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.

Key words： tradable green certificate; herding behavior; evolution; heterogeneous agent model; complex network

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).

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