Optimal hedging and parameter uncertainty

We explore the impact of drift parameter uncertainty in a basis risk model, an incomplete market in which a claim on a non-traded asset is optimally hedged using a correlated traded stock. Using analytic expansions for indifference prices and hedging strategies, we develop an efficient procedure to generate terminal hedging error distributions when the hedger has erroneous estimates of the drift parameters. These show that the effect of parameter uncertainty is occasionally benign, but often very destructive. In light of this, we develop a filtering approach in which the hedger updates her parameter estimates from observations of the asset prices, and we find an analytic soultion to the hedger's combined filtering and control problem in the case that the drift of the traded asset is known with certainty

Dynamic Incentive Contracts under Parameter Uncertainty

We analyze a long-term contracting problem involving common uncertainty about a parameter capturing the productivity of the relationship, and featuring a hidden action for the agent. We develop an approach that works for any utility function when the parameter and noise are normally distributed and when the effort and noise affect output additively. We then analytically solve for the optimal contract when the agent has exponential utility. We find that the Pareto frontier shifts out as information about the agent's quality improves. In the standard spot-market setup, by contrast, when the parameter measures the agent's "quality", the Pareto frontier shifts inwards with better information. Commitment is therefore more valuable when quality is known more precisely. Incentives then are easier to provide because the agent has less room to manipulate the beliefs of the principal. Moreover, in contrast to results under one-period commitment, wage volatility declines as experience accumulates.principal-agent model, optimal contract, learning, private information, reputation, career

Diagnostics of Data-Driven Models: Uncertainty Quantification of PM7 Semi-Empirical Quantum Chemical Method.

We report an evaluation of a semi-empirical quantum chemical method PM7 from the perspective of uncertainty quantification. Specifically, we apply Bound-to-Bound Data Collaboration, an uncertainty quantification framework, to characterize (a) variability of PM7 model parameter values consistent with the uncertainty in the training data and (b) uncertainty propagation from the training data to the model predictions. Experimental heats of formation of a homologous series of linear alkanes are used as the property of interest. The training data are chemically accurate, i.e., they have very low uncertainty by the standards of computational chemistry. The analysis does not find evidence of PM7 consistency with the entire data set considered as no single set of parameter values is found that captures the experimental uncertainties of all training data. A set of parameter values for PM7 was able to capture the training data within ±1 kcal/mol, but not to the smaller level of uncertainty in the reported data. Nevertheless, PM7 was found to be consistent for subsets of the training data. In such cases, uncertainty propagation from the chemically accurate training data to the predicted values preserves error within bounds of chemical accuracy if predictions are made for the molecules of comparable size. Otherwise, the error grows linearly with the relative size of the molecules

Keeping Negotiations in the Dark: Environmental Agreements under Incomplete Information

This paper investigates the role of uncertainty as a tool to support cooperation in international environmental agreements. We consider two layers of uncertainty. Under unilateral uncertainty treaties become successful with positive probability in the signaling game, even under parameter conditions for which no agreement is reached under complete information. Under bilateral uncertainty, a separating equilibrium emerges where the leader participates in the treaty only when its environmental concerns are high. We show that the agreement is signed for larger sets of parameter values under unilateral uncertainty. We then show that further layers of uncertainty might enhance social welfare.Signaling games; Unilaterial uncertainty; Bilateral uncertainty; Non-binding negotiations

Optimal Policy Under Model Uncertainty: A Structural-Bayesian Estimation Approach

In this paper we propose a novel methodology to analyze optimal policies under model uncertainty in micro-founded macroeconomic models. As an application we assess the relevant sources of uncertainty for the optimal conduct of monetary policy within (parameter uncertainty) and across models (specification uncertainty) using EU 13 data. Parameter uncertainty matters only if the zero bound on interest rates is explicitly taken into account. In any case, optimal monetary policy is highly sensitive with respect to specification uncertainty implying substantial welfare gains of a robustly-optimal rule that incorporates this risk.Optimal monetary policy, model uncertainty, Bayesian model estimation.

Welfare-maximizing monetary policy under parameter uncertainty

This paper examines welfare-maximizing monetary policy in an estimated micro-founded general equilibrium model of the U.S. economy where the policymaker faces uncertainty about model parameters. Uncertainty about parameters describing preferences and technology implies not only uncertainty about the dynamics of the economy. It also implies uncertainty about the model's utility-based welfare criterion and about the economy's natural rate measures of interest and output. We analyze the characteristics and performance of alternative monetary policy rules given the estimated uncertainty regarding parameter estimates. We find that the natural rates of interest and output are imprecisely estimated. We then show that, relative to the case of known parameters, optimal policy under parameter uncertainty responds less to natural-rate terms and more to other variables, such as price and wage inflation and measures of tightness or slack that do not depend on natural rates.Monetary policy