Dueling policies: Why systemic risk taxation can fail

Two policy instruments for the banking sector are investigated, namely systemic risk taxation and constructive ambiguity about bailout policy. Bailout expectations can induce moral hazard in the form of excessive risk taking by banks. Systemic risk taxation induces banks to prefer uncorrelated investments, leading to lower systemic risk formation. Constructive ambiguity generates uncertainty about bailout prospects. However, systemic risk taxation also may inform banks about the regulator׳s concern for financial stability and thereby its bailout policy. This result leads to a trade-off between systemic risk taxation and constructive ambiguity and highlights the need to consider interdependence across policies when evaluating their effectiveness.     

Intertemporal equilibria with Knightian uncertainty

We study a dynamic and infinite-dimensional model with incomplete multiple prior preferences. In interior efficient allocations, agents share a common risk-adjusted prior and subjective interest rate. Interior efficient allocations and equilibria coincide with those of economies with subjective expected utility and priors from the agents? multiple prior sets. A specific model with neither risk nor uncertainty at the aggregate level is considered. Risk is always fully insured. For small levels of ambiguity, there exists an equilibrium with inertia where agents also insure fully against Knightian uncertainty. When the level of ambiguity exceeds a critical threshold, full insurance no longer prevails and there exist equilibria with inertia where agents do not insure against uncertainty at all. We also show that equilibria with inertia are indeterminate.     

Risk, ambiguity, and state-preference theory

The state-preference framework for modeling choice under uncertainty, in which objects of choice are allocations of wealth or commodities across states of the world, is a natural one for modeling ??smooth?? ambiguity-averse preferences. It does not require reference to objective probabilities, personalistic consequences, or counterfactual acts, and it allows for state dependence of utility and unobservable background risk. The decision maker??s local revealed beliefs are encoded in her risk-neutral probabilities (her relative marginal rates of substitution between states) and her local risk preferences are encoded in the matrix of derivatives of the risk-neutral probabilities. This matrix plays a central but generally unappreciated role in the modeling of risk attitudes in the state-preference framework. It can be computed by inverting a bordered Slutsky matrix and vice versa, it generalizes the Arrow?CPratt measure for approximating local risk premia, and its structure reveals whether the decision maker??s risk preferences are ambiguity averse as well as risk-averse. Two versions of the smooth ambiguity model are analyzed??the source-dependent risk aversion model and the second-order uncertainty (KMM) model??and it is shown that in both cases, the overall premium for local uncertainty can be decomposed as the sum of a risk premium and an ambiguity premium.     

The maximum entropy moment matrix with missing values

The occurrence of missing values for one or several variables has the effect of adding a ridge along the diagonal of their maximum entropy (ME) covariance matrix. This is a second ridge in addition to the usual ridge of the ME covariance matrix.     

Principal Component Models for Generating Large GARCH Covariance Matrices

The implementation of multivariate GARCH models in more than a few dimensions is extremely difficult: because the model has many parameters, the likelihood function becomes very flat, and consequently the optimization of the likelihood becomes practicably impossible. There is simply no way that full multivariate GARCH models can be used to estimate directly the very large covariance matrices that are required to net all the risks in a large trading book. This paper begins by describing the principal component GARCH or 'orthogonal GARCH' (O-GARCH) model for generating large GARCH covariance matrices that was first introduced in Alexander and Chibumba (1996) and subsequently developed in Alexander (2000, 2001b). The O-GARCH model is an accurate and efficient method for generating large covariance matrices that only requires the estimation of univariate GARCH models. Hence, it has many practical advantages, for example in value–at–risk models. It works best in highly correlated systems, such as term structures. The purpose of this paper is to show that, if sufficient care is taken with the initial calibration of the model, equities and foreign exchange rates can also be included in one large covariance matrix. Simple conditions for the final covariance matrix to be positive semi-definite are derived.
(J.E.L.: C32, C53, G19, G21, G28).     

A locally linear estimation of regression discontinuity

In this paper, we propose a locally linear estimation of a regression discontinuity model. The proposed estimator is applicable to evaluation of the effectiveness of the program treatment, and it improves upon the existing literature by providing not just the treatment effect at discontinuity but also insight of the treatment effect on those near discontinuity. Under some familiar conditions, we establish the consistency and asymptotic normality of the proposed estimator. We also provide an easy to compute consistent covariance matrix.     

Strategic games beyond expected utility

This paper argues that Nash equilibrium is a solution where all strategic uncertainty has been resolved and, therefore, inappropriate to model situations that involve ??ambiguity.?? Instead, to capture what players will do in the presence of some strategic uncertainty, takes a solution concept that is closed under best replies. It is shown that such a solution concept, fixed sets under the best reply correspondence, exists for a class of games significantly wider than those games for which generalizations of Nash equilibrium exist. In particular, this solution can do without the expected utility hypothesis.     

Sources of employment fluctuations in Taiwan's industries and regions

This empirical study explores the sources of employment fluctuations in Taiwan's industries and regions over the period 1978 to 2004. The quarterly growth rates of employment in nine industries and four regions are modelled with a structural vector autoregression (VAR), and the employment shocks are measured by VAR residuals. The covariance matrix of the VAR residuals is decomposed using system estimation method that selects the parameters to make the error model close to the covariance matrix and, in turn, to estimate the relative importance of national as well as industry-specific and region-specific shocks. The empirical results show that industry-specific shocks account for the major fluctuations in industries and regions. On average, about 83.95% of an industry's cyclical variations and 56.28% of the volatility in a region may be attributed to industry-specific shocks. National shocks account for little employment volatility in industries. Only the finance and personal service industries are highly sensitive to national shocks.     

SEMINONPARAMETRIC MAXIMUM LIKELIHOOD ESTIMATION OF CONDITIONAL MOMENT RESTRICTION MODELS*

This article studies estimation of a conditional moment restriction model with the seminonparametric maximum likelihood approach proposed by Gallant and Nychka (Econometrica 55 (March 1987), 363–90). Under some sufficient conditions, we show that the estimator of the finite dimensional parameter θ is asymptotically normally distributed and attains the semiparametric efficiency bound and that the estimator of the density function is consistent under L₂ norm. Some results on the convergence rate of the estimated density function are derived. An easy to compute covariance matrix for the asymptotic covariance of the θ estimator is presented.     

Learning Under Ambiguity

This paper considers learning when the distinction between risk and ambiguity matters. It first describes thought experiments, dynamic variants of those provided by Ellsberg, that highlight a sense in which the Bayesian learning model is extreme—it models agents who are implausibly ambitious about what they can learn in complicated environments. The paper then provides a generalization of the Bayesian model that accommodates the intuitive choices in the thought experiments. In particular, the model allows decision-makers' confidence about the environment to change—along with beliefs—as they learn. A portfolio choice application compares the effect of changes in confidence under ambiguity vs. changes in estimation risk under Bayesian learning. The former is shown to induce a trend towards more stock market participation and investment even when the latter does not.     

Why uncertainty matters: discounting under intertemporal risk aversion and ambiguity

Uncertainty has an almost negligible impact on project value in the standard economic model. I show that a comprehensive evaluation of uncertainty and uncertainty attitude changes this picture fundamentally. The illustration of this result relies on the discount rate, which is the crucial determinant in balancing immediate costs against future benefits, and the single most important determinant of optimal mitigation policies in the integrated assessment of climate change. First, the paper removes an implicit assumption of (intertemporal or intrinsic) risk neutrality from the standard economic model. Second, the paper introduces aversion to non-risk uncertainty (ambiguity). I show a close formal similarity between the model of intertemporal risk aversion, which is a reformulation of the widespread Epstein–Zin–Weil model, and a recent model of smooth ambiguity aversion. I merge the models, achieving a threefold disentanglement between risk aversion, ambiguity aversion, and the propensity to smooth consumption over time.     

Biased managers as strategic commitment: The relative profit approach

This paper analyzes a duopolistic model wherein each firm׳s owner can hire a biased manager for strategic reasons. We focus on the situation wherein each firm׳s owner evaluates the performance of her/his manager on the basis of her/his relative profit, which is equal to the weighted sum of her/his absolute profit and the absolute profit of her/his opponent firm. We show that in both price-setting and quantity-setting competitions, the owners of the two private firms employ aggressive managers rather than absolute profit maximizing managers regardless of the degree of importance of each firm׳s relative performance. Furthermore, in both the price competition and the quantity competition, as the degree of importance of each firm׳s relative performance increases, we show that the firms׳ owners tend to hire more aggressive managers when the degree of importance of each firm׳s relative performance is sufficiently low, whereas in both the price competition and the quantity competition, the firms׳ owners tend to hire less aggressive managers otherwise. Thus, in both the price competition and the quantity competition, the type of each firm׳s manager is not monotone with respect to the degree of each firm׳s relative performance. Thus, in both the price competition and the quantity competition, we find that the change in the optimal type of manager hired by each firm is non-monotone against the change of competitiveness in the market with the increase in the degree of importance of each firm׳s relative performance.     

Ambiguity of scientific probability predictions and willingness-to-pay for climate change mitigation policies

We estimate a smooth ambiguity preference function, wherein an individual faces multiple probability predictions of policy outcomes, and then empirically measure their willingness-to-pay for the policy, ambiguity attitude, and ambiguity premium. Climate change mitigation policy is used as the example. The estimation results reveal that most people have ambiguity-seeking attitudes, but that these attitudes are heterogeneous across individuals. People who are older, are university graduates, have higher income, or trust more in science show stronger ambiguity-seeking attitudes. Their willingness-to-pay can be underestimated if ambiguity is not considered. Moreover, individuals with stronger ambiguity-seeking attitudes support aggressive mitigation policies more strongly. Our estimation strategy is generally applicable to policy evaluations wherein policy outcomes are ambiguous.     

A Payoff Uncertainty Explanation of Results in Experimental Centipede Games

This paper investigates how well a simple two-sided incomplete information game with a full support prior can be used to explain non-Nash equilibrium outcomes observed in the centipede game experiments. I estimate the variance of the uncertainty about preferences in several versions of the model, select two models, and compare these models to the estimation results of the altruism model and the quantal response models. It is shown that the two selected models have a better fit than the one-parameter altruism and quantal response models and that the estimated variance can explain all qualitative features of these experimental results.Journal of Economic LiteratureClassification Numbers: C72, C19, C 44, C91.     

Optimal portfolio positioning under ambiguity

This paper analyzes the optimality of financial portfolios when the investor has a utility with ambiguity aversion. It provides a general result about the optimal portfolio profile under ambiguity, in the Anscombe–Aumann framework, using the Maccheroni et al. (2006) approach which includes Gilboa and Schmeidler's (1989) multiple prior preferences and Hansen and Sargent's (2011) multiplier preferences. The paper then details the CRRA case with an ambiguity index based on relative entropy. Such findings have practical applications in structured portfolio management. Indeed, it is important to take account of uncertainty about the true values of financial parameters when determining the best portfolio profile.     

A system of demand equations for Austria

This paper presents results of parameter estimations of a small system of demand equations for Austria. The functional form of the equations follows the log-linear specification well known as the “Rotterdam”-System. Using annual data from 1954 to 1977 we estimate the absolute price version for a rather aggregated system consisting of four sectors of consumption expenditures. Aitken estimation with and without linear restrictions is the adopted estimation method. Tests for the validity of the general linear restrictions axe performed employing the usual criteria. Relations among the test statistics are discussed. Taking into accountBeaton's [1972] argument of the appropriate use of likelihood ratio tests we present results also after iterating on the restricted error-covariance matrix. The question of negative semidefiniteness of the matrix of price coefficients is examined by inspection of its characteristic roots and the calculation of their approximated asymptotic covariance matrix. Finally, our results are confronted with such of other comparable studies.     

Robustness and ambiguity in continuous time

We use statistical detection theory in a continuous-time environment to provide a new perspective on calibrating a concern about robustness or an aversion to ambiguity. A decision maker repeatedly confronts uncertainty about state transition dynamics and a prior distribution over unobserved states or parameters. Two continuous-time formulations are counterparts of two discrete-time recursive specifications of Hansen and Sargent (2007) [16]. One formulation shares features of the smooth ambiguity model of Klibanoff et al. (2005) and (2009) [24] and [25]. Here our statistical detection calculations guide how to adjust contributions to entropy coming from hidden states as we take a continuous-time limit.     

AGGREGATE LABOUR MARKET DYNAMICS IN HONG KONG

I specify a simple search and matching model of the labour market and estimate it on unemployment and vacancy data for Hong Kong over the period 2000–2010 using Bayesian methods. The model fits the data remarkably well. The estimation shows that productivity shocks are the main driver of fluctuations in the labour market, with cyclical movements in the separation rate playing only a subordinate role. The parameter estimates are broadly consistent with those found in the literature. To replicate the volatility of unemployment and vacancies, the model estimates require a high replacement ratio and a low bargaining power for workers, in addition to two extraneous sources of uncertainty. The estimates are robust to a relaxation of the prior information and small changes in the underlying model specification, which suggests that the data are informative and that the model is well specified. Overall, the Hong Kong labour market can be characterized by having a low degree of churning in normal times, but rapid firings and hirings in recessions and expansions.     

Average drawdown risk reduction and risk tolerances

We investigate the effects of average drawdown risk reduction on US mutual funds. Due to numerous evidence of the asymmetric distribution of portfolio returns, the asymmetric risk measures have extensively been used in risk management during the recent decades with extensive usages on the n-degree lower partial moment (LPM) methodology. Unlike the previous literature, we use the n-degree average drawdown risk measure, which is a special case of n-degree LPM, to empirically investigate the impacts of n-degree average drawdown risk reduction on the risk tolerances generated by the US mutual funds.The evidence shows that skewness does not impose any significant problem on the n-degree A-DRM model. Moreover, the effect of changing the tolerances of average drawdown risk in the n-degree A-DRM models is a reduction in the fund returns. The n-degree CA-DRM optimization model reduces investors׳ risk more than other models. Thus, the A-DRM can be accommodated with risk-averse investors׳ approach. The efficient set of mean–variance choices from the investment opportunity set, as described by Markowitz, shows that the n-degree CA-DRM algorithms create this set with lower risk than other algorithms. It implies that the mean–variance opportunity set generated by the n-degree CA-DRM creates lower risk for a given return than covariance and CLPM.     

A behavioral characterization of plausible priors

Recent decision theories represent ambiguity via multiple priors, interpreted as alternative probabilistic models of the relevant uncertainty. This paper provides a robust behavioral foundation for this interpretation. A prior P is “plausible” if preferences over some subset of acts admit an expected utility representation with prior P, but not with any other prior Q≠P. Under suitable axioms, plausible priors can be elicited from preferences, and fully characterize them; also, probabilistic sophistication implies that there exists only one plausible prior; finally, “plausible posteriors” can be derived via Bayesian updating. Several familiar decision models are consistent with the proposed axioms.