Operations Research
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Operations Research2015-07-25Walter FrischBayesian Variable Selection in Spatial Autoregressive Models
http://d.repec.org/n?u=RePEc:wiw:wiwwuw:wuwp199&r=ore
This paper compares the performance of Bayesian variable selection approaches for spatial autoregressive models. We present two alternative approaches which can be implemented using Gibbs sampling methods in a straightforward way and allow us to deal with the problem of model uncertainty in spatial autoregressive models in a flexible and computationally efficient way. In a simulation study we show that the variable selection approaches tend to outperform existing Bayesian model averaging techniques both in terms of in-sample predictive performance and computational efficiency.Jesus Crespo Cuaresma, Philipp Piribauer2015-07spatial autoregressive model, variable selection, model uncertainty, Markov chain Monte Carlo methodsThe South African Economic Response to Monetary Policy Uncertainty
http://d.repec.org/n?u=RePEc:pre:wpaper:201551&r=ore
We study the evolution of monetary policy uncertainty and its impact on the South African economy. We show that volatility is high and constant using a stochastic volatility model in a sign-restricted VAR setup. Stochastic volatility is model driven and there is an endogenous economic response to uncertainty. Both inflation and interest rates decline in response to uncertainty. Output rebounds quickly after a contemporaneous decrease. We study the transmission mechanism of uncertainty for South Africa using a nonlinear DSGE model. The model is calibrated based on the existing literature while the persistence and size of uncertainty is taken from the empirical VAR. The DSGE model shows that the size of the uncertainty shock matters - high uncertainty can lead to a severe contraction in output, inflation and interest rates.Mehmet Balcilar, Rangan Gupta, Charl Jooste2015-07Uncertainty, nonlinear DSGE, stochastic volatilityModeling and Forecasting Carbon Dioxide Emission Allowance Spot Price Volatility: Multifractal vs. GARCH-Type Volatility Models
http://d.repec.org/n?u=RePEc:pre:wpaper:201550&r=ore
This paper applies Markov-switching multifractal (MSM) processes to model and forecast carbon dioxide (CO2) emission price volatility, and compares their forecasting performance to the standard GARCH, fractionally integrated GARCH (FIGARCH) and the two-state Markov-switching GARCH (MS-GARCH) models via three loss functions (the mean squared error, the mean absolute error and the value-at-risk). We evaluate the performance of these models via the superior predictive ability test. We find that the forecasts based on the MSM model cannot be outperformed by its competitors under the vast majority of criteria and forecast horizons, while MS-GARCH mostly comes out as the least successful model. Applying various VaR backtesting procedures, we do, however, not find significant differences in the performance of the candidate models under this particular criterion. We also find that we cannot reject the null hypothesis of MSM forecasts encompassing those of GARCH-type models. In line with this result, optimally combined forecasts do indeed hardly improve upon the best single models in our sample.Mawuli Segnon, Thomas Lux, Rangan Gupta2015-07Carbon dioxide emission allowance prices, GARCH, Markov-switching GARCH, FIGARCH, Multifractal Processes, SPA test, encompassing test, BacktestingCharitable Behaviour and the Big Five Personality Traits: Evidence from UK Panel Data
http://d.repec.org/n?u=RePEc:shf:wpaper:2015017&r=ore
Sarah Brown, Karl Taylor2015-06Charitable donations; Volunteering; Personality traits; Tobit model; Censored quantile regression.Revisiting Nash Wages Negotiations in Matching Models
http://d.repec.org/n?u=RePEc:cir:cirwor:2015s-29&r=ore
In labour economics theory, wage negotiations use to rely on a SymmetricNash Bargaining Solution. This article aims at showing that this kind of solution may be not relevant. Indeed, in a matching model framework, the comparison with the Kalai-Smorodinsky Solution suggests that a reflection should systematically be made with respect to the negotiation power of each agent.Samir Amine, Sylvain Baumann, Pedro Lages Dos Santos, Fabrice Valognes2015-07-13The Performance of Conditional CAPMs based on Evidence from the European Union’s (EU) Financial Stock Markets before and after the Eurozone Financial Crisis
http://d.repec.org/n?u=RePEc:sek:iacpro:2604617&r=ore
This paper focuses on identifying the stochastic behavior of financial stock markets for the purpose of making profitable investment decisions. A time-varying version of the Linear Market Model (consistent with a conditional Capital Asset Pricing Model (CAPM)) which allows only for the time-varying beta risk parameter is the benchmark market model for this research. To validate and extend the time-varying Linear Market Model, two related extensions are defined. These are newly formulated forms of the time-varying Higher order Market Models (consistent with their equivalent conditional Higher order CAPMs (Neslihanoglu, 2014)) and are simple polynomial extensions of the time-varying Linear Market Model; namely, the time-varying Quadratic Market Model (which allows for the time-varying beta and time-varying co-skewness risk parameters) and the time-varying Cubic Market Model (which allows for the time-varying beta, time-varying co-skewness, and time-varying co-kurtosis risk parameters). Here, the time-varying risk parameters are estimated using the state space model. The data is based on several EU area financial stock markets before and after the Eurozone financial crisis as well as on forecasting made 2 years into the future. The empirical results found support the time-varying Linear Market Model which allows only for the time-varying beta risk parameter when modeling and forecasting EU area financial stock markets.Serdar NeslihanogluCAPM, EU Countries, Higher-Order Moments, State Space Model, Systematic Risk Measure Parameters"Pricing Average and Spread Options on Commodities under Local-Stochastic Volatility with Jumps Models"
http://d.repec.org/n?u=RePEc:tky:fseres:2015cf980&r=ore
This paper presents a new approximation formula for pricing discretely monitored av- erage options and spread options in a local-stochastic volatility (LSV) model with jumps. Particularly, our model includes local-volatility functions and jump components in both the underlying asset price and its volatility processes. To the best of our knowledge, the pro- posed approximation is the rst one which achieves analytic approximations for the average and spread option prices in this environment. In numerical experiments, by employing several models we provide approximate prices for average and calendar spread options on the WTI futures based on the parameters through calibration to the listed (plain-vanilla) futures option prices, and compare those with the CME settlement prices, which conrms the validity of the method. Moreover, we show the LSV with jumps model is able to replicate consistently and pre- cisely listed futures option, calendar spread option and average option prices with common parameters.--Kenichiro Shiraya, YAkihiko Takahashi2015-07Risk Assessment of Input Uncertainty in Stochastic Simulation
http://d.repec.org/n?u=RePEc:arx:papers:1507.06015&r=ore
When simulating a complex stochastic system, the behavior of the output response depends on the input parameters estimated from finite real-world data, and the finiteness of data brings input uncertainty to the output response. The quantification of the impact of input uncertainty on output response has been extensively studied. Most of the existing literature focuses on providing inferences on the mean output response with respect to input uncertainty, including point estimation and confidence interval construction of the mean response. However, risk assessment of the mean response with respect to input uncertainty often plays an important role in system evaluation/control because it quantifies the behavior of the mean response under extreme input models. To the best of our knowledge, it has been rarely systematically studied in the literature. In the present paper, we will fill in the gap and introduce risk measures for input uncertainty in output analysis. We develop nested Monte Carlo estimators and construct (asymptotically valid) confidence intervals for risk measures of mean response. We further study the associated budget allocation problem for more efficient nested simulation of the estimators, and propose a novel method to solve the problem.Helin Zhu, Enlu Zhou2015-07Revisiting non-linearities in business cycles around the world
http://d.repec.org/n?u=RePEc:pra:mprapa:65668&r=ore
We use first differenced logged quarterly series for the GDP of 29 countries and the euro area to (re)assess the need to use nonlinear models to describe business cycle dynamic behaviour. Our approach is model (estimation)-free, based on testing only. We aim to maximize power to detect non-linearities and, simultaneously, we purport avoiding the pitfalls of data mining. We find evidence supporting the presence of significant non-linearities in 2/3 of the cases only. Hence, it does not provide full support to some descriptions. Linear models cannot be simply dismissed as they are sometimes useful and in many cases they do not seem to leave a substantial fraction of variation to be explained by nonlinear rivals. Nonlinear business cycle variation does not seem to be an universal, undisputable and clearly dominant stylized fact. Therefore, our evidence broadly agrees with the one that has recently emerged from the ``features approach''. Some support for nonlinear dynamics for some further countries is obtained indirectly, through unit root tests, but this marginal to our study, based on indirect methods only and can hardly be invoked to support nonlinearity in classical business cycles. However, it is relevant from the output gap perspective.Silva Lopes, Artur C., Florin Zsurkis, Gabriel2015-06-15business cycles; nonlinear time series models; testing.Symmetric equilibria in stochastic timing games
http://d.repec.org/n?u=RePEc:bie:wpaper:543&r=ore
We construct subgame-perfect equilibria with mixed strategies for symmetric stochastic timing games with arbitrary strategic incentives. The strategies are qualitatively different for local first- or second-mover advantages, which we analyse in turn. When there is a local second-mover advantage, the players may conduct a war of attrition with stopping rates that we characterize in terms of the Snell envelope from the general theory of optimal stopping, which is very general but provides a clear interpretation. With a local first-mover advantage, stopping typically results from preemption and is abrupt. Equilibria may differ in the degree of preemption, precisely at which points it is triggered. We provide an algorithm to characterize where preemption is inevitable and to establish the existence of corresponding payoff-maximal symmetric equilibria.Steg, Jan-Henrik2015-07-17Stochastic timing games, mixed strategies, subgame perfect equilibrium, Snell envelope, optimal stopping