nep-cmp New Economics Papers
on Computational Economics
Issue of 2019‒11‒11
thirteen papers chosen by
Stan Miles
Thompson Rivers University

  1. Efficient Computation with Taste Shocks By Gordon, Grey
  2. ElecSim: Monte-Carlo Open-Source Agent-Based Model to Inform Policy for Long-Term Electricity Planning By Alexander J. M. Kell; Matthew Forshaw; A. Stephen McGough
  3. Deep Learning for Stock Selection Based on High Frequency Price-Volume Data By Junming Yang; Yaoqi Li; Xuanyu Chen; Jiahang Cao; Kangkang Jiang
  4. Recreating Banking Networks under Decreasing Fixed Costs By Maringer, Dietmar; Craig, Ben R.; Paterlini, Sandra
  5. Smart Hedging Against Carbon Leakage By Christoph Böhringer; Knut Einar Rosendahl; Halvor Briseid Storrøsten
  6. Modelling the Consequences of the U.S.-China Trade War and Related Trade Frictions for the U.S., Chinese, Australian and Global Economies By J.A. Giesecke; R. Waschik; N.H. Tran
  7. The survival of start-ups in time of crisis. A machine learning approach to measure innovation By Marco Guerzoni; Consuelo R. Nava; Massimiliano Nuccio
  8. The transport-based meshfree method (TMM) and its applications in finance: a review By Philippe G. LeFloch; Jean-Marc Mercier
  9. MUST-B: a multi-agent LUTI model for systemic simulation of urban policies By Nathalie GAUSSIER; Seghir ZERGUINI
  10. Deep Hedging: Learning to Simulate Equity Option Markets By Magnus Wiese; Lianjun Bai; Ben Wood; Hans Buehler
  11. Robo-advising: Learning Investor's Risk Preferences via Portfolio Choices By Humoud Alsabah; Agostino Capponi; Octavio Ruiz Lacedelli; Matt Stern
  12. Personalized Robo-Advising: Enhancing Investment through Client Interaction By Agostino Capponi; Sveinn Olafsson; Thaleia Zariphopoulou
  13. "Fighting Against Learning Crisis in Developing Countries: A Randomized Experiment of Self-Learning at the Right Level" By Masafumi Nakano; Akihiko Takahashi

  1. By: Gordon, Grey (Federal Reserve Bank of Richmond)
    Abstract: Taste shocks result in nondegenerate choice probabilities, smooth policy functions, continuous demand correspondences, and reduced computational errors. They also cause significant computational cost when the number of choices is large. However, I show that, in many economic models, a numerically equivalent approximation may be obtained extremely efficiently. If the objective function has increasing differences (a condition closely tied to policy function monotonicity) or is concave in a discrete sense, the proposed algorithms are O(n log n) for n states and n choice--a drastic improvement over the naive algorithm's O(n2) cost. If both hold, the cost can be further reduced to O(n). Additionally, with increasing differences in two state variables, I propose an algorithm that in some cases is O(n2) even without concavity (in contrast to the O(n3) naive algorithm). I illustrate the usefulness of the proposed approach in an incomplete markets economy and a long-term sovereign debt model, the latter requiring taste shocks for convergence. For grid sizes of 500 points, the algorithms are up to 200 times faster than the naive approach.
    Keywords: Computation; Monotonicity; Discrete Choice; Taste Shocks; Sovereign Default; Curse of Dimensionality
    JEL: C61 C63 E32 F34 F41 F44
    Date: 2019–09–11
  2. By: Alexander J. M. Kell; Matthew Forshaw; A. Stephen McGough
    Abstract: Due to the threat of climate change, a transition from a fossil-fuel based system to one based on zero-carbon is required. However, this is not as simple as instantaneously closing down all fossil fuel energy generation and replacing them with renewable sources -- careful decisions need to be taken to ensure rapid but stable progress. To aid decision makers, we present a new tool, ElecSim, which is an open-sourced agent-based modelling framework used to examine the effect of policy on long-term investment decisions in electricity generation. ElecSim allows non-experts to rapidly prototype new ideas. Different techniques to model long-term electricity decisions are reviewed and used to motivate why agent-based models will become an important strategic tool for policy. We motivate why an open-source toolkit is required for long-term electricity planning. Actual electricity prices are compared with our model and we demonstrate that the use of a Monte-Carlo simulation in the system improves performance by $52.5\%$. Further, using ElecSim we demonstrate the effect of a carbon tax to encourage a low-carbon electricity supply. We show how a {\pounds}40 ($\$50$) per tonne of CO2 emitted would lead to 70% renewable electricity by 2050.
    Date: 2019–10
  3. By: Junming Yang; Yaoqi Li; Xuanyu Chen; Jiahang Cao; Kangkang Jiang
    Abstract: Training a practical and effective model for stock selection has been a greatly concerned problem in the field of artificial intelligence. Even though some of the models from previous works have achieved good performance in the U.S. market by using low-frequency data and features, training a suitable model with high-frequency stock data is still a problem worth exploring. Based on the high-frequency price data of the past several days, we construct two separate models-Convolution Neural Network and Long Short-Term Memory-which can predict the expected return rate of stocks on the current day, and select the stocks with the highest expected yield at the opening to maximize the total return. In our CNN model, we propose improvements on the CNNpred model presented by E. Hoseinzade and S. Haratizadeh in their paper which deals with low-frequency features. Such improvements enable our CNN model to exploit the convolution layer's ability to extract high-level factors and avoid excessive loss of original information at the same time. Our LSTM model utilizes Recurrent Neural Network'advantages in handling time series data. Despite considerable transaction fees due to the daily changes of our stock position, annualized net rate of return is 62.27% for our CNN model, and 50.31% for our LSTM model.
    Date: 2019–11
  4. By: Maringer, Dietmar (University of Basel); Craig, Ben R. (Federal Reserve Bank of Cleveland); Paterlini, Sandra (University of Trento)
    Abstract: Theory emphasizes the central role of the structure of networks in the behavior of financial systems and their response to policy. Real-world networks, however, are rarely directly observable: Banks’ assets and liabilities are typically known, but not who is lending how much and to whom. We first show how to simulate realistic networks that are based on balance-sheet information by minimizing costs where there is a fixed cost to forming a link. Second, we also show how to do this for a model with fixed costs that are decreasing in the number of links. To approach the optimization problem, we develop a new algorithm based on the transportation planning literature. Computational experiments find that the resulting networks are not only consistent with the balance sheets, but also resemble real-world financial networks in their density (which is sparse but not minimally dense) and in their core-periphery and disassortative structure.
    Keywords: banking networks; network models; optimization;
    JEL: C44 E59 G21
    Date: 2019–11–05
  5. By: Christoph Böhringer; Knut Einar Rosendahl; Halvor Briseid Storrøsten
    Abstract: Policy makers in the EU and elsewhere are concerned that unilateral carbon pricing induces carbon leakage through relocation of emission-intensive and trade-exposed industries to other regions. A common measure to mitigate such leakage is to combine an emission trading system (ETS) with output-based allocation (OBA) of allowances to exposed industries. We first show analytically that in a situation with an ETS combined with OBA, it is optimal to impose a consumption tax on the goods that are entitled to OBA, where the tax is equivalent in value to the OBA-rate. Then, using a multi-region, multi-sector computable general equilibrium (CGE) model calibrated to empirical data, we quantify the welfare gains for the EU to impose such a consumption tax on top of its existing ETS with OBA. We run Monte Carlo simulations to account for uncertain leakage exposure of goods entitled to OBA. The consumption tax increases welfare whether the goods are highly exposed to leakage or not, and can hence be regarded as smart hedging against carbon leakage.
    Keywords: carbon leakage, output-based allocation, consumption tax
    JEL: D61 F18 H23 Q54
    Date: 2019
  6. By: J.A. Giesecke; R. Waschik; N.H. Tran
    Abstract: We model the U.S.-China trade war, a potential U.S.-China trade deal, and the effects of import restrictions on Australian coal. We begin by examining the effects of the bilateral tariff exchange on the economies of the U.S., China, Australia and the rest of the world. We then go on to examine a scenario in which a U.S.-China trade deal is struck involving removal of the trade war tariffs and an undertaking by China to reduce its bilateral trade surplus with the U.S. by eliminating all pre-trade war tariffs on U.S. goods.We end by noting that there are dimensions to the U.S.-China tariff exchange that go beyond concerns on the part of the U.S. that are purely economic. In this context, the tariff exchange can be viewed in part as a continuation of a wider recent pattern of use of trade instruments to advance political aims. Australia itself appears to have been subject to such instruments, with reports of a slowdown in processing of Australian coal imports through Chinese ports. We simulate the effects on Australia and China of a rise in Chinese barriers to Australian coal imports.
    Keywords: trade policy, trade war, coal embargo, multi-region CGE model
    JEL: F13 F17 C68
    Date: 2019–07
  7. By: Marco Guerzoni; Consuelo R. Nava; Massimiliano Nuccio
    Abstract: This paper shows how data science can contribute to improving empirical research in economics by leveraging on large datasets and extracting information otherwise unsuitable for a traditional econometric approach. As a test-bed for our framework, machine learning algorithms allow us to create a new holistic measure of innovation built on a 2012 Italian Law aimed at boosting new high-tech firms. We adopt this measure to analyse the impact of innovativeness on a large population of Italian firms which entered the market at the beginning of the 2008 global crisis. The methodological contribution is organised in different steps. First, we train seven supervised learning algorithms to recognise innovative firms on 2013 firmographics data and select a combination of those with best predicting power. Second, we apply the former on the 2008 dataset and predict which firms would have been labelled as innovative according to the definition of the law. Finally, we adopt this new indicator as regressor in a survival model to explain firms' ability to remain in the market after 2008. Results suggest that the group of innovative firms are more likely to survive than the rest of the sample, but the survival premium is likely to depend on location.
    Date: 2019–11
  8. By: Philippe G. LeFloch; Jean-Marc Mercier
    Abstract: We review a numerical technique, referred to as the Transport-based Meshfree Method (TMM), and we discuss its applications to mathematical finance. We recently introduced this method from a numerical standpoint and investigated the accuracy of integration formulas based on the Monte-Carlo methodology: quantitative error bounds were discussed and, in this short note, we outline the main ideas of our approach. The techniques of transportation and reproducing kernels lead us to a very efficient methodology for numerical simulations in many practical applications, and provide some light on the methods used by the artificial intelligence community. For applications in the finance industry, our method allows us to compute many types of risk measures with an accurate and fast algorithm. We propose theoretical arguments as well as extensive numerical tests in order to justify sharp convergence rates, leading to rather optimal computational times. Cases of direct interest in finance support our claims and the importance of the problem of the curse of dimensionality in finance applications is briefly discussed.
    Date: 2019–11
  9. By: Nathalie GAUSSIER; Seghir ZERGUINI
    Abstract: This article is part of the STRATEGIE research project co-financed by the Région Nouvelle Aquitaine. It presents the MUST-B model (Integrated Modeling of Land-Use – Transport: for application in the Bordeaux agglomeration) which is based on systemic land-use / transport modeling with regards to how the land and property markets operate, and the interdependent factors for selecting the locations of households and employment. MUST-B is an agent-oriented model which simulates household and job location choices. It is based on an auction mechanism which models competition between agents in the real estate market (existing property holdings, including residential, industrial and tertiary) and in the land property market (from buildable land reserves) over a given timeframe. This auction procedure is based on maximizing the utility provided to the agent by a given location: housing for a household and an area for business activity for employment premises. Utility is a function of several characteristics relating to the space and premises occupied, such as accessibility, surface area, energy quality of the building, notoriety, agglomeration effects and taxes, or property prices, the latter being endogenous. In this article, the mechanisms and functioning of the land property and real estate markets which prevail in MUST-B are presented. Methodological choices and behavioral guidelines for agents (households/workplaces), are also set out, and to illustrate the operational nature of the model, the databases required for implementing the MUST-B in the Bordeaux Urban Area are presented.
    Keywords: LUTI, Choice of location, Households, Employment, Accessibility, Real estate prices, Multi-agent, Systemic modeling.
    JEL: R14 R31 R41 R52
    Date: 2019
  10. By: Magnus Wiese; Lianjun Bai; Ben Wood; Hans Buehler
    Abstract: 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 limited real-world data sets available for the training and evaluation of option trading strategies. We show that network-based generators outperform classical methods on a range of benchmark metrics, and adversarial training achieves the best performance. Our work demonstrates for the first time that GANs can be successfully applied to the task of generating multivariate financial time series.
    Date: 2019–11
  11. By: Humoud Alsabah; Agostino Capponi; Octavio Ruiz Lacedelli; Matt Stern
    Abstract: We introduce a reinforcement learning framework for retail robo-advising. The robo-advisor does not know the investor's risk preference, but learns it over time by observing her portfolio choices in different market environments. We develop an exploration-exploitation algorithm which trades off costly solicitations of portfolio choices by the investor with autonomous trading decisions based on stale estimates of investor's risk aversion. We show that the algorithm's value function converges to the optimal value function of an omniscient robo-advisor over a number of periods that is polynomial in the state and action space. By correcting for the investor's mistakes, the robo-advisor may outperform a stand-alone investor, regardless of the investor's opportunity cost for making portfolio decisions.
    Date: 2019–11
  12. By: Agostino Capponi; Sveinn Olafsson; Thaleia Zariphopoulou
    Abstract: Automated investment managers, or robo-advisors, have emerged as an alternative to traditional financial advisors. Their viability crucially depends on timely communication of information from the clients they serve. We introduce and develop a novel human-machine interaction framework, in which the robo-advisor solves an adaptive mean-variance control problem, with the risk-return tradeoff dynamically updated based on the risk profile communicated by the client. Our model predicts that clients who value a personalized portfolio are more suitable for robo-advising. Clients who place higher emphasis on delegation and clients with a risk profile that changes frequently benefit less from robo-advising.
    Date: 2019–11
  13. By: Masafumi Nakano (Graduate School of Economics, The University of Tokyo); Akihiko Takahashi (Faculty of Economics, The University of Tokyo)
    Abstract: This paper proposes a novel approach to the portfolio management using an AutoEncoder. In particular, the features learned by an AutoEncoder with ReLU are directly exploited to the portfolio construction. Since the AutoEncoder extracts the characteristics of the data through the non-linear activation function ReLU, its realization is generally difficult due to the non-linear transformation procedure. In the current paper, we solve this problem by taking full advantage of the similarity of the ReLU and the option payoff. Especially, this paper shows that the features are successfully replicated by applying so-called the dynamic delta hedging strategy. An out of sample simulation with crypto currency dataset shows the effectiveness of our proposed strategy. Furthermore, we investigate the background of our proposed methodology, which suggests that the rst principal component is quite important.
    Date: 2019–10

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