Publisher Correction: Real options signaling game models for dynamic acquisition under information asymmetry

Decisions in Economics and Finance - In the original publication, the copyright holder was incorrectly published as ‘Springer-Verlag Italia S.r.l., part of Springer Nature’ instead of...     

Real options game analysis of sleeping patents

Using the real options game approach, we analyze the two-stage preemptive patent-investment race between an incumbent and a challenger (new entrant) in a product market with profit flow uncertainty. The challenger can gain entry into the monopolized product market dominated by the incumbent by patenting related technologies for a substitute product. To maintain its monopolized advantage, the incumbent has an incentive to block challenger’s entry by patenting the substitute product before the challenger. Either firm can pay an upfront fee to gain an immediate acquisition of the patent and subsequently holding the real option to develop the new substitute product. Under our simplified real options game model, there is no potential patent litgation risk. Also, the costs of holding the patent, like the payment of annual fees, are taken to be zero. We provide a full characterization of the optimal strategies adopted by the incumbent and challenger firm in this asymmetric patent-investment race. In particular, we examine the phenomena of sleeping patent, where the patented product is not launched immediately into the product market.     

Real options game models of R&D competition between asymmetric firms with spillovers

Using real options game models, we consider the characterization of strategic equilibria associated with an asymmetric Research and Development (R&D) race between an incumbent firm and an entrant firm in the development of a new innovative product under market and technological uncertainties. The random arrival time of the discovery of the patent protected innovative product is modeled as a Poisson process. Input spillovers on the R&D effort are modeled by the change in the leader’s hazard rate of success of innovation upon the follower’s entry into the R&D race. Asymmetry between the two competing firms include sunk costs of investment, stochastic revenue flow rates generated from the product, and hazard rates of arrival of success of R&D efforts of the two firms. Under asymmetric duopoly, we obtain the complete characterization of the three types of Markov perfect equilibria (sequential leader–follower, preemption and simultaneous entry) of the firms’ optimal R&D entry decisions with respect to various sets of model parameters. Our model shows that under positive input spillover, preemptive equilibrium does not occur in the R&D race due to the presence of dominant second mover advantage. The two firms choose optimally to enter simultaneously if the sunk cost asymmetry is relatively small; otherwise, sequential equilibrium would occur. When the initial hazard rate is low relative to the level of input spillover, simultaneous entry would occur as an optimal decision, signifying another scenario of dominant second mover advantage. On the other hand, when the initial hazard rate is sufficiently high so that the first mover advantage becomes more significant, simultaneous equilibrium does not occur even under high level of positive input spillover.     

Strategic real options under asymmetric information

This paper studies the effect of private information on the capital allocation decisions of firms who operate under imperfect competition. I analyze two interactive firms, one with private information and the other without, who must decide when to undertake an irreversible and uncertain investment decision. Traditional non-strategic models of irreversible investment under uncertainty involve a single decision maker and result in an optimal period of delay before the investment is undertaken. In a strategic setting, firms must balance their desire to delay against competitive advantages from early investment. I find that an equilibrium may not exist within the standard continuous framework when the private information is over revenues. Moreover, when an equilibrium does exist the competitive pressures from the uninformed firm are weak. This is in contrast to existing models with asymmetric information over costs, where an equilibrium always exists and the competitive pressures remain strong (Hsu and Lambrecht, 2007). This work shows that the investment timing decision, and thus the value of the private information, is highly sensitive to the nature of incomplete information.     

Fisher information under Gaussian quadrature models

This paper develops formulae to compute the Fisher information matrix for the regression parameters of generalized linear models with Gaussian random effects. The Fisher information matrix relies on the estimation of the response variance under the model assumptions. We propose two approaches to estimate the response variance: the first is based on an analytic formula (or a Taylor expansion for cases where we cannot obtain the closed form), and the second is an empirical approximation using the model estimates via the expectation–maximization process. Further, simulations under several response distributions and a real data application involving a factorial experiment are presented and discussed. In terms of standard errors and coverage probabilities for model parameters, the proposed methods turn out to behave more reliably than does the ‘disparity rule’ or direct extraction of results from the generalized linear model fitted in the last expectation–maximization iteration.     

Numerical solution of dynamic equilibrium models under Poisson uncertainty

We propose a simple and powerful numerical algorithm to compute the transition process in continuous-time dynamic equilibrium models with rare events. In this paper we transform the dynamic system of stochastic differential equations into a system of functional differential equations of the retarded type. We apply the Waveform Relaxation algorithm, i.e., we provide a guess of the policy function and solve the resulting system of (deterministic) ordinary differential equations by standard techniques. For parametric restrictions, analytical solutions to the stochastic growth model and a novel solution to Lucas' endogenous growth model under Poisson uncertainty are used to compute the exact numerical error. We show how (potential) catastrophic events such as rare natural disasters substantially affect the economic decisions of households.     

Bias in dynamic panel models under time series misspecification

We consider within-group estimation of higher-order autoregressive panel models with exogenous regressors and fixed effects, where the lag order is possibly misspecified. Even when disregarding the misspecification bias, the fixed-effect bias formula is quite different from the correctly specified case though its asymptotic order remains the same under the stationarity. We suggest bias reduction methods under the possible time series misspecification.     

Multi-player dynamic game model for Bitcoin transaction bidding prediction

With the rapid rise of cryptocurrencies, it has become an urgent problem to realize the flat use of digital currency, with making it really put into use, and giving full play to its utility in the current economic market. This paper innovatively takes the maximization of user benefit as the key point to predict transaction bidding price combining dynamic game theory. The bidding price of user transaction not only refers to historical transactions, but also considers the impact on future subsequences, and the result describes the interaction between transactions in detail. Also this paper proposes a method to express user satisfaction and establishes a user benefit model accordingly, so as to ensure the transaction is packaged successfully to the greatest extent within the acceptable range of transaction pricing. Finally this paper compares the proposed model with conventional machine learning prediction algorithms, finding that when user does not participate in the trading for the first time, the prediction effect of this proposal is better than that of machine learning over small data sets, moreover superior to machine learning methods in prediction accuracy and sensitivity, with a lower time complexity.     

A unified approach to Bermudan and barrier options under stochastic volatility models with jumps

Many financial assets, such as currencies, commodities, and equity stocks, exhibit both jumps and stochastic volatility, which are especially prominent in the market after the financial crisis. Some strategic decision making problems also involve American-style options. In this paper, we develop a novel, fast and accurate method for pricing American and barrier options in regime switching jump diffusion models. By blending regime switching models and Markov chain approximation techniques in the Fourier domain, we provide a unified approach to price Bermudan, American options and barrier options under general stochastic volatility models with jumps. The models considered include Heston, Hull–White, Stein–Stein, Scott, the 3/2 model, and the recently proposed 4/2 model and the α-Hypergeometric model with general jump amplitude distributions in the return process. Applications include the valuation of discretely monitored contracts as well as continuously monitored contracts common in the foreign exchange markets. Numerical results are provided to demonstrate the accuracy and efficiency of the proposed method.     

Indirect inference for dynamic panel models

Maximum likelihood (ML) estimation of the autoregressive parameter of a dynamic panel data model with fixed effects is inconsistent under fixed time series sample size and large cross section sample size asymptotics. This paper proposes a general, computationally inexpensive method of bias reduction that is based on indirect inference, shows unbiasedness and analyzes efficiency. Monte Carlo studies show that our procedure achieves substantial bias reductions with only mild increases in variance, thereby substantially reducing root mean square errors. The method is compared with certain consistent estimators and is shown to have superior finite sample properties to the generalized method of moment (GMM) and the bias-corrected ML estimator.     

Planning models for non-cooperative situations: A two-player game approach

In the analysis of urban systems, attempts are usually made to subdivide the system into a set of interest groups, according to the.pursuit of certain common purposes. The behaviour of each group is described in terms on an objective and a set of variables under its control. Groups may be linked through the occurrence of variables controlled by other groups either in their objectives, in their constraints, or both. In this paper, the problem is addressed, whereby equilibrium states are sought for situations where one group is the planning authority, exercising policy instruments to influence system outcome at a level different to that required to reasonably describe the macro behaviour or objectives of all groups in the system, including the authority itself. The principles are illustrated on some specific models, and alternative means of handling various classes of intergroup linkages are investigated.     

Valuation of options on the maximum of two prices with default risk under GARCH models

In this paper, we work under GARCH models to value options on the maximum or the minimum of two prices. In addition, we consider not only two underlying asset prices but also geometric average ones. Further, default risk is also incorporated in a reduced-form model. In the proposed framework, closed-form pricing formulae of options on the maximum with or without default risk are derived and then used to perform numerical examples.     

Convergence rates of recombining trees for pricing options on stocks under GBM and regime-switching models with known cash dividends

In the literature there appear various kinds of binomial trees for pricing options on stocks under geometric Brownian motions (GBMs) with known cash dividends. The aim of this paper is to compare the performance of the existing binomial trees in aspect of the convergence rates, which are usually used to measure precisely how fast the approximate values converge to the exact one, and to give a theoretical proof of the convergence rates for the interpolation binomial trees which are based on a model that excludes the arbitrage possibilities. Also the paper extends the studies to the regime-switching models with known cash dividend payment.     

Dynamic specification tests for dynamic factor models

We derive computationally simple expressions for score tests of misspecification in parametric dynamic factor models using frequency domain techniques. We interpret those diagnostics as time domain moment tests which assess whether certain autocovariances of the smoothed latent variables match their theoretical values under the null of correct model specification. We also reinterpret reduced‐form residual tests as checking specific restrictions on structural parameters. Our Gaussian tests are robust to nonnormal, independent innovations. Monte Carlo exercises confirm the finite‐sample reliability and power of our proposals. Finally, we illustrate their empirical usefulness in an application that constructs a US coincident indicator.     

A game theoretic approach to optimize multi-stakeholder utilities for land acquisition negotiations with informality

To combat the critical stresses of rising urbanization, the government acquires land from private owners using the power of eminent domain. This land assembly causes negative externalities such as increasing social tension and injustice that may impose a long-term threat to stability and sustainable development. Therefore, there is a need to make the land acquisition process more transparent and just. Considering the unique Indian context where informality has a strong presence, we propose an approach based on game theory that models the bargain through a three-stage Nash equilibrium game. Four agents – the government, the private developer, the landowner, and the free rider – are considered. We provide conditional solutions for the generalizable Case and proceed to model different stakeholder behavior patterns through two utility functional forms – linear and exponential. In the linear case, we find that the free rider obtains half of the revenue of the project, whereas the landowner gains between one-fourth and one-half of the revenue. Thus, we highlight the undeniably crucial role free riders play in land acquisition negotiations. However, closed form solutions cannot be obtained for the exponential form, due to which we use simulations to demonstrate a solution procedure. We conclude by stating that the proposed model can be useful in formulating future land policies in a sustainable and inclusive manner, with optimal utility derivations for all concerned stakeholders. Our model can also be extended to other spatial contexts where informality features heavily in the land market, especially in the Global South.     

Online doctor-patient dynamic stable matching model based on regret theory under incomplete information

With the development of the Internet era, online medical community is increasingly favored by patients and health care workers. How to make a reasonable doctor-patient matching and meet patients' relevant needs through online medical platform is the key to realizing effective allocation of medical resources. This paper proposes an online doctor-patient dynamic stable matching model to deeply investigate the online doctor-patient matching problem under incomplete information. Firstly, we assign the score to each doctor based on the patient's attribute expectations and the doctor's evaluation information. Considering the difficulty of obtaining complete information in real life and different attribute representations, we propose incomplete information filling methods based on collaborative filtering algorithm. To make the filling result more reasonable, we introduce probabilistic linguistic information entropy to process the incomplete information. Secondly, considering psychological behavior, we use regret theory to calculate patients' satisfaction with doctors. Finally, with the definitions of rational matching and stable matching, we propose a doctor-patient dynamic stable matching model. This model can solve the problems like a temporary appointment and private matching and ensure the stability of matching. In addition, the model proposed in this paper considers not only patients' but also the doctors' needs. The case of online doctor-patient matching is based on “haodf.com”, and through comparative analysis can prove the effectiveness and superiority of this method.     

Nonparametric tests for conditional symmetry in dynamic models

This article proposes omnibus tests for conditional symmetry around a parametric function in a dynamic context. Conditional moments may not exist or may depend on the explanatory variables. Test statistics are suitable functionals of the empirical process of residuals and explanatory variables, whose limiting distribution under the null is nonpivotal. The tests are implemented with the assistance of a bootstrap method, which is justified assuming very mild regularity conditions on the specification of the center of symmetry and the underlying serial dependence structure. Finite sample properties are examined by means of a Monte Carlo experiment.     

Bayesian network models for incomplete and dynamic data

Bayesian networks are a versatile and powerful tool to model complex phenomena and the interplay of their components in a probabilistically principled way. Moving beyond the comparatively simple case of completely observed, static data, which has received the most attention in the literature, in this paper, we will review how Bayesian networks can model dynamic data and data with incomplete observations. Such data are the norm at the forefront of research and in practical applications, and Bayesian networks are uniquely positioned to model them due to their explainability and interpretability.     

Artificial stock markets with different maturity levels: simulation of information asymmetry and herd behavior using agent-based and network models

This paper aims mainly at building artificial stock markets with different maturity levels by modeling information asymmetry and herd behavior. The developed artificial markets are multi-assets, order-driven and populated by agents having heterogeneous behaviors and information. Agents are defined by their information and their herd behavior levels. Agents trade multiple risky assets based on their wealth, their behaviors and their available information which spread among multiple behavioral networks. In a novel contribution to artificial stock markets literature, agents’ behaviors modeling is mixed with social network simulation to reproduce different degrees of information asymmetry and herd behavior based on several assortative topologies. Several simulations validated the proposed model since univariate and multivariate stylized facts were reproduced both for mature and immature stock markets. The proposed artificial stock market can be considered as a first step toward decision and simulation tools for optimal management, strategy analysis and predictions evolution of immature stock markets.