Report NEP-CMP-2020-07-27

This is the archive for NEP-CMP, a report on new working papers in the area of Computational Economics. Stan Miles issued this report. It is usually issued weekly.

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Other reports in NEP-CMP

The following items were announced in this report:

• Sana Ben Hamida & Wafa Abdelmalek & Fathi Abid, 2020. "Applying Dynamic Training-Subset Selection Methods Using Genetic Programming for Forecasting Implied Volatility," Papers 2007.07207, arXiv.org.
• Fathi Abid & Wafa Abdelmalek & Sana Ben Hamida, 2020. "Dynamic Hedging using Generated Genetic Programming Implied Volatility Models," Papers 2006.16407, arXiv.org.
• Martin, Ian & Nagel, Stefan, 2019. "Market Efficiency in the Age of Big Data," CEPR Discussion Papers 14235, C.E.P.R. Discussion Papers.
• Giglio, Stefano & Feng, Guanhao & Xiu, Dacheng, 2020. "Taming the Factor Zoo: A Test of New Factors," CEPR Discussion Papers 14266, C.E.P.R. Discussion Papers.
• Christian Hauenstein & Franziska Holz, 2020. "The U.S. Coal Sector between Shale Gas and Renewables: Last Resort Coal Exports?," Discussion Papers of DIW Berlin 1880, DIW Berlin, German Institute for Economic Research.
• Kevin S. Zhang & Traian A. Pirvu, 2020. "Numerical Simulation of Exchange Option with Finite Liquidity: Controlled Variate Model," Papers 2006.07771, arXiv.org.
• Samuel Mugel & Carlos Kuchkovsky & Escolastico Sanchez & Samuel Fernandez-Lorenzo & Jorge Luis-Hita & Enrique Lizaso & Roman Orus, 2020. "Dynamic Portfolio Optimization with Real Datasets Using Quantum Processors and Quantum-Inspired Tensor Networks," Papers 2007.00017, arXiv.org, revised Dec 2021.
• Mostapha Kalami Heris & Shahryar Rahnamayan, 2020. "Multi-objective Optimal Control of Dynamic Integrated Model of Climate and Economy: Evolution in Action," Papers 2007.00449, arXiv.org.
• Ali Kabiri & Harold James & John Landon-Lane & David Tuckett & Rickard Nyman, 2020. "The Role of Sentiment in the Economy: 1920 to 1934," CESifo Working Paper Series 8336, CESifo.
• Cyrille Lenoel & Garry Young, 2020. "Real-time turning point indicators: Review of current international practices," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2020-05, Economic Statistics Centre of Excellence (ESCoE).
• Jonathan Readshaw & Stefano Giani, 2020. "Using Company Specific Headlines and Convolutional Neural Networks to Predict Stock Fluctuations," Papers 2006.12426, arXiv.org.
• Marijn A. Bolhuis & Brett Rayner, 2020. "Deus ex Machina? A Framework for Macro Forecasting with Machine Learning," IMF Working Papers 2020/045, International Monetary Fund.
• Jaqueson Kingeski Galimberti, 2019. "An approximation of the distribution of learning estimates in macroeconomic models," KOF Working papers 19-453, KOF Swiss Economic Institute, ETH Zurich.
• Roberto Molinari & Gaetan Bakalli & Stéphane Guerrier & Cesare Miglioli & Samuel Orso & O. Scaillet, 2020. "Swag: A Wrapper Method for Sparse Learning," Swiss Finance Institute Research Paper Series 20-49, Swiss Finance Institute.
• Derek Singh & Shuzhong Zhang, 2020. "Distributionally Robust Profit Opportunities," Papers 2006.11279, arXiv.org.
• Emile Cammeraat & Ernesto Crivelli, 2020. "Toward a Comprehensive Tax Reform for Italy," IMF Working Papers 2020/037, International Monetary Fund.
• Hassan, Tarek & Hollander, Stephan & van Lent, Laurence & Tahoun, Ahmed, 2019. "The Global Impact of Brexit Uncertainty," CEPR Discussion Papers 14253, C.E.P.R. Discussion Papers.
• Josef Danv{e}k & J. Posp'iv{s}il, 2020. "Numerical aspects of integration in semi-closed option pricing formulas for stochastic volatility jump diffusion models," Papers 2006.13181, arXiv.org.
• Sam Ganzfried, 2020. "Algorithm for Computing Approximate Nash Equilibrium in Continuous Games with Application to Continuous Blotto," Papers 2006.07443, arXiv.org, revised Jun 2021.
• Pai, Mallesh & Hansen, Karsten, 2020. "Algorithmic Collusion: Supra-competitive Prices via Independent Algorithms," CEPR Discussion Papers 14372, C.E.P.R. Discussion Papers.
• Daniel Bartl & Samuel Drapeau & Jan Obloj & Johannes Wiesel, 2020. "Sensitivity analysis of Wasserstein distributionally robust optimization problems," Papers 2006.12022, arXiv.org, revised Nov 2021.
• Philippe Goulet Coulombe, 2020. "The Macroeconomy as a Random Forest," Papers 2006.12724, arXiv.org, revised Mar 2021.
• Romain Plassard, 2020. "Making a Breach: The Incorporation of Agent-Based Models into the Bank of England's Toolkit," GREDEG Working Papers 2020-30, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
• Marijn A. Bolhuis & Brett Rayner, 2020. "The More the Merrier? A Machine Learning Algorithm for Optimal Pooling of Panel Data," IMF Working Papers 2020/044, International Monetary Fund.
• Ali Hirsa & Weilong Fu, 2020. "An unsupervised deep learning approach in solving partial integro-differential equations," Papers 2006.15012, arXiv.org, revised Dec 2020.
• Warwick McKibbin & David Vines, 2020. "Global macroeconomic cooperation in response to the COVID-19 pandemic: a roadmap for the G20 and the IMF," CAMA Working Papers 2020-68, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
• Warwick McKibbin & Roshen Fernando, 2020. "Global macroeconomic scenarios of the COVID-19 pandemic," CAMA Working Papers 2020-62, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
• E. Ramos-P'erez & P. J. Alonso-Gonz'alez & J. J. N'u~nez-Vel'azquez, 2020. "Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network," Papers 2006.16383, arXiv.org, revised Aug 2020.
• Roc Armenter & Michèle Müller-Itten & Zachary Stangebye, 2020. "Rational Inattention via Ignorance Equivalence," Working Papers 20-24, Federal Reserve Bank of Philadelphia.
• Jacq, Kévin & William, Rapuc & Alexandre, Benoit & Didier, Coquin & Bernard, Fanget & Perrette, Yves & Sabatier, Pierre & Bruno, Wilhelm & Maxime, Debret & Arnaud, Fabien, 2020. "Sedimentary structures discriminations with hyperspectral imaging on sediment cores," Earth Arxiv 4ue5s, Center for Open Science.
• Gambacorta, Leonardo & Huang, Yiping & Qiu, Han & Wang, Jingyi, 2019. "How do machine learning and non-traditional data affect credit scoring? New evidence from a Chinese fintech firm," CEPR Discussion Papers 14259, C.E.P.R. Discussion Papers.