|
on Computational Economics |
|
Issue of 2018‒08‒27
ten papers chosen by |
| By: | Mauro Napoletano (Observatoire français des conjonctures économiques); Lilit Popoyan (Laboratory of Economics and Management); andrea Roventini, (Observatoire français des conjonctures économiques) |
| Abstract: | We develop an agent-based model to study the macroeconomic impact of alternative macro-prudential regulations and their possible interactions with different monetary policy rules. The aim is to shed light on the most appropriate policy mix to achieve the resilience of the banking sector and foster macroeconomic stability. Simulation results show that a triple-mandate Taylor rule,focused onoutput gap, inflationand credit growth, and a BaselIII prudential regulationis the bestpolicymix to improve the stability ofthe banking sector and smooth output fluctuations. Moreover, we consider the different levers of Basel III and their combinations. We find that minimum capital requirements and counter-cyclical capital buffers allow to achieve results close to the Basel III first-best with a much more simplified regulatory framework. Finally, the components of Basel III are non-additive: the inclusion of an additional lever does not always improve the performance of the macro-prudential regulation. |
| Keywords: | Macro-prudential policy; Basel III regulation; Financial stability; Monetary policy; Agent based computational economics |
| JEL: | C63 E52 E6 G1 G21 G28 |
| Date: | 2017–02 |
| URL: | http://d.repec.org/n?u=RePEc:spo:wpmain:info:hdl:2441/5hussro0tc951q0jqpu8quliqu&r=cmp |
| By: | Karvik, Geir-Are (Bank of England); Noss, Joseph (Bank of England); Worlidge, Jack (Bank of England); Beale, Daniel (Bank of England) |
| Abstract: | This paper examines the role of high-frequency traders in flash episodes in electronic financial markets. To do so, we construct an agent-based model of a market for a financial asset in which trading occurs through a central limit order book. The model consists of heterogeneous agents with different trading strategies and frequencies, and is calibrated to high-frequency time series data on the sterling-US dollar exchange rate. Flash episodes occur in the model due to the procyclical behaviour of high-frequency market participants. This is aligned with some empirical evidence as to the drivers of real-world flash crashes. We find that the prevalence of flash episodes increases with the frequency with which high-frequency market participants trade compared to their low-frequency counterparts. This provides tentative theoretical evidence that the recent growth in high-frequency trading across some markets has led to flash episodes. Furthermore, we adapt the model so that large movements in price trigger temporary halts in trading (ie circuit breakers). This is found to reduce the magnitude and frequency of flash episodes. |
| Keywords: | Agent-based modelling; high-frequency trading; financial stability; market liquidity; flash episodes; principal trading firms (PTFs) |
| JEL: | C63 G11 G12 G17 |
| Date: | 2018–07–27 |
| URL: | http://d.repec.org/n?u=RePEc:boe:boeewp:0743&r=cmp |
| By: | Athanasoglou, Stergios; Bosetti, Valentina; Drouet, Laurent |
| Abstract: | We propose a novel framework for the economic assessment of climate-change policy. Our main point of departure from existing work is the adoption of a "satisficing", as opposed to optimizing, modeling approach. Along these lines, we place primary emphasis on the extent to which different policies meet a set of goals at a specific future date instead of their performance vis-à-vis some intertemporal objective function. Consistent to the nature of climate-change policy making, our model takes explicit account of model uncertainty. To this end, the value function we propose is an analogue of the well-known success-probability criterion adapted to settings characterized by model uncertainty. We apply this decision criterion to probability distributions constructed by Drouet et al. (2015) linking carbon budgets to future consumption. The main result that emerges is the superiority of "medium" carbon budgets in line with a 3°C target (i.e., 2000-3000 GtCO2) in preventing large future consumption losses with high probability. Insights from computational geometry facilitate computations considerably, and allow for the efficient application of the model in high-dimensional settings. |
| Keywords: | Research Methods/ Statistical Methods |
| Date: | 2017–03–22 |
| URL: | http://d.repec.org/n?u=RePEc:ags:feemmi:254043&r=cmp |
| By: | Su, Chi; Schoney, Richard; Nolan, James |
| Abstract: | The impact of widespread farm ownership by large investors is influential and uncertain. There are various reasons for buying farmland as an investment, and one of them diversification benefits for financial portfolios because many studies have found that the correlation between farmland price yield and the yields of major financial assets, such as stocks, bonds and real estates are negative. On the other hand, the prime objectives of many farm family businesses are “to maintain control and pass on a secure and sound business to the next generation” (Hay and Morris 1984, Errington 2002). Farmland is generally retained within the family by succession due to the strong emotional and economic linkage. However, very little literature has studied the interaction between these two types of farmland transitions. Given limited data on this research topic, an agent based simulation model (ABSM) is one of the few practical solutions for the study of this area. The model in this study is built upon Anderson’s (2012) model that simulates farming activities in Canadian Agricultural Region 1A in Saskatchewan in Repast©. Two modules, farm succession and institutional investors who purchase farmland as a financial asset to diversify the aggregate risk of the portfolio, are added. Further, 30-year farming and investing performances are simulated in four different scenarios to test the impacts on different activeness of institutional investments. They are assumed to lease the farmland they own back to the local farmers for the rent as their dividends. Conclusively, it is found the participation of institutional investors in farmland purchase market can push up the farmland price for 15% to 40% in different scenarios, and the farmers tend to lease slightly more. Meanwhile, the number of farms constantly decrease and more and more large farms emerge in the simulation period with or without investors. Finally, the results show that the existence of institutional investors would not negatively impact the industry health. Les impacts de l'achat en masse de terres agricoles par de grands investisseurs sont à la fois influents et incertains. L'achat de terres agricoles comme investissement se fait pour diverses raisons. L'une d'entre elles est l'avantage que représente la diversification des portefeuilles financiers parce que de nombreuses études démontrent le lien négatif entre la valeur du rendement agricole et les rendements d'avoirs financiers d'importance comme les actions, obligations et investissements immobiliers. D'autre part, les objectifs principaux de nombreuses entreprises agricoles sont de « maintenir le contrôle et de transmettre à la prochaine génération une entreprise solide et saine » [traduction libre] (Hay and Morris 1984, Errington 2002). Les terres agricoles sont ainsi conservées au sein d'une même famille par succession grâce aux liens émotifs et économiques forts. Par contre, peu d'études portent sur cette interaction entre ces deux types de transitions agricoles. Étant donné le peu de données à ce sujet, le modèle de simulation à base d'agents représente l'une des solutions les plus pratiques pour étudier ce sujet. Le modèle utilisé pour cette étude se base sur le modèle d'Anderson (2012) qui simule les activités agricoles de la région agricole canadienne 1A en Saskatchewan dans Repast©. Deux modules sont ajoutés : succession agricole et investisseurs institutionnels qui font l'acquisition de terres agricoles comme avoir financier pour diversifier le risque global au portefeuille. De plus, les performances agricoles et d'investissement sur une période de 30 ans sont simulées dans quatre scénarios distincts afin d'évaluer les impacts sur différents niveaux d'activités des investissements institutionnels. L'on suppose que ces derniers louent les terres agricoles aux fermiers locaux pour le loyer comme dividendes. Il est donc trouvé définitivement que la participation des investisseurs institutionnels dans l'achat de terres agricoles peut faire augmenter le prix de ces terres de 15 % à 40 $ selon le scénario, et les fermiers ont légèrement plus tendance à louer. Parallèlement, le nombre de fermes diminue constamment et de plus en plus de grandes fermes apparaissent dans la période de simulation avec ou sans investisseurs. Finalement, les résultats démontrent que l'existence d'investisseurs institutionnels n'aurait pas d'impact sur la santé de l'industrie. |
| Keywords: | Farm Management |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:ags:cafp17:253250&r=cmp |
| By: | Guo, P.; Lam, J.; Li, V. |
| Abstract: | Time-based pricing programs for domestic electricity users have been effective in reducing peak demand and facilitating renewables integration. Nevertheless, high cost, price non-responsiveness and adverse selection may create the possible challenges. To overcome these challenges, it can be fruitful to investigate the ‘high-potential’ users, which are more responsive to price changes and apply time-based pricing to these users. Few studies have investigated how to identify which users are more price-responsive. We aim to fill this gap by comprehensively identifying the drivers of domestic users’ price responsiveness, in order to facilitate the selection of the high-potential users. We adopt a novel data-driven approach, first by a feed forward neural network model to accurately determine the baseline monthly peak consumption of individual households, followed by an integrated machine-learning variable selection methodology to identify the drivers of price responsiveness applied to Irish smart meter data from 2009-10 as part of a national Time of Use trial. This methodology substantially outperforms traditional variable selection methods by combining three advanced machine-learning techniques. Our results show that the response of energy users to price change is affected by a number of factors, ranging from demographic and dwelling characteristics, psychological factors, historical electricity consumption, to appliance ownership. In particular, historical electricity consumption, income, the number of occupants, perceived behavioural control, and adoption of specific appliances, including immersion water heater and dishwasher, are found to be significant drivers of price responsiveness. We also observe that continual price increase within a moderate range does not drive additional peak demand reduction, and that there is an intention-behaviour gap, whereby stated intention does not lead to actual peak reduction behavior. Based on our findings, we have conducted scenario analysis to demonstrate the feasibility of selecting the high potential users to achieve significant peak reduction. |
| Keywords: | Time-based electricity pricing, price responsiveness, high-potential users, variable selection, Time of Use, machine learning |
| JEL: | Q41 |
| Date: | 2018–08–16 |
| URL: | http://d.repec.org/n?u=RePEc:cam:camdae:1844&r=cmp |
| By: | Turrell, Arthur (Bank of England); Thurgood, James (Bank of England); Djumalieva, Jyldyz (Nesta); Copple, David (Bank of England); Speigner, Bradley (Bank of England) |
| Abstract: | What type of disaggregation should be used to analyse heterogeneous labour markets? How granular should that disaggregation be? Economic theory does not currently tell us; perhaps data can. Analyses typically split labour markets according to top-down classification schema such as sector or occupation. But these may be slow-moving or inaccurate relative to the structure of the labour market as perceived by firms and workers. Using a dataset of 15 million job adverts posted online between 2008 and 2016, we create an empirically driven, ‘bottom-up’ segmentation of the labour market which cuts across wage, sector, and occupation. Our segmentation is based upon applying machine learning techniques to the demand expressed in the text of job descriptions. This segmentation automatically identifies traditional job roles but also surfaces sub-markets not apparent in current classifications. We show that the segmentation has explanatory power for offered wages. The methodology developed could be deployed to create data-driven taxonomies in conditions of rapidly changing labour markets and demonstrates the potential of unsupervised machine learning in economics. |
| Keywords: | Vacancies; classification; disaggregation |
| JEL: | J42 |
| Date: | 2018–07–27 |
| URL: | http://d.repec.org/n?u=RePEc:boe:boeewp:0742&r=cmp |
| By: | Clarete, Ramon |
| Abstract: | Among several factors that may explain why electricity in the Philippines is expensive compared to other ASEAN member states, this paper zeroes in on two: the value added tax (VAT) and red tape in obtaining generation business permits. Legislators have raised the timeliness of lifting just the VAT on electricity to reduce electricity prices. This study however observes that red tape may contribute three times more than the VAT to making electricity costly in the country. The study uses a computable general equilibrium (CGE) model of the Philippine economy to explore the relative contributions of the two to electricity price, and simulate their economic effects. Besides reducing electricity prices, streamlining and shortening the business permitting process for new generation companies in the country will make the economy more efficient and raise the revenue from VAT. |
| Keywords: | Electric Energy, Electricity, Policy Analysis |
| JEL: | D04 D58 H25 Q48 |
| Date: | 2016–10 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:87727&r=cmp |
| By: | Nobuhiro Abe (Bank of Japan); Kimiaki Shinozaki (Bank of Japan) |
| Abstract: | This paper compiles experimental price indices for 20 home electrical appliances and digital consumer electronic products using big data obtained from Kakaku.com, the largest price comparison website in Japan, and a machine-learning algorithm which pairs legacy and successor products with high precision. In so doing, authors examine the validity of quality adjustment methods by performing comparative analyses on the difference these methods have on price indices. Findings from the analyses are as follows: Indices applied with the Webscraped Prices Comparison Method--the quality adjustment method newly developed and introduced by the Bank of Japan--are more cost-effective than those applied with the Hedonic Regression Method which is known to possess high accuracy in index creation. Indices applied with the Matched-Model Method, which is frequently applied to price indices using big data is unable to precisely reflect price increases intended to ensure the profitability often seen in home electronics at time of product turnover. This indicates the significant downward bias in price indices. These findings once again highlight the importance of selecting the appropriate quality adjustment method when compiling price indices. |
| Keywords: | price index; quality adjustment method; hedonic approach; support vector machine |
| JEL: | C43 C45 E31 |
| Date: | 2018–08–20 |
| URL: | http://d.repec.org/n?u=RePEc:boj:bojwps:wp18e13&r=cmp |
| By: | Anurag Sodhi |
| Abstract: | This paper explores alternative regression techniques in pricing American put options and compares to the least-squares method (LSM) in Monte Carlo implemented by Longstaff-Schwartz, 2001 which uses least squares to estimate the conditional expected payoff to the option holder from continuation. The pricing is done under general model framework of Bakshi, Cao and Chen 1997 which incorporates, stochastic volatility, stochastic interest rate and jumps. Alternative regression techniques used are Artificial Neural Network (ANN) and Gradient Boosted Machine (GBM) Trees. Model calibration is done on American put options on SPY using these three techniques and results are compared on out of sample data. |
| Date: | 2018–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1808.02791&r=cmp |
| By: | Ben Mermelstein; Volker Nocke; Mark A. Satterthwaite; Michael D. Whinston |
| Abstract: | We study optimal merger policy in a dynamic model in which the presence of scale economies implies that firms can reduce costs through either internal investment in building capital or through mergers. The model, which we solve computationally, allows firms to invest or propose mergers according to the relative profitability of these strategies. An antitrust authority is able to block mergers at some cost. We examine the optimal policy for an antitrust authority who cannot commit to its future policy rule and approves or rejects mergers as they are proposed, considering both consumer value and aggregate value as its possible objectives. We find that the optimal policy can differ substantially from what would be best considering only welfare in the period the merger is proposed. In general, antitrust policy can greatly affect firms' optimal investment behavior, and firms' investment behavior can in turn greatly affect the antitrust authority's optimal policy. Moreover, externalities imposed by mergers on rivals can have significant effects on firms' investment incentives and thereby shape the optimal policy. |
| Keywords: | Horizontal merger, merger policy, investment, scale economies, antitrust |
| JEL: | L13 L40 |
| Date: | 2018–08 |
| URL: | http://d.repec.org/n?u=RePEc:bon:boncrc:crctr224_038_2018&r=cmp |