我国A股市场的模糊性溢价——基于日内高频数据的分析

基于Izhakian(2020)不确定性概率分布下的预期效用理论框架,本文实证研究了我国A股市场的模糊性溢价问题。利用上证综指的日内高频收益数据估计日收益率分布,以月度内日收益率分布的波动性衡量月度市场模糊性,并对模糊性-风险-收益的权衡关系进行实证检验。结果发现:(1)我国A股市场存在时变的模糊性,其模糊性溢价不总是正数,而是依赖于投资者对市场的预期;(2)模糊性溢价和风险溢价受到市场预期的影响,好的市场预期有正的模糊性溢价(模糊厌恶)和负的风险溢价(风险爱好),不好的市场预期有负的模糊性溢价(模糊爱好)和正的风险溢价(风险厌恶)。这些发现不同于当前文献中的许多结果,可以很好地解释市场中的"追涨杀跌"现象。     

Inventory control under different forms of uncertainty: Ambiguity and stochastic variability

Inventory decision makers routinely face ambiguity due to the psychological awareness that there is unknown information about salient events that is knowable in principle. Researchers on inventory control behavior in the face of uncertainty have primarily focused on uncertainty due to stochastic variability. However, most decision situations in the naturally occurring world involve both forms of uncertainty—ambiguity and stochastic variability. We report the results of two experiments that partial out the effects of ambiguity and stochastic variability by orthogonal manipulation of these two forms of uncertainty in a newsvendor task. Contrary to established mathematical models of decision making under uncertainty, increased ambiguity results in increased mean absolute percentage error, and a corresponding decrease in profit. We also find a systematic bias toward underordering associated with increased ambiguity, which is over and above the bias associated with increased stochastic variability. We do not see evidence for learning with repeated play, so that the effects of induced ambiguity appear to persist. Finally, based on our findings, we suggest measures that managers can use to ameliorate the effects of ambiguity.     

Perceived Ambiguity and Relevant Measures

We axiomatize preferences that can be represented by a monotonic aggregation of subjective expected utilities generated by a utility function and some set of i.i.d. probability measures over a product state space, S^∞. For such preferences, we define relevant measures, show that they are treated as if they were the only marginals possibly governing the state space, and connect them with the measures appearing in the aforementioned representation. These results allow us to interpret relevant measures as reflecting part of perceived ambiguity, meaning subjective uncertainty about probabilities over states. Under mild conditions, we show that increases or decreases in ambiguity aversion cannot affect the relevant measures. This property, necessary for the conclusion that these measures reflect only perceived ambiguity, distinguishes the set of relevant measures from the leading alternative in the literature. We apply our findings to a number of well‐known models of ambiguity‐sensitive preferences. For each model, we identify the set of relevant measures and the implications of comparative ambiguity aversion.     

Public Perception of Uncertainties Within Climate Change Science

Climate change is a complex, multifaceted problem involving various interacting systems and actors. Therefore, the intensities, locations, and timeframes of the consequences of climate change are hard to predict and cause uncertainties. Relatively little is known about how the public perceives this scientific uncertainty and how this relates to their concern about climate change. In this article, an online survey among 306 Swiss people is reported that investigated whether people differentiate between different types of uncertainty in climate change research. Also examined was the way in which the perception of uncertainty is related to people's concern about climate change, their trust in science, their knowledge about climate change, and their political attitude. The results of a principal component analysis showed that respondents differentiated between perceived ambiguity in climate research, measurement uncertainty, and uncertainty about the future impact of climate change. Using structural equation modeling, it was found that only perceived ambiguity was directly related to concern about climate change, whereas measurement uncertainty and future uncertainty were not. Trust in climate science was strongly associated with each type of uncertainty perception and was indirectly associated with concern about climate change. Also, more knowledge about climate change was related to less strong perceptions of each type of climate science uncertainty. Hence, it is suggested that to increase public concern about climate change, it may be especially important to consider the perceived ambiguity about climate research. Efforts that foster trust in climate science also appear highly worthwhile.     

Uncertainty about Probability: A Decision Analysis Perspective

The issue of how to think about "uncertainty about probability" is framed and analyzed from the viewpoint of a decision analyst. The failure of nuclear power plants is used as an example. The key idea is to think of probability as describing a state of information on an uncertain event, and to pose the issue of uncertainty in this quantity as uncertainty about a number that would be definitive: it has the property that you would assign it as the probability if you knew it. Logical consistency requires that the probability to assign to a single occurrence in the absence of further information be the mean of the distribution of this definitive number, not the median as is sometimes suggested. Any decision that must be made without the benefit of further information must also be made using the mean of the definitive number's distribution. With this formulation, we find further that the probability of r occurrences in n exchangeable trials will depend on the first n moments of the definitive number's distribution. In making decisions, the expected value of clairvoyance on the occurrence of the event must be at least as great as that on the definitive number. If one of the events in question occurs, then the increase in probability of another such event is readily computed. This means, in terms of coin tossing, that unless one is absolutely sure of the fairness of a coin, seeing a head must increase the probability of heads, in distinction to usual thought. A numerical example for nuclear power shows that the failure of one plant of a group with a low probability of failure can significantly increase the probability that must be assigned to failure of a second plant in the group.     

The impact of tax uncertainty on irreversible investment

Traditional models of capital budgeting with taxes are based on deterministic tax rates and tax bases. In reality, however, there are multiple sources of tax uncertainty. Frequent tax reforms make future taxation of investments a stochastic process. Fiscal authorities and tax courts create additional tax uncertainty by interpreting current tax laws differently. Moreover, simplified models that anticipate the actual tax base incorrectly contribute to tax uncertainty as perceived by investors. I analyze the effects of stochastic taxation on investment behavior in a real options model. The investor holds an option to invest in an irreversible project with stochastic cash flows and stochastic tax payments. Pre-tax cash flows and tax payments are assumed to be correlated. Increased tax uncertainty has an ambiguous impact on investment timing. For low tax uncertainty, high cash flow uncertainty and high correlation of cash flows and tax payments, increased tax uncertainty is likely to accelerate investment. A higher expected tax payment delays investment. A higher after-tax discount rate affects investment timing ambiguously.     

PERCEIVED ENVIRONMENTAL UNCERTAINTY AS A MEDIATOR OF EXPECTANCY THEORY PREDICTIONS: SOME PRELIMINARY FINDINGS

Expectancy theory has been tested in a variety of research settings with somewhat mixed results. One possible explanation for the sometimes low predictive ability of the model is the presence of environmental uncertainty. It was hypothesized that the greater the perceived environmental uncertainty, the greater the uncertainty regarding expectancy estimates. Hence, the predicted relationship between the level of motivation and job performance was expected to vary with the level of perceived uncertainty. Empirical results are presented supporting the hypothesis.     

The Effects of Presenting Imprecise Probabilities in Intelligence Forecasts

How to assess and present analytic uncertainty to policymakers has emerged as an important topic in risk and policy analysis. Due to the complexity and deep uncertainty present in many forecasting domains, these reports are often fraught with analytic uncertainty. In three studies, we explore the effect of presenting probability assessments and analytic uncertainty through probability ranges. Participants were presented with mock intelligence forecasts that include narrative evidence as well as numerical probability assessments. Participants were sensitive to the ambiguity communicated through the confidence range. The narrative appeared to have a smaller effect on judgments when accompanied by a probability range as opposed to a point assessment. In one study, participants also thought that the probability range was more useful for decision making at a higher probability whereas the point estimate was more useful at a lower probability. When evaluating a forecast in hindsight, decisionmakers tended to report lower levels of blame and higher levels of source credibility for forecasts that reported ranges as compared to point assessments. These findings suggest that decisionmakers are not necessarily “ambiguity averse” in the forecasting context. Presenting ranges of probability may have distinct advantages as a way to communicate probability and analytic confidence to decisionmakers.     

An Empirical Model of Perceived Mortality Risks for Selected U.S. Arsenic Hot Spots

Researchers have long recognized that subjective perceptions of risk are better predictors of choices over risky outcomes than science‐based or experts’ assessments of risk. More recent work suggests that uncertainty about risks also plays a role in predicting choices and behavior. In this article, we develop and estimate a formal model for an individual's perceived health risks associated with arsenic contamination of his or her drinking water. The modeling approach treats risk as a random variable, with an estimable probability distribution whose variance reflects uncertainty. The model we estimate uses data collected from a survey given to a sample of people living in arsenic‐prone areas in the United States. The findings from this article support the fact that scientific information is essential to explaining the mortality rate perceived by the individuals, but uncertainty about the probability remains significant.     

The Effect of Cognitive Load on Decision Making with Graphically Displayed Uncertainty Information

An experiment examined the ability of five graphical displays to communicate uncertainty information when end users were under cognitive load (i.e., remembering an eight‐digit number). The extent to which people could accurately derive information from the graphs and the adequacy of decisions about optimal behaviors based on the graphs were assessed across eight scenarios in which probabilistic outcomes were described. Results indicated that the load manipulation did not have an overall effect on derivation of information from the graphs (i.e., mean and probability estimation) but did suppress the ability to optimize behavioral choices based on the graph. Cognitive load affected people's use of some graphical displays (basic probability distribution function) more than others. Overall, the research suggests that interpreting basic characteristics of uncertainty data is unharmed under conditions of limited cognitive resources, whereas more deliberative processing is negatively affected.     

Leadership and Uncertainty: How Role Ambiguity Affects the Relationship between Leader Group Prototypicality and Leadership Effectiveness

In social identity analyses of leadership the role of leader group prototypicality (the extent to which the leader is representative of the collective identity) in leadership effectiveness is emphasized. We extend this analysis by identifying role ambiguity as a situational influence that feeds into the desire to reduce uncertainty, as a moderator of the relationship between leader group prototypicality and indicators of leadership effectiveness (perceived effectiveness, job satisfaction and turnover intentions). Role ambiguity is proposed to lead people to turn to their group memberships, making leadership effectiveness more contingent on the extent to which leaders are group prototypical. Results of a survey of n=368 employees of four Italian companies supported this hypothesis. Role ambiguity and leader group prototypicality interacted in predicting perceived effectiveness, job satisfaction and turnover intentions, such that leader group prototypicality was more strongly related to leadership effectiveness for employees experiencing greater role ambiguity.     

Perceived Risk and Uncertainty of Nuclear Waste: Differences Among Science,Business, and Environmental Group Members1

This paper presents survey results on perceived risk regarding the management of nuclear waste. Using data taken from random mail surveys of members of scientific, business, and environmental groups in Colorado and New Mexico in the summer of 1990, we examine differences between the groups in their expressed perceptions of risk and also their assessments of the certainty of their beliefs. We consider whether (a) greater uncertainty is associated with greater perceived risks and (b) whether there is greater responsiveness to new information for those whose beliefs are least certain. We find that there are connections between perceived risk and uncertainty, and there is a greater tendency to update risk assessements from a position of greater initial uncertainty. There are, however, differences between the groups as well as asymmetries in their responses to new information. The latter suggests that perceived risks will ratchet upward over time even when information is not biased toward more or less risk.     

An Experimental Study of the Effectiveness of Three Debiasing Techniques*

Subjective probability distributions constitute an important part of the input to decision analysis and other decision aids. The long list of persistent biases associated with human judgments under uncertainy [16] suggests, however, that these biases can be translated into the elicited probabilities which, in turn, may be reflected in the output of the decision aids, potentially leading to biased decisions. This experiment studies the effectiveness of three debiasing techniques in elicitation of subjective probability distributions. It is hypothesized that the Socratic procedure [18] and the devil's advocate approach [6] [7] [31] [32] [33] [34] will increase subjective uncertainty and thus help assessors overcome a persistent bias called “overconfidence.” Mental encoding of the frequency of the observed instances into prespecified intervals, however, is expected to decrease subjective uncertainty and to help assessors better capture, mentally, the location and skewness of the observed distribution. The assessors' ratings of uncertainty confirm these hypotheses related to subjective uncertainty but three other measures based on the dispersion of the elicited subjective probability distributions do not. Possible explanations are discussed. An intriguing explanation is that debiasing may affect what some have called “second order” uncertainty. While uncertainty ratings may include this second component, the measures based on the elicited distributions relate only to “first order” uncertainty.     

Assessing Risk Factors in the Organic Control System: Evidence from Inspection Data in Italy

Certification is an essential feature in organic farming, and it is based on inspections to verify compliance with respect to European Council Regulation—EC Reg. No 834/2007. A risk‐based approach to noncompliance that alerts the control bodies to activate planning inspections would contribute to a more efficient and cost‐effective certification system. An analysis of factors that can affect the probability of noncompliance in organic farming has thus been developed. This article examines the application of zero‐inflated count data models to farm‐level panel data from inspection results and sanctions obtained from the Ethical and Environmental Certification Institute, one of the main control bodies in Italy. We tested many a priori hypotheses related to the risk of noncompliance. We find evidence of an important role for past noncompliant behavior in predicting future noncompliance, while farm size and the occurrence of livestock also have roles in an increased probability of noncompliance. We conclude the article proposing that an efficient risk‐based inspection system should be designed, weighting up the known probability of occurrence of a given noncompliance according to the severity of its impact.     

Uncertain Numbers and Uncertainty in the Selection of Input Distributions—Consequences for a Probabilistic Risk Assessment of Contaminated Land

Risks from exposure to contaminated land are often assessed with the aid of mathematical models. The current probabilistic approach is a considerable improvement on previous deterministic risk assessment practices, in that it attempts to characterize uncertainty and variability. However, some inputs continue to be assigned as precise numbers, while others are characterized as precise probability distributions. Such precision is hard to justify, and we show in this article how rounding errors and distribution assumptions can affect an exposure assessment. The outcome of traditional deterministic point estimates and Monte Carlo simulations were compared to probability bounds analyses. Assigning all scalars as imprecise numbers (intervals prescribed by significant digits) added uncertainty to the deterministic point estimate of about one order of magnitude. Similarly, representing probability distributions as probability boxes added several orders of magnitude to the uncertainty of the probabilistic estimate. This indicates that the size of the uncertainty in such assessments is actually much greater than currently reported. The article suggests that full disclosure of the uncertainty may facilitate decision making in opening up a negotiation window. In the risk analysis process, it is also an ethical obligation to clarify the boundary between the scientific and social domains.     

DO FINANCIAL INCENTIVES AFFECT FIRMS’ DEMAND FOR DISABLED WORKERS?

A number of OECD countries aim to encourage work integration of disabled persons using quota policies. For instance, Austrian firms must provide at least one job to a disabled worker per 25 nondisabled workers and are subject to a tax if they do not. This “threshold design” provides causal estimates of the noncompliance tax on disabled employment if firms do not manipulate nondisabled employment; a lower and upper bound on the causal effect can be constructed if they do. Results indicate that firms with 25 nondisabled workers employ about 0.04 (or 12%) more disabled workers than without the tax; firms do manipulate employment of nondisabled workers but the lower bound on the employment effect of the quota remains positive; employment effects are stronger in low‐wage firms than in high‐wage firms; and firms subject to the quota of two disabled workers or more hire 0.08 more disabled workers per additional quota job. Moreover, increasing the noncompliance tax increases excess disabled employment, whereas paying a bonus to overcomplying firms slightly dampens the employment effects of the tax.     

An Approach for Modeling Noncancer Dose Responses with an Emphasis on Uncertainty

This paper presents an approach for characterizing the probability of adverse effects occurring in a population exposed to dose rates in excess of the Reference Dose (RfD). The approach uses a linear threshold (hockey stick) model of response and is based on the current system of uncertainty factors used in setting RfDs. The approach requires generally available toxicological estimates such as No-Observed-Adverse-Effect Levels (NOAELs) or Benchmark Doses and doses at which adverse effects are observed in 50% of the test animals (ED₅₀s). In this approach, Monte Carlo analysis is used to characterize the uncertainty in the dose response slope based on the range and magnitude of the key sources of uncertainty in setting protective doses. The method does not require information on the shape of the dose response curve for specific chemicals, but is amenable to the inclusion of such data. The approach is applied to four compounds to produce estimates of response rates for dose rates greater than the RfD     

The problem with knowledge ambiguity

This paper proposes a new conceptualisation of the construct of knowledge ambiguity. This new conceptualisation is essential because (1) past researchers have tended to narrowly define and operationalise knowledge ambiguity in terms of causal ambiguity or tacitness and (2) the prevalent non-comprehensive conceptualisation constrains our ability to overcome the problem of knowledge ambiguity. Knowledge ambiguity has been identified as a major obstacle to effective knowledge transfer and to the implementation of overall knowledge management systems. The new conceptualisation proposes that knowledge ambiguity is composed of two types of ambiguity: component ambiguity and causal ambiguity. Component ambiguity is uncertainty about knowledge content, whereas causal ambiguity is uncertainty about how to use the knowledge. This re-conceptualisation is supported by previous studies on knowledge characteristics, absorptive capacity and cognitive learning. In this paper, theoretical propositions are developed to demonstrate the compatibility of the new conceptualisation with the current understanding of these concepts. The present paper not only advances our understanding of knowledge ambiguity, it also points towards solutions for overcoming the problems associated with knowledge ambiguity. Different measures are required to overcome problems created by component ambiguity vs. causal ambiguity. This paper’s re-conceptualisation of knowledge ambiguity makes it easier to theorise about and operationalise the concept. It aligns the definition of knowledge ambiguity with current definitions of related constructs such as absorptive ambiguity and cognitive learning that are used in the broader knowledge transfer and knowledge management literatures.     

Communicating Conservation Status: How Different Statistical Assessment Criteria Affect Perceptions of Extinction Risk

Although alternative forms of statistical and verbal information are routinely used to convey species’ extinction risk to policymakers and the public, little is known about their effects on audience information processing and risk perceptions. To address this gap in literature, we report on an experiment that was designed to explore how perceptions of extinction risk differ as a function of five different assessment benchmarks (Criteria A–E) used by scientists to classify species within IUCN Red List risk levels (e.g., Critically Endangered, Vulnerable), as well as the role of key individual differences in these effects (e.g., rational and experiential thinking styles, environmental concern). Despite their normative equivalence within the IUCN classification system, results revealed divergent effects of specific assessment criteria: on average, describing extinction risk in terms of proportional population decline over time (Criterion A) and number of remaining individuals (Criterion D) evoked the highest level of perceived risk, whereas the single‐event probability of a species becoming extinct (Criterion E) engendered the least perceived risk. Furthermore, participants scoring high in rationality (analytic thinking) were less prone to exhibit these biases compared to those low in rationality. Our findings suggest that despite their equivalence in the eyes of scientific experts, IUCN criteria are indeed capable of engendering different levels of risk perception among lay audiences, effects that carry direct and important implications for those tasked with communicating about conservation status to diverse publics.     

On “Black Swans” and “Perfect Storms”: Risk Analysis and Management When Statistics Are Not Enough

Two images, “black swans” and “perfect storms,” have struck the public's imagination and are used—at times indiscriminately—to describe the unthinkable or the extremely unlikely. These metaphors have been used as excuses to wait for an accident to happen before taking risk management measures, both in industry and government. These two images represent two distinct types of uncertainties (epistemic and aleatory). Existing statistics are often insufficient to support risk management because the sample may be too small and the system may have changed. Rationality as defined by the von Neumann axioms leads to a combination of both types of uncertainties into a single probability measure—Bayesian probability—and accounts only for risk aversion. Yet, the decisionmaker may also want to be ambiguity averse. This article presents an engineering risk analysis perspective on the problem, using all available information in support of proactive risk management decisions and considering both types of uncertainty. These measures involve monitoring of signals, precursors, and near‐misses, as well as reinforcement of the system and a thoughtful response strategy. It also involves careful examination of organizational factors such as the incentive system, which shape human performance and affect the risk of errors. In all cases, including rare events, risk quantification does not allow “prediction” of accidents and catastrophes. Instead, it is meant to support effective risk management rather than simply reacting to the latest events and headlines.