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Deep Hedging : Learning to Simulate Equity Option Markets
Wiese, Magnus - 2019
We construct realistic equity option market simulators based on generative adversarial networks (GANs). We consider recurrent and temporal convolutional architectures, and assess the impact of state compression. Option market simulators are highly relevant because they allow us to extend the...
Persistent link: https://www.econbiz.de/10012861067
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Risk dominance, payoff dominance and probabilistic choice learning
Battalio, Raymond Charles; Samuelson, Larry; Van Huyck, … - 1997
Persistent link: https://www.econbiz.de/10000990245
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Small-loss bounds for online learning with partial information
Lykouris, Thodoris; Sridharan, Karthik; Tardos, Éva - In: Mathematics of operations research 47 (2022) 3, pp. 2186-2218
Persistent link: https://www.econbiz.de/10013375043
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Implementation rationality : the nexus of psychology and economics at the RAND logistics systems laboratory, 1956-1966s
Klein, Judy L. - 2015 - Version 3
simulation to translate the broad findings of normative microeconomics into detailed, implementable pr ocedures for US Air Force … oper ations. The laboratory was housed in the training simulation facilities that had been recently vacated by … problems. Economists went from using simulation to demonstrate the superiority of optimal policies derived from deductive …
Persistent link: https://www.econbiz.de/10011600635
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Deep Hedging : Learning Risk-Neutral Implied Volatility Dynamics
Buehler, Hans; Phillip, Murray; Pakkanen, Mikko; Wood, Ben - 2021
We present a numerically efficient approach for machine-learning a risk-neutral measure for paths of simulated spot and option prices up to a finite horizon under convex transaction costs and convex trading constraints. This approach can then be used to implement a stochastic implied volatility...
Persistent link: https://www.econbiz.de/10013236469
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On the Investment-Uncertainty Relationship in a Real Option Model with Stochastic Volatility
Ting, Sai Hung Marten - 2012
We consider the classical investment timing problem in a framework where the instantaneous volatility of the project value is itself given by a stochastic process, hence lifting the old question about the investment-uncertainty relationship to a new level. Motivated by the classical cases of...
Persistent link: https://www.econbiz.de/10013114717
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Implementation Rationality : The Nexus of Psychology and Economics at the RAND Logistics Systems Laboratory, 1956-1966
Klein, Judy - 2015
simulation to translate the broad findings of normative microeconomics into detailed, implementable procedures for US Air Force … operations. The laboratory was housed in the training simulation facilities that had been recently vacated by psychologists … problems. Economists went from using simulation to demonstrate the superiority of optimal policies derived from deductive …
Persistent link: https://www.econbiz.de/10013015484
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Simulating with Rice Coalitionally Stable Burden Sharing Agreements for the Climate Change Problem
Tulkens, Henry; Eyckmans, Johan - 2021
In this paper we test empirically with the Nordhaus and Yang (1996) RICE model the core property of the transfer scheme advocated by Germain, Toint and Tulkens (1997). This scheme is designed to sustain full cooperation in a voluntary international environmental agreement by making all countries...
Persistent link: https://www.econbiz.de/10013321371
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Chapter 19 Agent-Based Models and Human Subject Experiments
Duffy, John - In: Handbook of computational economics : volume 2, …, (pp. 949-1011). 2006
This chapter examines the relationship between agent-based modeling and economic decision-making experiments with human subjects. Both approaches exploit controlled “laboratory” conditions as a means of isolating the sources of aggregate phenomena. Research findings from laboratory studies...
Persistent link: https://www.econbiz.de/10014024386
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An Empirical Model of Learning Under Ambiguity : The Case of Clinical Trials
Fernandez, Jose M. - 2014
In this paper, I present an empirical model of learning under ambiguity in the context of clinical trials. Patients are concern with learning the treatment effect of the experimental drug, but face the ambiguity of random group assignment. A two dimensional Bayesian model of learning is proposed...
Persistent link: https://www.econbiz.de/10014048207