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This study aims to examine a potential case of interdependence in loan and deposit interest rate setting.

The authors set up a theoretical microsimulation model with endogenous loan interest rate determination via a learning algorithm.

The authors show that in certain environments, it may be beneficial for large banks to incorporate information on retail funding costs into the lending rate setting decision.

The author’s model is based on the realistic money creation mechanism.

This paper explores abnormal price changes in the FOREX by using both daily and intraday data on the EURUSD, USDJPY, USDCAD, AUDUSD and EURJPY exchange rates over the period 01.01.2008–31.12.2018.

It applies a dynamic trigger approach to detect abnormal price changes and then various statistical methods, including cumulative abnormal returns analysis, to test the following hypotheses: the intraday behaviour of hourly returns on overreaction days is different from that on normal days (H1), there are detectable patterns in intraday price dynamics on days with abnormal price changes (H2) and on the following days (H3).

The results suggest that there are statistically significant differences between intraday dynamics on days with abnormal price changes and normal days respectively; also, prices tend to change in the direction of the abnormal change during that day, but move in the opposite direction on the following day. Finally, there exist trading strategies that generate abnormal profits by exploiting the detected anomalies, which can be seen as evidence of market inefficiency.

New evidence on abnormal price changes and related trading strategies in the FOREX.

The purpose of this paper is to provide a comprehensive survey of the literature about the use of agent-based simulation (ABS) in the study of organizational behavior, decision making, and problem-solving. It aims at contributing to the consolidation of ABS as a field of applied research in management and organizational studies.

The authors carried out a non-systematic search in literature published between 2000 and 2016, by using the keyword “agent-based” to search through Scopus’ business, management and accounting database. Additional search criteria were devised using the papers’ keywords and the categories defined by the divisions and interest groups of the Academy of Management. The authors found 181 articles for this survey.

The survey shows that ABS provides a robust and rigorous framework to elaborate descriptions, explanations, predictions and theories about organizations and their processes as well as develop tools that support strategic and operational decision making and problem-solving. The authors show that the areas that report the highest number of applications are operations and logistics (37 percent), marketing (17 percent) and organizational behavior (14 percent).

The paper illustrates the increasingly prominent role of ABS in fields such as organizational behavior, strategy, human resources, marketing and logistics. To-date, this is the most complete survey about ABS in all management areas.

This paper analyzes variations in the effects of monetary and fiscal shocks on responses of macroeconomic variables, determinacy region, and welfare costs due to changes in trend inflation.

The authors develop the New-Keynesian model, in which the central banks can employ either nominal interest rate (IR rule) or money supply (MS rule) to conduct monetary policies. They also use their capital and recurrent spending budgets to conduct fiscal policies. By using the simulated method of moment (SMM) for parameter estimation, the authors characterize Vietnam's economy during 1996Q1–2015Q1.

The results report that consequences of monetary policy and fiscal policy shocks become more serious if there is a rise in trend inflation. Furthermore, the money supply might not be an effective instrument, and using the government budget for recurrent spending produces severe consequences in the high-trend inflation economy.

This paper's findings are critical for economists and monetary and fiscal authorities in effectively designing both the monetary and fiscal policies in confronting the shift in the inflation targets.

This is the first paper that examines the effects of trend inflation on the monetary and fiscal policy implementation in the case of Vietnam.

The assets of bankrupt firms are usually sold to unsuitable buyers at an extremely discounted price. Aiming to reduce the bankruptcy cost, the purpose of this paper is to propose a novel insurance system for associated loans.

In this insurance system, the joined firms are from the same industry and have a responsibility to buy the assets of potentially bankrupt firms at a relatively high price, because they could make better use of the assets than the buyers outside the industry. Further, the authors use the Shapley value to address the problem of bankruptcy cost allocation and additionally employ the method of Monte Carlo simulation to derive the numerical solution of the insurance premium of bankruptcy cost.

First, the relatively healthy and solvent firms in the insurance system could gain a larger proportion of benefits derived from the reduced cost of default, interestingly, the more so when the external cost of default is larger. Second, given the positive relationship between bankruptcy cost and asset correlation in practice, lenders and insurers face a trade-off to balance the cost against the benefit of asset correlation. Third, insurance premiums and bankruptcy costs decrease with the number of firms participating in this insurance system.

This paper proposes a novel insurance for associated loans, in which joined firms can pay a relatively low insurance premium due to the realization of reducing bankruptcy cost.

The BEKK GARCH class of models presents a popular set of tools for applied analysis of dynamic conditional covariances. Within this class the analyst faces a range of model choices that trade off flexibility with parameter parsimony. In the most flexible unrestricted BEKK the parameter dimensionality increases quickly with the number of variables. Covariance targeting decreases model dimensionality but induces a set of nonlinear constraints on the underlying parameter space that are difficult to implement. Recently, the rotated BEKK (RBEKK) has been proposed whereby a targeted BEKK model is applied after the spectral decomposition of the conditional covariance matrix. An easily estimable RBEKK implies a full albeit constrained BEKK for the unrotated returns. However, the degree of the implied restrictiveness is currently unknown. In this paper, we suggest a Bayesian approach to estimation of the BEKK model with targeting based on Constrained Hamiltonian Monte Carlo (CHMC). We take advantage of suitable parallelization of the problem within CHMC utilizing the newly available computing power of multi-core CPUs and Graphical Processing Units (GPUs) that enables us to deal effectively with the inherent nonlinear constraints posed by covariance targeting in relatively high dimensions. Using parallel CHMC we perform a model comparison in terms of predictive ability of the targeted BEKK with the RBEKK in the context of an application concerning a multivariate dynamic volatility analysis of a Dow Jones Industrial returns portfolio. Although the RBEKK does improve over a diagonal BEKK restriction, it is clearly dominated by the full targeted BEKK model.

This paper provides household lifetime inequality indexes derived from representative U.S. labor market data. We obtain this result by using estimates of the household search model proposed by Flabbi and Mabli (2012). Inequality indexes computed on the benchmark model shows that inequality in utility values is substantially different from inequality in earnings and wages and that inequality at the cross-sectional level is significantly different from inequality at the lifetime level. Both results deliver original policy implications that would have not been captured without using our approach. In particular, we find that a counterfactual policy experiment consisting in a mean-preserving spread of the wage offers distributions increases lifetime inequality in wages and earnings but not in utility. When comparing inequality at the individual level between men and women, we find inequality in wages and earnings to be higher for husbands than wives but inequality in utility to be higher for wives. A counterfactual decomposition shows that the job offers parameters are the main source of the gender differential.

Many dynamic problems in economics are characterized by large state spaces which make both computing and estimating the model infeasible. We introduce a method for approximating the value function of high-dimensional dynamic models based on sieves and establish results for the (a) consistency, (b) rates of convergence, and (c) bounds on the error of approximation. We embed this method for approximating the solution to the dynamic problem within an estimation routine and prove that it provides consistent estimates of the modelik’s parameters. We provide Monte Carlo evidence that our method can successfully be used to approximate models that would otherwise be infeasible to compute, suggesting that these techniques may substantially broaden the class of models that can be solved and estimated.

This paper aims to introduce a new numerical approach based on the local weak form and the Petrov–Galerkin idea to numerically simulation of a predator–prey system with two-species, two chemicals and an additional chemotactic influence.

In the first proceeding, the space derivatives are discretized by using the direct meshless local Petrov–Galerkin method. This generates a nonlinear algebraic system of equations. The mentioned system is solved by using the Broyden’s method which this technique is not related to compute the Jacobian matrix.

This current work tries to bring forward a trustworthy and flexible numerical algorithm to simulate the system of predator–prey on the nonrectangular geometries.

The proposed numerical results confirm that the numerical procedure has acceptable results for the system of partial differential equations.

Online ride-hailing platforms match drivers with passengers by receiving ride requests from passengers and forwarding them to the nearest driver. In this context, the low acceptance rate of offers by drivers leads to friction in the process of driver and passenger matching. What policies by the platform may increase the acceptance rate and by how much? What factors influence drivers' decisions to accept or reject offers and how much? Are drivers more likely to turn down a ride offer because they know that by rejecting it, they can quickly receive another offer, or do they reject offers due to the availability of outside options? This paper aims to answer such questions using a novel dataset from Tapsi, a ride-hailing platform located in Iran.

The authors specify a structural discrete dynamic programming model to evaluate how drivers decide whether to accept or reject a ride offer. Using this model, the authors quantitatively measure the effect of different policies that increase the acceptance rate. In this model, drivers compare the value of each ride offer with the value of outside options and the value of waiting for better offers before making a decision. The authors use the simulated method of moments (SMM) method to match the dynamic model with the data from Tapsi and estimate the model's parameters.

The authors find that the low driver acceptance rate is mainly due to the availability of a variety of outside options. Therefore, even hiding information from or imposing fines on drivers who reject ride offers cannot motivate drivers to accept more offers and does not affect drivers' welfare by a large amount. The results show that by hiding the information, the average acceptance rate increases by about 1.81 percentage point; while, it is 4.5 percentage points if there were no outside options. Moreover, results show that the imposition of a 10-min delay penalty increases acceptance rate by only 0.07 percentage points.

To answer the questions of the paper, the authors use a novel and new dataset from a ride-hailing company, Tapsi, located in a Middle East country, Iran and specify a structural discrete dynamic programming model to evaluate how drivers decide whether to accept or reject a ride offer. Using this model, the authors quantitatively measure the effect of different policies that could potentially increase the acceptance rate.