A simple mean–dispersion model of ambiguity attitudes

Several characterizations of ambiguity aversion decompose preferences into the expected utility of an act and an adjustment factor, an ambiguity index, or a dispersion function. In each of these cases, the adjustment factor has very little structure imposed on it, and thus these models provide little guidance as to which function to use from the infinite class of possible alternatives. In this paper, we provide a simple axiomatic characterization of mean–dispersion preferences which uniquely determines a subjective probability distribution over a set of possible priors and which uniquely identifies the dispersion function. We provide an algorithm for determining this subjective probability distribution and the coefficient in the dispersion function from experimental data. We also demonstrate that the model accommodates ambiguity aversion in the Ellsberg paradox.     

IDENTIFICATION ISSUES IN LIMITED‐INFORMATION BAYESIAN ANALYSIS OF STRUCTURAL MACROECONOMIC MODELS

The likelihood of the parameters in structural macroeconomic models typically has non‐identification regions over which it is constant. When sufficiently diffuse priors are used, the posterior piles up in such non‐identification regions. Use of informative priors can lead to the opposite, so both can generate spurious inference. We propose priors/posteriors on the structural parameters that are implied by priors/posteriors on the parameters of an embedding reduced‐form model. An example of such a prior is the Jeffreys prior. We use it to conduct Bayesian limited‐information inference on the new Keynesian Phillips curve with a VAR reduced form for US data. Copyright © 2014 John Wiley & Sons, Ltd.     

Decision making with imprecise probabilistic information

We develop an axiomatic approach to decision under uncertainty that explicitly takes into account the information available to the decision maker. The information is described by a set of priors and a reference prior. We define a notion of imprecision for this informational setting and show that a decision maker who is averse to information imprecision maximizes the minimum expected utility computed with respect to a subset of the set of initially given priors. The extent to which this set is reduced can be seen as a measure of imprecision aversion. This approach thus allows a lot of flexibility in modelling the decision maker attitude towards imprecision. In contrast, applying Gilboa and Schmeidler [J. Math. Econ. 18 (1989) 141] maxmin criterion to the initial set of priors amounts to assuming extreme pessimism.     

Credit market segmentation,essentiality of commodities,and supermodularity

We consider incomplete market economies where agents are subject to price-dependent trading constraints compatible with credit market segmentation. Equilibrium existence is guaranteed when either commodities are essential, i.e, indifference curves through individuals’ endowments do not intersect the boundary of the consumption set, or utility functions are concave and supermodular. The smoothness of mappings representing preferences, financial promises, or trading constraints is not required. Hence, we may include in our framework economies where ambiguity is allowed and agents maximize the minimum expected utility over a set of priors, or where markets include non-recourse collateralized loans.     

Ambiguous volatility,possibility and utility in continuous time

This paper formulates a model of utility for a continuous time framework that captures the decision-maker’s concern with ambiguity about both the drift and volatility of the driving process. At a technical level, the analysis requires a significant departure from existing continuous time modeling because it cannot be done within a probability space framework. This is because ambiguity about volatility leads invariably to a set of nonequivalent priors, that is, to priors that disagree about which scenarios are possible.     

Incomplete markets, ambiguity, and irreversible investment

The problem of irreversible investment with idiosyncratic risk is studied by interpreting market incompleteness as a source of ambiguity over the appropriate no-arbitrage discount factor. The maxmin utility over multiple priors framework is used to model and solve the irreversible investment problem. Multiple priors are modeled using the notion of κ‐ignorance. This set-up is used to analyze finitely lived options. For infinitely lived options the notion of constant κ‐ignorance is introduced. For these sets of density generators the corresponding optimal stopping problem is solved for general (in-)finite horizon optimal stopping problems driven by geometric Brownian motion. It is argued that an increase in the set of priors delays investment, whereas an increase in the degree of market completeness can have a non-monotonic effect on investment.     

Forecasting with Global Vector Autoregressive Models: a Bayesian Approach

This paper develops a Bayesian variant of global vector autoregressive (B‐GVAR) models to forecast an international set of macroeconomic and financial variables. We propose a set of hierarchical priors and compare the predictive performance of B‐GVAR models in terms of point and density forecasts for one‐quarter‐ahead and four‐quarter‐ahead forecast horizons. We find that forecasts can be improved by employing a global framework and hierarchical priors which induce country‐specific degrees of shrinkage on the coefficients of the GVAR model. Forecasts from various B‐GVAR specifications tend to outperform forecasts from a naive univariate model, a global model without shrinkage on the parameters and country‐specific vector autoregressions. Copyright © 2016 John Wiley & Sons, Ltd.     

Dynamic portfolio choice under ambiguity and regime switching mean returns

I examine a continuous-time intertemporal consumption and portfolio choice problem under ambiguity, where expected returns of a risky asset follow a hidden Markov chain. Investors with Chen and Epstein's (2002) recursive multiple priors utility possess a set of priors for unobservable investment opportunities. The optimal consumption and portfolio policies are explicitly characterized in terms of the Malliavin derivatives and stochastic integrals. When the model is calibrated to U.S. stock market data, I find that continuous Bayesian revisions under incomplete information generate ambiguity-driven hedging demands that mitigate intertemporal hedging demands. In addition, ambiguity aversion magnifies the importance of hedging demands in the optimal portfolio policies. Out-of-sample experiments demonstrate the economic importance of accounting for ambiguity.     

Optimal investment and reinsurance policies for an insurer with ambiguity aversion

In this paper, we study the optimal investment and reinsurance problem for an insurer based on the variance premium principle, in which three cases are considered. First, we assume that the financial market does not exist. The insurer only holds an insurance business, and the optimal reinsurance problem is studied. Subsequently, we assume that there exists a financial market with an accurately modeled risky asset. The optimal investment and reinsurance problem is investigated under these conditions. Finally, we consider the general case in which the insurer is concerned about the model ambiguity of both the insurance market and the financial market. In all three cases, the value function is set to maximize the expected utility of terminal wealth. By employing the dynamic programming principle, we derive the Hamilton–Jacobi–Bellman (HJB) equations, which are satisfied by the value functions and obtain closed-form solutions for optimal reinsurance and investment policies and the value functions in all three cases. Most interestingly, we elucidate how investment improves the insurer’s utility and find that the existence of ambiguity can significantly affect the optimal policies and value functions. We also compare the ambiguities in the two markets and find that ambiguity in the insurance market has much more significant impact on the value function than the ambiguity in the financial market. It implies that it is more valuable for insurer to precisely evaluate the insurance risk. We also provide some numerical examples and economic explanations to illustrate our results.     

非财务信息与财务危机预警——来自我国上市公司的证据

本文从我国上市公司中,选取30家财务危机公司,与30家财务健康公司作为样本。首先对两组公司的13个财务变量,进行均值的t检验和Wilcoxon秩检验。选择合适的财务变量集,再将此财务变量集分别与董事会高管人员持股比例、股权集中度、股票价格变动趋势,及是否更换会计师事务所等,四个非财务变量一起作为解释变量,建立Logistic回归模型。实证研究结果表明,股票价格变动趋势与财务危机风险显著相关,加入非财务变量的预测模型效果更优。     

A flexible approach to parametric inference in nonlinear and time varying time series models

Many structural break and regime-switching models have been used with macroeconomic and financial data. In this paper, we develop an extremely flexible modeling approach which can accommodate virtually any of these specifications. We build on earlier work showing the relationship between flexible functional forms and random variation in parameters. Our contribution is based around the use of priors on the time variation that is developed from considering a hypothetical reordering of the data and distance between neighboring (reordered) observations. The range of priors produced in this way can accommodate a wide variety of nonlinear time series models, including those with regime-switching and structural breaks. By allowing the amount of random variation in parameters to depend on the distance between (reordered) observations, the parameters can evolve in a wide variety of ways, allowing for everything from models exhibiting abrupt change (e.g. threshold autoregressive models or standard structural break models) to those which allow for a gradual evolution of parameters (e.g. smooth transition autoregressive models or time varying parameter models). Bayesian econometric methods for inference are developed for estimating the distance function and types of hypothetical reordering. Conditional on a hypothetical reordering and distance function, a simple reordering of the actual data allows us to estimate our models with standard state space methods by a simple adjustment to the measurement equation. We use artificial data to show the advantages of our approach, before providing two empirical illustrations involving the modeling of real GDP growth.     

Ellsberg’s two-color experiment,portfolio inertia and ambiguity

Results in this paper relate the observation of an interval of prices at which a decision maker (DM) strictly prefers to hold a zero position on an asset (termed “portfolio inertia”) to the DM’s perception of the underlying payoff relevant events as ambiguous, as the term is defined in [Econometrica 69 (2001) 265]. The connection between portfolio inertia and ambiguity is established without invoking a parametric preference form, such as the Choquet expected utility or the max–min multiple priors model. This allows us to draw an observable distinction between portfolio inertia that may arise purely due to first-order risk aversion type effects, such as those which could arise even if preferences were probabilistically sophisticated, and portfolio inertia that involves ambiguity perceptions.     

Data-based priors for vector error correction models

We propose two data-based priors for vector error correction models. Both priors lead to highly automatic approaches which require only minimal user input. For the first one, we propose a reduced rank prior which encourages shrinkage towards a low-rank, row-sparse, and column-sparse long-run matrix. For the second one, we propose the use of the horseshoe prior, which shrinks all elements of the long-run matrix towards zero. Two empirical investigations reveal that Bayesian vector error correction (BVEC) models equipped with our proposed priors scale well to higher dimensions and forecast well. In comparison to VARs in first differences, they are able to exploit the information in the level variables. This turns out to be relevant to improve the forecasts for some macroeconomic variables. A simulation study shows that the BVEC with data-based priors possesses good frequentist estimation properties.     

A representation of decision by analogy

We give a representation of analogical reasoning in choice under uncertainty. A decision maker is faced with two decision problems, a familiar and a novel one. It is shown that, under assumptions that capture the salience of alignable differences in choice, the decision maker can be construed as choosing between acts in the novel decision problem as if drawing all likelihood information from a multi-valued correspondence between states of the world in the familiar domain and states of the world in the novel domain. The latter is interpreted as an analogy between the two decision problems, and the fuzziness of the analogy is related to revealed ambiguity in the novel decision problem.     

Beliefs,payoffs, information: On the robustness of the BDP property in models with endogenous beliefs

Neeman (2004) and Heifetz and Neeman (2006) have shown that, in auctions with incomplete information about payoffs, full surplus extraction is only possible if agents’ beliefs about other agents are fully informative about their own payoff parameters. They argue that the set of incomplete-information models with common priors that satisfy this so-called BDP property (“beliefs determine preferences”) is negligible. In contrast, we show that, in models with finite-dimensional abstract type spaces, the set of belief functions with this property is topologically generic in the set of all belief functions. Our result implies genericity of (non-common or common) priors with the BDP property.     

DECISION THEORY UNDER AMBIGUITY

Abstract We review recent advances in the field of decision making under uncertainty or ambiguity. We start with a presentation of the general approach to a decision problem under uncertainty, as well as the ‘standard’ Bayesian treatment and issues with this treatment. We present more general approaches (Choquet expected utility, maximin expected utility, smooth ambiguity and so forth) that have been developed in the literature under the name of models of ambiguity sensitive preferences. We draw a distinction between fully subjective models and models incorporating explicitly some information. We review definitions and characterizations of ambiguity aversion in these models. We mention the challenges posed by some of the models presented. We end with a review of part of the experimental literature and applications of these models to economic settings.     

Real-time inflation forecasting using non-linear dimension reduction techniques

In this paper, we assess whether using non-linear dimension reduction techniques pays off for forecasting inflation in real-time. Several recent methods from the machine learning literature are adopted to map a large dimensional dataset into a lower-dimensional set of latent factors. We model the relationship between inflation and the latent factors using constant and time-varying parameter (TVP) regressions with shrinkage priors. Our models are then used to forecast monthly US inflation in real-time. The results suggest that sophisticated dimension reduction methods yield inflation forecasts that are highly competitive with linear approaches based on principal components. Among the techniques considered, the Autoencoder and squared principal components yield factors that have high predictive power for one-month- and one-quarter-ahead inflation. Zooming into model performance over time reveals that controlling for non-linear relations in the data is of particular importance during recessionary episodes of the business cycle or the current COVID-19 pandemic.     

The effects of ambiguity on entrepreneurship

We incorporate ambiguity (Knightian uncertainty) into a classic model of entrepreneurship to analyze, among other things, its effects on the optimal level of business startups, the relation between total assets and the size of the entrepreneurial investment, the effects of increasing ambiguity on developing new ventures, and the decision to self‐select into entrepreneurship for an indifferent decision maker. We first show that, under the monotone‐likelihood ratio property, the introduction of ambiguity negatively affects the optimal entrepreneurial investment, something that is consistent with most experimental evidence about entrepreneurial choice under ambiguity. Then, we show that the classical explanations for the positive correlation between total assets and business startups based on decreasing absolute risk aversion preferences and prudent behavior can be challenged when ambiguity is incorporated into the analysis, and we provide the conditions that guarantee that the traditional comparative static result under risk is replicated under ambiguity. We also show that increases in ambiguity aversion reduce entrepreneurial activities. Finally, we discuss our results under alternative ways of modeling ambiguity.     

What drives the banks' diversification decision? A dynamic nonlinear panel data approach

This paper empirically determines the drivers of functional diversification decision for 365 banks set in selected Middle East and North Africa (MENA) countries over 1988–2015. For this purpose, we use a dynamic nonlinear panel data model. Our findings reveal that both market share and financial intermediation stratify the diversification decision for the whole MENA sample. Splitting the sample shows that the risk‐adjusted profitability and the loan loss provision ratio exert a major influence over the diversification indicator for Gulf Cooperation Council (GCC) banks, whereas the net interest margin ratio, the bank market share, and financial intermediation are the major drivers of the strategic decision for the remaining non‐GCC banks.     

A nonparametric decision approach for entrepreneurship

Credit identification is one of core issues of financing process. Enterprise credit involves a lot of financial and non-financial measures, among which entrepreneurship is an important but rarely mentioned variable. Good entrepreneur credit often leads to good enterprise credit. A comprehensive analysis of enterprise credit identification is important to avoid losses, foster excellent enterprise and make the optimal allocation of resources. The existing literature mainly studied the impact of entrepreneurship on enterprise credit from the perspective of historical information, which is about average and tendency. Hence, those models were unable to explain the function of complex human nature and, consequently, linear models are unable to well describe the relationship between enterprise credit and entrepreneur credit. Given the deficiency of parametric models when discussing the impact of entrepreneur credit, a non parametric approach are proposed to individually describe the impact path of different individuals. This paper established a decision tree based on nonparametric approach to verify the practicability of the model in the evaluation of enterprise credit recognition. In the end of this paper, we demonstrate the validity of the non parametric model and the validation method of it.