State-Dependent Nominal Rigidities & Disinflation Programs in Small Open Economies

Empirical regularities from high-inflation economies, especially in Latin America, suggest that exchange rate-based (ERB) disinflations and money-based (MB) disinflations induce sharply different dynamics in consumption and GDP. I study the role of nominal rigidities to explain business cycle fluctuations associated to ERB and MB disinflations within a single framework. By building on Calvo's (1983) pricing theory, this paper introduces elements of state-dependent pricing at the firm level into an otherwise standard small open economy model. This new feature allows for endogenous variations in the aggregate degree of nominal rigidities. The model contains as a special case a time- dependent pricing model discussed in the literature. Nonlinear simulations show that the model with state-dependent nominal rigidities generates a dynamic behavior that is more consistent with the empirical evidence, compared to the model with time-dependent pricing.

Contextual Dynamic Pricing with Strategic Buyers

Personalized pricing, which involves tailoring prices based on individual characteristics, is commonly used by firms to implement a consumer-specific pricing policy. In this process, buyers can also strategically manipulate their feature data to obtain a lower price, incurring certain manipulation costs. Such strategic behavior can hinder firms from maximizing their profits. In this paper, we study the contextual dynamic pricing problem with strategic buyers. The seller does not observe the buyer's true feature, but a manipulated feature according to buyers' strategic behavior. In addition, the seller does not observe the buyers' valuation of the product, but only a binary response indicating whether a sale happens or not. Recognizing these challenges, we propose a strategic dynamic pricing policy that incorporates the buyers' strategic behavior into the online learning to maximize the seller's cumulative revenue. We first prove that existing non-strategic pricing policies that neglect the buyers' strategic behavior result in a linear $\Omega(T)$ regret with $T$ the total time horizon, indicating that these policies are not better than a random pricing policy. We then establish that our proposed policy achieves a sublinear regret upper bound of $O(\sqrt{T})$. Importantly, our policy is not a mere amalgamation of existing dynamic pricing policies and strategic behavior handling algorithms. Our policy can also accommodate the scenario when the marginal cost of manipulation is unknown in advance. To account for it, we simultaneously estimate the valuation parameter and the cost parameter in the online pricing policy, which is shown to also achieve an $O(\sqrt{T})$ regret bound. Extensive experiments support our theoretical developments and demonstrate the superior performance of our policy compared to other pricing policies that are unaware of the strategic behaviors

Dynamic pricing with constant demand elasticity

The model of Gallego and van Ryzin (1994) is specialized to the case of constant elasticity of demand. A closed form is developed, which has an even simpler form than that arising with exponential demand, and possesses an excellent approximation. It is shown in this environment that monopoly is efficient, which means that all the behavior usually attributed to monopoly pricing is actually a consequence of efficient pricing and would arise even in a perfectly competitive environment. If the initial supply is not too large, it is shown that consumers have no incentive to delay their purchases in order to get a lower price at the average inventory prevailing at any time

Sequential Monte Carlo pricing of American-style options under stochastic volatility models

We introduce a new method to price American-style options on underlying investments governed by stochastic volatility (SV) models. The method does not require the volatility process to be observed. Instead, it exploits the fact that the optimal decision functions in the corresponding dynamic programming problem can be expressed as functions of conditional distributions of volatility, given observed data. By constructing statistics summarizing information about these conditional distributions, one can obtain high quality approximate solutions. Although the required conditional distributions are in general intractable, they can be arbitrarily precisely approximated using sequential Monte Carlo schemes. The drawback, as with many Monte Carlo schemes, is potentially heavy computational demand. We present two variants of the algorithm, one closely related to the well-known least-squares Monte Carlo algorithm of Longstaff and Schwartz [The Review of Financial Studies 14 (2001) 113-147], and the other solving the same problem using a "brute force" gridding approach. We estimate an illustrative SV model using Markov chain Monte Carlo (MCMC) methods for three equities. We also demonstrate the use of our algorithm by estimating the posterior distribution of the market price of volatility risk for each of the three equities.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS286 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org