Liberal Democracy in Eclipse: The Economy Post-February 2020, Nourished by Four Odds—Disease Phobia, Profanity in the Media, Unethical Profit Seekers and Tail Risk
The novel coronavirus (COVID-19) had spread across the globe since late February 2020 and posed a significant menace to public health worldwide. The travel industry, the public and private sectors, democratic activities in a liberal democracy, public policies, etc., have been affected. The important democratic activities affected have been election canvassing, the counting of votes, forming constitutional bodies, etc. The disaster had given rise to extraordinary circumstances attributed to noncooperation, coronaphobia, workplace conflicts, violations of contractual terms and frivolous lawsuits, technopiracy, and several maladaptive coping strategies post-March 25, 2020, which were inconspicuous before the date. Political satire in media podcasts has used sarcasm and sardonic comments to present a society, which unequivocally has pernicious effects on the cognitive behaviour of individuals of different age groups. Furthermore, unpremeditated remarks in relation to tail risk measures were frequently discovered in post-February 2020 political-economic discourse. On the one hand, those remarks/arguments have been difficult to justify convincingly, and, on the other, there is evidence of model misspecification. We produce anecdotal evidence of the fact that the adoption of heuristics in model formulation to simplify the framework affects model performance. In particular, models failed to capture the unusual flux of financial markets.
References
[1]
Aaltola, M. (2022). Understanding the Politics of Pandemic Emergencies in the Time of COVID-19. Routledge. https://doi.org/10.4324/9781003169147
[2]
Akhtari, M., Moreira, D., & Trucco, L. (2022). Political Turnover, Bureaucratic Turnover, and the Quality of Public Services. American Economic Review, 112, 442-493. https://doi.org/10.1257/aer.20171867
[3]
Artzner, P., Delbaen, F., Eber, J.-M., & Heath, D. (1999). Coherent Measures of Risk. Mathematical Finance, 9, 203-228. https://doi.org/10.1111/1467-9965.00068
[4]
Balkema, A. A., & De Haan, L. (1974). Residual Lifetime at Great Age. Annals of Probability, 2, 792-804. https://doi.org/10.1214/aop/1176996548
[5]
Bańbura, M., Giannone, D., Modugno, M., & Reichlin, L. (2013). Now-Casting and the Real-Time Data Flow. Working Paper 1564, European Central Bank. https://doi.org/10.1016/B978-0-444-53683-9.00004-9
[6]
Barone-Adesi, G., Engle, R. F., & Manchini, L. (2008). A GARCH Option Pricing Model with Historical Filtered Simulation. Review of Financial Studies, 21, 1223-1258. https://doi.org/10.1093/rfs/hhn031
[7]
Barone-Adesi, G., Giannopoulos, K., & Vosper, L. (1999). VaR without Correlations for Non-Linear Portfolios. Journal of Futures Markets, 19, 583-602. https://doi.org/10.1002/(SICI)1096-9934(199908)19:5<583::AID-FUT5>3.0.CO;2-S
[8]
Berkowitz, J., & O’Brien, J. (2002). How Accurate Are Value-at-Risk Models at Commercial Banks? Journal of Finance, 57, 1093-1111. https://doi.org/10.1111/1540-6261.00455
[9]
Bitar, M., & Tarazi, A. (2022). A Note on Regulatory Responses to COVID-19 Pandemic: Balancing Banks’ Solvency and Contribution to Recovery.Journal of Financial Stability, 60, Article 101009. https://doi.org/10.1016/j.jfs.2022.101009
[10]
Boucher, C. M., Daníelsson, J., Kouontchou, P. S., & Maillet, B. B. (2014). Risk Models-at-Risk. Journal of Banking & Finance, 44, 72-92. https://doi.org/10.1016/j.jbankfin.2014.03.019
[11]
Brodeur, A., Cook, N., & Heyes, A. (2020). Methods Matter: P-Hacking and Publication Bias in Causal Analysis in Economics. American Economic Review, 110, 3634-3460. https://doi.org/10.1257/aer.20190687
[12]
Bucciol, A., Quercia, S., & Sconti, A. (2021). Promoting Financial Literacy among the Elderly: Consequences on Confidence. Journal of Economic Psychology, 87, Article 102428. https://doi.org/10.1016/j.joep.2021.102428
[13]
Bulletin, Reserve Bank of India (2020a). Liquidity Management in the Time of COVID-19: An Outcomes Report (pp. 15-34).
[14]
Bulletin, Reserve Bank of India (2020b). Demystifying Equity Prices Using Dividend Discount Model: An Indian Context (pp. 123-132).
[15]
Business Standard (2020). Coronavirus: Govt Restricts Exports of 26 Active Pharma Drugs, Formulations. Business Standard, 3 March 2020.
[16]
Byström, H. N. E. (2004). Managing Extreme Risks in Tranquil and Volatile Markets Using Conditional Extreme Value Theory. International Review of Financial Analysis, 13, 133-152. https://doi.org/10.1016/j.irfa.2004.02.003
[17]
Chan, K. F., & Gray, P. (2006). Using Extreme Value Theory to Measure Value-at-Risk for Daily Electricity Spot Prices. International Journal of Forecasting, 22, 283-300. https://doi.org/10.1016/j.ijforecast.2005.10.002
[18]
Choi, S.-Y. (2021). Analysis of Stock Market Efficiency during Crisis Periods in the US Stock Market: Differences between the Global Financial Crisis and COVID-19 Pandemic. Physica A: Statistical Mechanics and Its Applications, 574, Article 125988. https://doi.org/10.1016/j.physa.2021.125988
[19]
Daníelsson, J. (2002). The Emperor Has No Clothes: Limits to Risk Modelling. Journal of Banking & Finance, 26, 1273-1296. https://doi.org/10.1016/S0378-4266(02)00263-7
[20]
Economic and Political Weekly (2021a). Engineering Flexibility without Accountability: Changing Chief Ministers Reflects a Deep Damage to Substantive Accountability in a Democracy. Editorials, 18 September 2021.
[21]
Economic and Political Weekly (2021b). Indefensible Political Acrimony. Editorials, 11 September 2021.
[22]
Eguia, J. X., & Xefteris, D. (2021). Implementation by Vote-Buying Mechanisms. American Economic Review, 111, 2811-2828. https://doi.org/10.1257/aer.20190197
[23]
Eliaz, K., Spiegler, R., & Weisss, Y. (2021). Cheating with Models. American Economic Review, 3, 417-434. https://doi.org/10.1257/aeri.20200635
[24]
Fox, H. C., Tuit, K. L., & Sinha, R. (2013). Stress System Changes Associated with Marijuana Dependence Increase Craving for Alcohol & Cocaine. Human Psychopharmacology, 28, 40-53. https://doi.org/10.1002/hup.2280
[25]
Gopinath, G. (2020a). The Great Lockdown: Worst Economic Downturn Since the Great Depression. The Daily Tribute, 15 April 2020.
[26]
Gopinath, G. (2020b). COVID-19 Crisis Exacerbates Globalisation Worries. Deccan Herald, 25 June 2020.
[27]
Greene, A. (2020). Emergency Powers in a Time of Pandemic. Bristol University Press. https://doi.org/10.46692/9781529215434
[28]
Greene, C. M., Nash, R. A., & Murphy, G. (2021). Misremembering Brexit: Partisan Bias and Individual Predictors of False Memories for Fake News Stories among Brexit Voters. Memory, 29, 587-604. https://doi.org/10.1080/09658211.2021.1923754
[29]
Hausmann, R. (2014). Venezuela before Chavez: Anatomy of an Economic Collapse. Pennsylvania State University Press. https://doi.org/10.1515/9780271064628
[30]
Hogg, R. V., & Klugman, S. A. (1984). Loss Distributions. Wiley. https://doi.org/10.1002/9780470316634
[31]
Holsti, O. R. (1962). The Belief System and National Images: A Case Study. The Journal of Conflict Resolution, 6, 244-252. https://doi.org/10.1177/002200276200600306
[32]
Jorion, P. (2002). Value at Risk: The New Benchmark for Controlling Market Risk. McGraw-Hill.
[33]
Joshi, K. V., & Patel, N. M. (2021). A CNN Based Approach for Crowd Anomaly Detection. International Journal of Next-Generation Computing, 12, 1-11.
[34]
Kakade, K., Jain, I., & Mishra, A. K. (2022). Value-at-Risk Forecasting: A Hybrid Ensemble Learning GARCH-LSTM Based Approach. Resources Policy, 78, Article 102903. https://doi.org/10.1016/j.resourpol.2022.102903
[35]
Kennedy, S., & Dodge, S. (2020). How Unconventional Monetary Policy Turned Conventional. BloombergQuint, 13 September 2020.
[36]
Kerkhof, J., & Melenberg, B. (2004). Backtesting for Risk-Based Regulatory Capital. Journal of Banking & Finance, 28, 1845-1865. https://doi.org/10.1016/j.jbankfin.2003.06.007
[37]
Kim, D. K. D., & Kreps, G. L. (2020). An Analysis of Government Communication in the United States during the COVID-19 Pandemic: Recommendations for Effective Government Health Risk Communication. WHMP, 12, 398-412. https://doi.org/10.1002/wmh3.363
[38]
Lehtinen, A., & Kuorikoskit, J. (2007). Computing the Perfect Model: Why Do Economists Shun Simulation? Philosophy of Science, 74, 304-329. https://doi.org/10.1086/522359
[39]
Mabrouk, S., & Saadi, S. (2012). Parametric Value-at-Risk Analysis: Evidence from Stock Indices. The Quarterly Review of Economics and Finance, 52, 305-321. https://doi.org/10.1016/j.qref.2012.04.006
[40]
Madison, T. (2020). The FEAR-19 Pandemic: How Lies, Damn Lies, and Fake Statistics Created a Pandemic of Fear That Spread Faster and Created More Damage than COVID-19 Ever Could Have by Itself. A Different Animal LLC.
[41]
Majumder, D. (2016). Proposing Model Based Risk Tolerance Level for Value-at-Risk—Is It a Better Alternative to BASEL’s Rule Based Strategy? Journal of Contemporary Management, 5, 71-85.
[42]
Majumder, D. (2018). Value-at-Risk Based on Time-Varying Risk Tolerance Level. Theoretical Economics Letters, 8, 111-118. https://doi.org/10.4236/tel.2018.81007
[43]
Majumder, D. (2021). I Was Learning Economics at the Cost of the Economy. Theoretical Economics Letters, 11, 615-642. https://doi.org/10.4236/tel.2021.113041
[44]
Majumder, D. (2023). Subjectivity in Conventional Tail Measures: An Exploratory Model with “Risks & Biases”.Finance Research Letters, 55, Article 103951. https://doi.org/10.1016/j.frl.2023.103951
[45]
Manganelli, S., & Engle, R. F. (2004). A Comparison of Value-at-Risk Models in Finance. In G. Szegö (Ed.), Risk Measures for the 21st Century. John Wiley & Sons.
[46]
Marshall, A. (1890). Principles of Economics. Macmillan.
[47]
McNeil, A., & Frey, R. (2000). Estimation of Tail-Related Risk Measures for Heteroscedastic Financial Time Series: An Extreme Value Approach. Journal of Empirical Finance, 7, 271-300. https://doi.org/10.1016/S0927-5398(00)00012-8
[48]
Mint (2020). RBI Only Central Bank to Set Up Quarantine Facility for Continued Services: Guv Das. Mint, 6 August 2020.
[49]
Mishra, P. K. (2020). COVID-19, Black Swan Events and the Future of Disaster Risk Management in India. Progress in Disaster Science, 8, Article 100137. https://doi.org/10.1016/j.pdisas.2020.100137
[50]
Mishra, P. K., & Rajan, R. (2016). Rules of the Monetary Game. RBI WPS (DEPR): 04/201.
[51]
Neufeld, M., Lachenmeier, D. W., Ferreira-Borges, C., & Rehm, J. (2020). Is Alcohol an “Essential Good” during COVID-19? Yes, but Only as a Disinfectant! Alcohol, Clinical and Experimental Research, 44, 1906-1909. https://doi.org/10.1111/acer.14417
[52]
Ognyanova, K., Lazer, D., Robertson, R. E., & Wilson, C. (2020). Misinformation in Action: Fake News Exposure Is Linked to Lower Trust in Media, Higher Trust in Government When Your Side Is in Power. The Harvard Kennedy School Misinformation, 1, 1-19. https://doi.org/10.37016/mr-2020-024
[53]
Pathania, R. (2020). Literacy in India: Progress and Inequality. Bangladesh e-Journal of Sociology, 17, 57-64.
[54]
Pennycook, G., & Rand, D. G. (2021). The Psychology of Fake News. Trends in Cognitive Sciences, 25, 388-402. https://doi.org/10.1016/j.tics.2021.02.007
[55]
Perignon, C., & Smith, D. R. (2010). The Level and Quality of Value-at-Risk Disclosure by Commercial Banks. Journal of Banking & Finance, 34, 362-377. https://doi.org/10.1016/j.jbankfin.2009.08.009
[56]
Pickands, J. (1975). Statistical Inference Using Extreme Order Statistics. The Annals of Statistics, 3, 119-131. https://doi.org/10.1214/aos/1176343003
[57]
Pradhan, S. K. (2023). International Exposure of Indian Banks and Non-Banks during Post-Reform Period: An Empirical Study. Ph.D. Thesis, University of Calcutta.
[58]
Rajan, R. (2010). Fault Lines: How Hidden Fractures Still Threaten the World Economy. Princeton University Press. https://doi.org/10.1515/9781400839803
[59]
Reiss, R.-D., & Thomas, M. (1997). Statistical Analysis of Extreme Values. Birkhäuser. https://doi.org/10.1007/978-3-0348-6336-0
[60]
Rogoff, K. (2021). Fiscal Sustainability in the Aftermath of the Great Pause. Journal of Policy Modeling, 43, 783-793. https://doi.org/10.1016/j.jpolmod.2021.02.007
[61]
Roland, G. (2020). The Deep Historical Roots of Modern Culture: A Comparative Perspective. Journal of Comparative Economics, 48, 483-508. https://doi.org/10.1016/j.jce.2020.02.001
[62]
Roy, I. (2011). Estimating Value at Risk (VaR) Using Filtered Historical Simulation in the Indian Capital Market. Reserve Bank of India Occasional Paper, 32, 81-98.
[63]
Salvatore, D. (2021). The U.S. and the World Economy after COVID-19. Journal of Policy Modeling, 43, 728-738. https://doi.org/10.1016/j.jpolmod.2021.02.002
[64]
Schluter, M. (1994). The Rise and Fall of Nations: How Far Can Christians Interpret History? Cambridge Paper, 3, 42-45. https://static1.squarespace.com/static/62012941199c974967f9c4ad/t/65ae9ec3fc62146576ebceeb/1705942725023/Vol03 no3 The Rise and Fall of Nations.pdf
[65]
Seidl, H., & Seyrich, F. (2023). Unconventional Fiscal Policy in a Heterogeneous-Agent New Keynesian Model. Journal of Political Economy Macroeconomics, 1, 633-664
[66]
Shaik, M., & Padmakumari, L. (2022). Value-at-Risk (VAR) Estimation and Backtesting during COVID-19: Empirical Analysis Based on BRICS and US Stock Markets. Investment Management and Financial Innovations, 19, 51-63. https://doi.org/10.21511/imfi.19(1).2022.04
[67]
Singh, T. (2023). The Authoritarian Roots of India’s Democracy. Journal of Democracy, 34, 133-143. https://doi.org/10.1353/jod.2023.a900439
[68]
Stiglitz, J. (2021a). Lessons from COVID-19 and Trump for Theory and Policy (Paper). Journal of Policy Modeling, 43, 749-760. https://doi.org/10.1016/j.jpolmod.2021.02.004
[69]
Stiglitz, J. (2021b). Globalization in the Aftermath of the Pandemic and Trump. Journal of Policy Modeling, 43, 794-804. https://doi.org/10.1016/j.jpolmod.2021.02.008
[70]
Stolle, D., & Harell, A. (2013). Social Capital and Ethno-Racial Diversity: Learning to Trust in an Immigrant Society. Political Studies, 61, 42-66. https://doi.org/10.1111/j.1467-9248.2012.00969.x
[71]
Szegö, G. (2002). Measures of Risk. Journal of Banking & Finance, 26, 1253-1272. https://doi.org/10.1016/S0378-4266(02)00262-5
[72]
Taleb, N. N. (2007). The Black Swan: The Impact of the Highly Improbable. Penguin.
[73]
Taylor v Caldwell (1863). EWHC J1 (QB), 3 B & S 826, 122 ER 309, Court of Queen’s Bench. https://www.bailii.org/ew/cases/EWHC/QB/1863/J1.html
[74]
The Economic Times (2020). India Will Be Under Complete Lockdown for 21 Days: Narendra Modi. The Economic Times, 25 March 2020.
[75]
The Economic Times (2021). India’s Export Restrictions, Import Duties on Farm Goods, Reforms Come Up in WTO Review. The Economic Times, 7 January 2021.
[76]
The Economist (2019). Tech’s Raid on the Bank: Digital Disruption Is Coming to Banking at Last. The Economist.
[77]
The Economic Times (2019). Provision Coverage Ratio of Banks Has Improved to More than 70%. The Economic Times.
[78]
The Times of India (2020). Private Details of 73 Lakh Indians Exposed in BHIM Data Leak, NPCI Issues Denial. The Times of India, 1 June 2020.
[79]
Wise, A., & Noble, G. (2016). Convivialities: An Orientation. Journal of Intercultural Studies, 37, 423-431. https://doi.org/10.1080/07256868.2016.1213786
[80]
Wu, J., Chao, Z., & Yun, C. (2022). Analysis of Risk Correlations among Stock Markets during the COVID-19 Pandemic. International Review of Financial Analysis, 83, Article 102220. https://doi.org/10.1016/j.irfa.2022.102220
