Operations Research
http://lists.repec.orgmailman/listinfo/nep-ore
Operations Research
2017-11-12
Realized Stochastic Volatility Models with Generalized Gegenbauer Long Memory
http://d.repec.org/n?u=RePEc:ems:eureir:102576&r=ore
In recent years fractionally differenced processes have received a great deal of attention due to their exibility in nancial applications with long memory. In this paper, we develop a new re- alized stochastic volatility (RSV) model with general Gegenbauer long memory (GGLM), which encompasses a new RSV model with seasonal long memory (SLM). The RSV model uses the infor- mation from returns and realized volatility measures simultaneously. The long memory structure of both models can describe unbounded peaks apart from the origin in the power spectrum. For estimating the RSV-GGLM model, we suggest estimating the location parameters for the peaks of the power spectrum in the rst step, and the remaining parameters based on the Whittle likelihood in the second step. We conduct Monte Carlo experiments for investigating the nite sample properties of the estimators, with a quasi-likelihood ratio test of RSV-SLM model against theRSV-GGLM model. We apply the RSV-GGLM and RSV-SLM model to three stock market indices. The estimation and forecasting results indicate the adequacy of considering general long memory.
Asai, M.
McAleer, M.J.
Peiris, S.
Stochastic Volatility, Realized Volatility Measure, Long Memory, Gegenbauer Poly-nomial, Seasonality, Whittle Likelihood
2017-11-01
Forecasting compositional risk allocations
http://d.repec.org/n?u=RePEc:xrp:wpaper:xreap2017-04&r=ore
We analyse models for panel data that arise in risk allocation problems,when a given set of sources are the cause of an aggregate risk value. We focus on the modeling and forecasting of proportional contributions to risk. Compositional data methods are proposed and the regression is flexible to incorporate external information from other variables. We guarantee that projected proportional contributions add up to 100%, and we introduce a method to generate confidence regions with the same restriction. An illustration using data from the stock exchange is provided.
Tim J. Boonen
Montserrat GuillĂ©n
Miguel Santolino
Simplex, capital allocation, dynamic management.
2017-10
Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors
http://d.repec.org/n?u=RePEc:bis:biswps:667&r=ore
We develop uncertainty measures for point forecasts from surveys such as the Survey of Professional Forecasters, Blue Chip, or the Federal Open Market Committee's Summary of Economic Projections. At a given point of time, these surveys provide forecasts for macroeconomic variables at multiple horizons. To track time-varying uncertainty in the associated forecast errors, we derive a multiple-horizon speci cation of stochastic volatility. Compared to constant-variance approaches, our stochastic-volatility model improves the accuracy of uncertainty measures for survey forecasts.
Todd E Clark
Michael W McCracken
Elmar Mertens
stochastic volatility, survey forecasts, fan charts
2017-10
Simultaneous Spatial Panel Data Models with Common Shocks
http://d.repec.org/n?u=RePEc:fip:fedbqu:rpa17-3&r=ore
I consider a simultaneous spatial panel data model, jointly modeling three effects: simultaneous effects, spatial effects and common shock effects. This joint modeling and consideration of cross-sectional heteroskedasticity result in a large number of incidental parameters. I propose two estimation approaches, a quasi-maximum likelihood (QML) method and an iterative generalized principal components (IGPC) method. I develop full inferential theories for the estimation approaches and study the trade-off between the model specifications and their respective asymptotic properties. I further investigate the finite sample performance of both methods using Monte Carlo simulations. I find that both methods perform well and that the simulation results corroborate the inferential theories. Some extensions of the model are considered. Finally, I apply the model to analyze the relationship between trade and GDP using a panel data over time and across countries.
Lu, Lina
Panel data model; Spatial model; Simultaneous equations system; Common shocks; Simultaneous effects; Incidental parameters; Maximum likelihood estimation; Principal components; High dimensionality; Inferential theory
2017-08-09
Identification and Estimation of Heterogeneous Agent Models: A Likelihood Approach
http://d.repec.org/n?u=RePEc:ces:ceswps:_6717&r=ore
In this paper, we study the statistical properties of heterogeneous agent models with incomplete markets. Using a Bewley-Hugget-Aiyagari model we compute the equilibrium density function of wealth and show how it can be used for likelihood inference. We investigate the identifiability of the model parameters based on data representing a large cross-section of individual wealth. We also study the finite sample properties of the maximum likelihood estimator using Monte Carlo experiments. Our results suggest that while the parameters related to the householdâ€™s preferences can be correctly identified and accurately estimated, the parameters associated with the supply side of the economy cannot be separately identified leading to inferential problems that persist even in large samples. In the presence of partially identification problems, we show that an empirical strategy based on fixing the value of one the troublesome parameters allows us to pin down the other unidentified parameter without compromising the estimation of the remaining parameters of the model. An empirical illustration of our maximum likelihood framework using the 2013 SCF data for the U.S. confirms the results from our identification experiments.
Juan Carlos Parra-Alvarez
Olaf Posch
Mu-Chun Wang
heterogeneous agent models, continuous-time, Fokker-Planck equations, identification, maximum likelihood
2017
Proof-of-Sovereignty (PoSv) as a Method to Achieve Distributed Consensus in Crypto-Currency Networks
http://d.repec.org/n?u=RePEc:pra:mprapa:82072&r=ore
In this paper, a method to implement K-Y protocol using Distributed Consensus is discussed. Firstly, the various available methods are discussed. Then, Proof - Of - Sovereignty (PoSv) is proposed. Its mechanism is deliberated and its advantages are described vis-a-vis other methods of distributed consensus. Finally a summary of all the procedures involved in 'NationCoin Mining' is explained.
Hegadekatti, Kartik
S G, Yatish
Distributed Consensus, K-Y Protocol, Proof-of-Work
2016-09-01
Qualitative analysis of common belief of rationality in strategic-form games
http://d.repec.org/n?u=RePEc:cda:wpaper:17-5&r=ore
We study common belief of rationality in strategic-form games with ordinal utilities, employing a model of qualitative beliefs. We characterize the three main solution concepts for such games, viz., Iterated Deletion of Strictly Dominated Strategies (IDSDS), Iterated Deletion of Boergers-dominated Strategies (IDBS) and Iterated Deletion of Inferior Strategy Profiles (IDIP), by means of gradually restrictive properties imposed on the models of qualitative beliefs. As a corollary, we prove that IDIP refines IDBS, which refines IDSDS.
Giacomo Bonanno
Elias Tsakas
Qualitative likelihood relation, ordinal payoffs, common belief of rationality, iterative deletion procedures
2017-05-12
Semiparametric Estimation of Structural Functions in Nonseparable Triangular Models
http://d.repec.org/n?u=RePEc:arx:papers:1711.02184&r=ore
This paper introduces two classes of semiparametric triangular systems with nonadditively separable unobserved heterogeneity. They are based on distribution and quantile regression modeling of the reduced-form conditional distributions of the endogenous variables. We show that these models are flexible and identify the average, distribution and quantile structural functions using a control function approach that does not require a large support condition. We propose a computationally attractive three-stage procedure to estimate the structural functions where the first two stages consist of quantile or distribution regressions. We provide asymptotic theory and uniform inference methods for each stage. In particular, we derive functional central limit theorems and bootstrap functional central limit theorems for the distribution regression estimators of the structural functions. We illustrate the implementation and applicability of our methods with numerical simulations and an empirical application to demand analysis.
Victor Chernozhukov
Iv\'an Fern\'andez-Val
Whitney Newey
Sami Stouli
Francis Vella
2017-11