation for this persistent phenomenon based on rational economic incentives. Methodology: We examine the firm's problem through a dynamic principal-agent model that captures the contractor's potential for under-reporting near misses along with \textit{moral hazard} related to preventive effort. Results: We find that the firm may fail to capitalize on near miss information due to conflicting incentives. This occurs, for instance, when the contractor is unwilling to report near misses because the resulting safety improvements replace the need for his unverifiable preventive effort, thereby lowering his compensation. However, even when the contractor is willing to report near misses, we find that the firm may choose not to record them, opting instead to create stronger incentives for prevention. In both scenarios, the firm forgoes opportunities for process-safety improvement, focusing on existing preventive measures. Managerial Implications: Our findings highlight the challenges in providing incentives for both reporting and prevention, that can result in a failure to leverage near miss information. We examine potential remedies to overcome these issues and increase reporting.

Exchange rate returns and volatility: the role of time-varying rare disaster risks

ABSTRACTThis paper provides empirical evidence to the theoretical claim that rare disaster risks have predictability for exchange rate returns and volatility using a nonparametric quantile-based methodology. Using dollar-based exchange rates for Brazil, Russia, India, China, and South Africa, the quantile-causality test shows that indeed rare disaster-risks affects both returns and volatility over the majority of their respective conditional distributions. In addition, these effects are much stronger when compared to those using the British pound, especially in terms of currency returns.

Time-varying rare disaster risks, oil returns and volatility

This paper provides a novel perspective to the predictive ability of rare disaster risks for West Texas Intermediate (WTI) oil market returns and volatility using a nonparametric quantile-based methodology over the monthly period of 1918:01–2013:12. We show that a nonlinear relationship and structural breaks exists between oil returns and various rare disaster risks; hence, linear Granger causality tests are misspecified and the linear model results of non-predictability are unreliable. However, the quantile-causality test shows that rare disaster-risks strongly affect both WTI returns and volatility, with stronger evidence of predictability observed at lower quantiles of the respective conditional distributions. Our results are robust to alternative specification of volatility (based on a GARCH model), and measure of rare disaster risks (based on the number of crises).

Stochastic Volatility and Asset Pricing Puzzles

This paper builds a real-options, term structure model of the …rm to shed new light on the value premium, …nancial distress, momentum, and credit spread puzzles. The model incorporates stochastic volatility in the …rm productivity process and a negative market price of volatility risk. Since the equity of growth …rms and …nancially distressed …rms have embedded options, such securities hedge against volatility risk in the market and thus command lower volatility risk premia than the equities of value or …nancially healthy …rms. Abnormal risk-adjusted momentum pro…ts are concentrated among low credit-rating …rms for similar reasons. Conversely, since increases in volatility generally reduce the value of debt, corporate debt will tend to command large volatility risk premia, allowing the model to generate higher credit spreads than existing structural models. The paper illustrates that allowing for endogenous default by equityholders is necessary for the model to account for the credit spreads of both investment grade and junk debt. The model is extended to include rare disasters and multiple time scales in volatility dynamics to better account for the expected default frequencies and credit spreads of short maturity debt. Finally, the paper uses a methodology based on asymptotic expansions to solve the model.

Endogenous Disasters

Market economies are intrinsically unstable. The standard search model of equilibrium unemployment, once solved accurately with a globally nonlinear algorithm, gives rise endogenously to rare disasters. Intuitively, in the presence of cumulatively large negative shocks, inertial wages remain relatively high, and reduce profits. The marginal costs of hiring run into downward rigidity, which stems from the trading externality of the matching process, and fail to decline relative to profits. Inertial wages and rigid hiring costs combine to stifle job creation flows, depressing the economy into disasters. The disaster dynamics are robust to extensions to home production, capital accumulation, and recursive utility. (JEL E22, E23, E24, E32, J41, J63, N12)

Do Hedge Funds Exploit Rare Disaster Concerns?

Using out-of-the-money put options on indices of various economic sectors, we develop a rare disaster index (RIX) to quantify ex ante market expectations on future rare disasters (rare disaster concerns). In the cross-section, we measure the aggressiveness of hedge funds exploiting market rare disaster concerns. During normal market times, hedge funds that more aggressively exploit such concerns and supply rare disaster insurance earn higher returns on average. A novel …nding is that during stressful market times, when rare disaster concerns over longer horizons escalate, a subset of hedge funds that are able to supply long-term rare disaster insurance continues to make large pro…ts.

Doubts and variability: A robust perspective on exotic consumption series

Consumption-based asset-pricing models have experienced success in recent years by augmenting the consumption process in ‘exotic’ ways. Two notable examples are the Long-Run Risk and rare disaster frameworks. Such models are difficult to characterize from consumption data alone. Accordingly, concerns have been raised regarding their specification. Acknowledging that both phenomena are naturally subject to ambiguity, we show that an ambiguity-averse agent may behave as if Long-Run Risk and disasters exist even if they do not or exaggerate them if they do. Consequently, prices may be misleading in characterizing these phenomena since they encode a pessimistic perspective of the data-generating process.

Solution methods for models with rare disasters

This paper compares different solution methods for computing the equilibrium of dynamic stochastic general equilibrium (DSGE) models with rare disasters along the lines of those proposed by Rietz (1988), Barro (2006), Gabaix (2012), and Gourio (2012). DSGE models with rare disasters require solution methods that can handle the large nonlinearities triggered by low‐probability, high‐impact events with accuracy and speed. We solve a standard New Keynesian model with Epstein–Zin preferences and time‐varying disaster risk with perturbation, Taylor projection, and Smolyak collocation. Our main finding is that Taylor projection delivers the best accuracy/speed tradeoff among the tested solutions. We also document that even third‐order perturbations may generate solutions that suffer from accuracy problems and that Smolyak collocation can be costly in terms of run time and memory requirements. Rare disasters DSGE models solution methods Taylor projection perturbation Smolyak C63 C68 E32 E37 E44 G12

The World Price of Rare Disaster

This article examines the cross-country asset pricing implications of disaster risk concerns. We construct a new disaster risk index relying on six news-implied rare disaster proxies of Manela and Moreira (2017) and show that this index is a powerful predictor for stock returns and other asset returns in international markets both inand out-of-sample. By further disentangling a global common component from our rare disaster index, we find evidence supporting theories that emphasize globally shared disaster risk as the important driving force of asset price fluctuations. Moreover, we conduct a return decomposition analysis and find that the global disaster risk drives stock returns primarily through the discount rate channel.

A Unified Theory of Asset Pricing

In this paper, we propose a general methodology to characterize (i.e. develop the recursive equation systems for) the dynamic stochastic general equilibrium asset pricing problems (DSGE) with arbitrary numbers of agents and financial assets in a Lucas economy and propose a convergent numerical method to solve the equation systems. Potentially, we can introduce arbitrary market structures, frictions or other exotic settings in agents' optimization problems, such as incomplete market, portfolio constraint, transaction cost, price impact, heterogeneous beliefs, habit formation, generalized recursive preferences, long run risks, idiosyncratic risk, rare disasters, ambiguity aversion, Knightian uncertainty, information asymmetry or some behavioral finance features, such as non-concave utility functions or probability distortions. In particular, we apply our method to three related theoretical asset pricing problems in the DSGE framework: asset pricing with complete market, or incomplete market, heterogeneous beliefs and external habits or generalized recursive preferences and portfolio constraints. A novel convergent numerical technique is proposed, which is based on convergent function approximation (e.g., machine learning function approximation via artificial neural networks). The numerical method introduced is powerful and can be applied to problems of high dimensions and extended to all types of the backward stochastic differential equations or partial differential equations in the literature. With the help of machine learning function approximation, we are able to accurately find the numerical solution of a DSGE or a partial equilibrium asset pricing problem in a future time-space grid. Machine learning technique is also combined with traditional stochastic differential equations with jumps to model the underlying asset prices, which opens the door to a completely new modeling field and therefore gives classical stochastic finance theory a new life. In the end, some forward-looking thoughts in financial modeling are provided. Numerical experiments are carried out and the solutions are analyzed.

Are CEOs Incentivized to Shelter Good Information?

Prior theoretical studies on agency problems hold different opinions against empirical works on two questions: (i). Are CEOs incentivized to shelter good information? (ii). Are CEOs incentivized to evenly shelter firms’ good and bad information? This paper answers these questions by demonstrating that CEOs with high pay-performance incentives tend to successfully shelter good information rather than the bad one from investors. Furthermore, we find that those CEOs with high equity incentives shelter good information by using real earnings management but not accrual-based earnings management. This one-sided information manipulation decreases their firms’ exposure to both stock lottery-like behavior and rare disasters.

Discretizing nonlinear, non-Gaussian Markov processes with exact conditional moments

Approximating stochastic processes by finite‐state Markov chains is useful for reducing computational complexity when solving dynamic economic models. We provide a new method for accurately discretizing general Markov processes by matching low order moments of the conditional distributions using maximum entropy. In contrast to existing methods, our approach is not limited to linear Gaussian autoregressive processes. We apply our method to numerically solve asset pricing models with various underlying stochastic processes for the fundamentals, including a rare disasters model. Our method outperforms the solution accuracy of existing methods by orders of magnitude, while drastically simplifying the solution algorithm. The performance of our method is robust to parameters such as the number of grid points and the persistence of the process. Asset pricing models duality Kullback–Leibler information numerical methods solution accuracy C63 C68 G12

Time-Varying Beta and the Value Premium

We model conditional market beta and alpha as flexible functions of state variables identified via a formal variable-selection procedure. In the post-1963 sample, the beta of the value premium comoves strongly with unemployment, inflation, and the price–earnings ratio in a countercyclical manner. We also uncover a novel nonlinear dependence of alpha on business conditions: It falls sharply and even becomes negative during severe economic downturns but is positive and flat otherwise. The conditional capital asset pricing model (CAPM) performs better than the unconditional CAPM, but this does not fully explain the value premium. Our findings are consistent with a conditional CAPM with rare disasters.

Asset Pricing with Housing Booms and Disasters

By extending Cumulant Generating Function-based pricing formulas to a two-good economy (non-housing and housing), we obtain closed-form solutions for asset prices. The presence of housing impacts risk aversion and induces time variation that is priced in the model. Since housing also brings about composition risk, we show that it introduces endogenously a long-run risk component into the consumption bundle dynamics. Estimating the model over the period 1959 -– 2020, we show that rare booms and busts events in housing expenditures is determinant in obtaining moderate housing risk premium and housing excess return volatility, both in line with empirical results. The real interest rate and both the level and volatility of the equity risk premium also fit the data owing to the presence of the COVID period into our sample period.

Generational Risk Is It a Big Deal? Simulating an 80-Period OLG Model with Aggregate Shocks

We calibrate and simulate 80, 40, and 20-period OLG models with aggregate shocks to assess generational risk. We overcome the curse of dimensionality by building on the Judd, Maliar, and Maliar algorithm, which limits a model's solution to its ergodic states, with no reliance on sparse grids, state-variable aggregation, or local approximations. We find that intrinsic generational risk is quite small, that government policies can produce generational risk, and that bond markets can share generational risk, including risks generated by policy. Our results hold with rare disasters, high risk aversion, persistent shocks, and stochastic depreciation.

What Drives the Consumer Credit Spread? An Explanation Based on Rare Event Risk and Belief Dispersion

What drives consumers borrowing/lending and the credit spread over their debt? This paper offers a novel explanation based on rare event risk and belief dispersion in a dynamic general equilibrium model. Heterogeneous beliefs drive consumers to borrow, but the market is incomplete and subject to rare event risk and thus default endogenously occurs in equilibrium. The paper derives the credit spread in closed form and yields a credit spread similar to real data when the model is calibrated. The model also well captures the relationship between belief dispersion, risk-free rate and credit spread. It shows that belief dispersion, rare event risk and wealth distribution together drive both credit spread and risk-free rate. An increase in either rare event risk or belief dispersion leads to a higher credit spread and a lower risk-free rate. However, the underlying mechanisms are quite different, as the former (rare event risk) is due to substitution effect while the latter (belief dispersion) is due to wealth effect. The paper also makes a contribution to the literature on rare disaster by endogenizing default and clarifies Barro(2006)'s argument on the countervailing effects of rare disasters on interest rates.

News implied volatility and disaster concerns

We construct a text-based measure of uncertainty starting in 1890 using front-page articles of the Wall Street Journal. News implied volatility (NVIX) peaks during stock market crashes, times of policy-related uncertainty, world wars, and financial crises. In US postwar data, periods when NVIX is high are followed by periods of above average stock returns, even after controlling for contemporaneous and forward-looking measures of stock market volatility. News coverage related to wars and government policy explains most of the time variation in risk premia our measure identifies. Over the longer 1890–2009 sample that includes the Great Depression and two world wars, high NVIX predicts high future returns in normal times and rises just before transitions into economic disasters. The evidence is consistent with recent theories emphasizing time variation in rare disaster risk as a source of aggregate asset prices fluctuations.