Effects of herding behavior of tradable green certificate market players on market efficiency: Insights from heterogeneous agent model

Abstract

Tradable green certificate (TGC) scheme promotes the development of renewable energy industry which currently has a dual effect on economy and environment. TGC market efficiency is reflected in stimulating renewable energy investment, but may be reduced by the herding behavior of market players. This paper proposes and simulates an artificial TGC market model which contains heterogeneous agents, communication structure, and regulatory rules to explore the characteristics of herding behavior and its effects on market efficiency. The results show that the evolution of herding behavior reduces information asymmetry and improves market efficiency, especially when the borrowing is allowed. In addition, the fundamental strategy is diffused by herding evolution, but TGC market efficiency may be remarkably reduced by herding with borrowing mechanism. Moreover, the herding behavior may evolve to an equilibrium where the revenue of market players is comparable, thus the fairness in TGC market is improved.

Graphical abstract

Keywords

tradable green certificate / herding behavior / evolution / heterogeneous agent model / complex network

References

[1] IEA. Global CO₂ emissions in 2019. 2020, available at website of IEA

[2] United Nations Environment Programme. Emissions Gap Report 2019. 2020

[3] Zenghelis D, Agarwala M, Coyle D, Valuing Wealth, Building Prosperity. The Wealth Economy Project on Natural and Social Capital, One Year Report. Bennett Institute for Public Policy, Cambridge, 2020

[4] Blyth W, Gross R, Speirs J, Low Carbon Jobs: The Evidence for Net Job Creation from Policy Support for Energy Efficiency and Renewable Energy. London: UK Energy Research Centre, 2014

[5] Barbose G. US Renewables Portfolio Standards: 2017 Annual Status Report. Lawrence Berkeley National Lab., Berkeley, CA, USA, 2017

[6] Agnolucci P. The effect of financial constraints, technological progress and long-term contracts on tradable green certificates. Energy Policy, 2007, 35(6): 3347–3359

[7] Darmani A, Rickne A, Hidalgo A, When outcomes are the reflection of the analysis criteria: a review of the tradable green certificate assessments. Renewable & Sustainable Energy Reviews, 2016, 62: 372–381

[8] BenSaïda A. Herding effect on idiosyncratic volatility in US industries. Finance Research Letters, 2017, 23: 121–132

[9] Economou F, Gavriilidis K, Gregoriou G, Handbook of Investors’ Behavior During Financial Crises. Pittsburgh: Academic Press, 2017, 151–168

[10] Júnior G S R, Palazzi R B, Klotzle M C, Analyzing herding behavior in commodities markets—an empirical approach. Finance Research Letters, 2020, 35: 101285

[11] Wang H, Zhao X G, Ren L Z, An agent-based modeling approach for analyzing the influence of market participants’ strategic behavior on green certificate trading. Energy, 2021, 218: 119463

[12] Palao F, Pardo A. Do carbon traders behave as a herd? North American Journal of Economics and Finance, 2017, 41: 204–216

[13] Brunetti C, Buyuksahin B, Harris J H. Herding and speculation in the crude oil market. Energy Journal, 2013, 34(3): 83–104

[14] Hirshleifer D, Hong Teoh S. Herd behaviour and cascading in capital markets: a review and synthesis. European Financial Management, 2003, 9(1): 25–66

[15] Chauhan Y, Ahmad N, Aggarwal V, Herd behaviour and asset pricing in the Indian stock market. IIMB Management Review, 2020, 32(2): 143–152

[16] Nielsen L, Jeppesen T. Tradable Green Certificates in selected European countries—overview and assessment. Energy Policy, 2003, 31(1): 3–14

[17] Ghaffari M, Hafezalkotob A, Makui A. Analysis of implementation of Tradable Green Certificates system in a competitive electricity market: a game theory approach. Journal of Industrial Engineering International, 2016, 12(2): 185–197

[18] Hasani-Marzooni M, Hosseini S H. Trading strategies for wind capacity investment in a dynamic model of combined tradable green certificate and electricity markets. IET Generation, Transmission & Distribution, 2012, 6(4): 320–330

[19] Zuo Y, Zhao X G, Zhang Y Z, From feed-in tariff to renewable portfolio standards: an evolutionary game theory perspective. Journal of Cleaner Production, 2019, 213: 1274–1289

[20] Vogstad K, Kristensen I S, Wolfgang O. Tradable green certificates: the dynamics of coupled electricity markets. In: Proceedings of System Dynamics Conference, New York, USA, 2003

[21] An X, Zhang S, Li X, Two-stage joint equilibrium model of electricity market with tradable green certificates. Transactions of the Institute of Measurement and Control, 2019, 41(6): 1615–1626

[22] Maug E, Naik N. Herding and delegated portfolio management: the impact of relative performance evaluation on asset allocation. Quarterly Journal of Finance, 2011, 1(02): 265–292

[23] Huang T C, Lin B H, Yang T H. Herd behavior and idiosyncratic volatility. Journal of Business Research, 2015, 68(4): 763–770

[24] Yamamoto R. Volatility clustering and herding agents: does it matter what they observe? Journal of Economic Interaction and Coordination, 2011, 6(1): 41–59

[25] Lakonishok J, Shleifer A, Vishny R W. The impact of institutional trading on stock prices. Journal of Financial Economics, 1992, 32(1): 23–43

[26] Wermers R. Mutual fund herding and the impact on stock prices. Journal of Finance, 1999, 54(2): 581–622

[27] Choi N, Skiba H. Institutional herding in international markets. Journal of Banking & Finance, 2015, 55: 246–259

[28] Hessary Y K, Hadzikadic M. An agent-based study of herding relationships with financial markets phenomena. In: 2017 Winter Simulation Conference (WSC), Las Vegas, USA, 2017, 1204–1215

[29] Lux T. Herd behaviour, bubbles and crashes. Economic Journal (London), 1995, 105(431): 881–896

[30] Kaizoji T. Speculative bubbles and crashes in stock markets: an interacting-agent model of speculative activity. Physica A, 2000, 287(3–4): 493–506

[31] Foroni I, Agliari A. Complex price dynamics in a financial market with imitation. Computational Economics, 2008, 32(1–2): 21–36

[32] Manahov V, Hudson R. Herd behaviour experimental testing in laboratory artificial stock market settings. Behavioural foundations of stylised facts of financial returns. Physica A, 2013, 392(19): 4351–4372

[33] Leece R D, White T P. The effects of firms’ information environment on analysts’ herding behavior. Review of Quantitative Finance and Accounting, 2017, 48(2): 503–525

[34] Avery C, Zemsky P. Multidimensional uncertainty and herd behavior in financial markets. American Economic Review, 1998, 88(4): 724–748

[35] Carro A, Toral R, San Miguel M. Markets, herding and response to external information. PLoS One, 2015, 10(7): e0133287

[36] Galariotis E C, Rong W, Spyrou S I. Herding on fundamental information: a comparative study. Journal of Banking & Finance, 2015, 50: 589–598

[37] Yang W R. Herding with costly information and signal extraction. International Review of Economics & Finance, 2011, 20(4): 624–632

[38] Chiarella C. The dynamics of speculative behaviour. Annals of Operations Research, 1992, 37(1): 101–123

[39] Iihara Y, Kato H K, Tokunaga T. Investors’ herding on the Tokyo stock exchange. International Review of Finance, 2001, 2(1–2): 71–98

[40] Lakonishok J, Shleifer A, Vishny R W. The impact of institutional trading on stock prices. Journal of Financial Economics, 1992, 32(1): 23–43

[41] Lux T, Marchesi M. Volatility clustering in financial markets: a microsimulation of interacting agents. International Journal of Theoretical and Applied Finance, 2000, 3(04): 675–702

[42] Hommes C H. Heterogeneous agent models in economics and finance. In: Handbook of Computational Economics, 2006, 2: 1109–1186

[43] Chiarella C, Dieci R, Gardini L, A model of financial market dynamics with heterogeneous beliefs and state-dependent confidence. Computational Economics, 2008, 32(1–2): 55–72

[44] Peng C, Wang C. Positive feedback trading and stock prices: evidence from mutual funds. 2019, available at the website of ssrn

[45] Simon H A. Bounded rationality and organizational learning. Organization Science, 1991, 2(1): 125–134

[46] Sciubba E. Bounded rationality. Adaptive Toolbox., 2003, 113(485): F189–F190

[47] Morita S. Six susceptible-infected-susceptible models on scale-free networks. Scientific Reports, 2016, 6(1): 1–8

[48] Barabási A L. Scale-free networks: a decade and beyond. Science, 2009, 325(5939): 412–413

[49] Grimm V, Berger U, Bastiansen F, A standard protocol for describing individual-based and agent-based models. Ecological Modelling, 2006, 198(1–2): 115–126

[50] Grimm V, Railsback S F, Vincenot C E, The ODD protocol for describing agent-based and other simulation models: A second update to improve clarity, replication, and structural realism. Journal of Artificial Societies and Social Simulation, 2020, 23(2): 7

[51] Francès G, Rubio-Campillo X, Lancelotti C, Decision making in agent-based models. In: European Conference on Multi-Agent Systems. Springer, Cham, 2014, 370–378

[52] Lui Y H, Mole D. The use of fundamental and technical analyses by foreign exchange dealers: Hong Kong evidence. Journal of International Money and Finance, 1998, 17(3): 535–545

[53] Menkhoff L, Taylor M P. The obstinate passion of foreign exchange professionals: technical analysis. Journal of Economic Literature, 2007, 45(4): 936–972

[54] Wang Z, Pan C. Research on the evolution of information strategy and herd behavior based on dynamic scale-free network. Chinese Journal of Management Science, 2018, 26(12): 66–77

[55] Lincoln J R, Wellman B, Berkowitz S D. Social structures: a network approach. Administrative Science Quarterly, 1990, 35(4):746

[56] Agnolucci P. The effect of financial constraints, technological progress and long-term contracts on tradable green certificates. Energy Policy, 2007, 35(6): 3347–3359

[57] Darmani A, Rickne A, Hidalgo A, When outcomes are the reflection of the analysis criteria: a review of the tradable green certificate assessments. Renewable & Sustainable Energy Reviews, 2016, 62: 372–381

[58] Zuo Y, Zhao X G, Zhang Y Z, From feed-in tariff to renewable portfolio standards: an evolutionary game theory perspective. Journal of Cleaner Production, 2019, 213: 1274–1289

[59] National Bureau of Statistics and National Development and Reform Commission. China Energy Statistics Yearbook 2018. Beijing: China Statistics Press, 2019

[60] Fagiolo G, Moneta A, Windrum P. A critical guide to empirical validation of agent-based models in economics: Methodologies, procedures, and open problems. Computational Economics, 2007, 30(3): 195–226

[61] Farmer J D, Joshi S. The price dynamics of common trading strategies. Journal of Economic Behavior & Organization, 2002, 49(2): 149–171

[62] Schaeffer G J, Boots M G, Mitchell C, Options for design of tradable green certificate systems. Petten, The Netherlands: ECN, 2000

Acknowledgment

This paper was supported by the Beijing Municipal Social Science Foundation (No. 16JDYJB031) and the Fundamental Research Funds for the Central Universities (No. 2020YJ008).

RIGHTS & PERMISSIONS

2021 Higher Education Press