ISSN: 2056-3736 (Online Version) | 2056-3728 (Print Version)

Foreign Aid and Dutch Disease in Thailand

Hiroaki Sakurai

Correspondence: Hiroaki Sakurai,

Ministry of Land, Infrastructure, Transportation and Tourism, Government of Japan

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This paper examines the economic impact of foreign aid, specifically whether it leads to Dutch disease, in Thailand between 1972 and 2014, using a VAR model, together with the Granger causality test and the impulse response test. Few previous studies have been made of Southeast Asian countries even though Thailand has experienced rapid economic growth using foreign aid to construct infrastructure, and by introducing foreign direct investment into manufacturing industries. The causality and impulse response tests indicate that Dutch disease has not occurred; the impact of foreign aid proved positive, as there was little room to increase consumption and the aid contributed directly to capital accumulation.


  Foreign Aid, Dutch Disease, Thailand


Burke, P.J. and F.Z. Ahmadi-Esfahani (2006) “Aid and growth: A study of South East Asia” Journal of Asian Economics 17(2), 350–362.

Burnside, C., and D. Dollar (2000) “Aid, Policies, and Growth” American Economic Review 90(4), 847-868.

Corden, W.M. and J.P. Neary (1982) “Booming sector and de-industrialisation in a small open economy” Economic Journal 92, 825–848.

Dalgaard, C., H. Hansen, and F. Trap (2004) “On the Empirics of Foreign Aid and Growth” Economic Journal 114, F191-F216.

Dufrenot, G., J., and E.B. Yehoue (2005) “Real Exchange Rate Misalignment: A Panel Co-Integration and Common Factor Analysis” IMF Working Paper, WP/05/164.

Easterly, W., R. Levine, and D. Roodman (2004) “New Data, New Doubts: Comment on ‘Aid, Policies and Growth (2000)’ by Burnside and Dollar” American Economic Review 94(3), 774-780.

Easterly, W. (2007) “Was Development Assistance a Mistake?” American Economic Review 97(2), 328-332.

Elbadawi, A. E., L. Kalttani, and K. Schmidt-Hebbel (2008) “Foreign Aid, the Real Exchange Rate, and Economic Growth in the Aftermath of Civil Wars” World Bank Economic Review 22(1), 113-140.

Fielding, D., and F. Gibson (2013) “Aid and Dutch Disease in Sub-Saharan Africa” Journal of African Economies 22(1), 1-21.

Godfrey, M., Sophal, C., Kato, T., Piseth, L. V., Dorina, P., Saravy T., Savora, T., and Sovannarith, S. (2002) “Technical Assistance and Capacity Development in an Aid-dependent Economy: The Experience of Cambodia” World Development 30(3), 355-373.

Hansen, H., and F. Trap (2001) “Aid and Growth Regressions” Journal of Development Economics 64, 547-570.

Rajan, R.G. and A. Subramanian (2011) “Aid, Dutch disease, and manufacturing growth” Journal of Development Economics 94(1), 106–118.

Tekin, R.B. (2012) “Development Aid, Openness to Trade and Economic Growth in Least Developed Countries: Bootstrap Panel Granger Causality Analysis” Social and Behavioral Sciences 62, 716-721.

Basel III impact on the Italian banking sector

Vasilios Sogiakas

Correspondence: Vasilios Sogiakas,

Adam Smith Business School, University of Glasgow, United Kingdom

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This paper examines the incentives and the effectiveness of tighter regulation of the Italian banks in terms of their profitability. Using balance and off-balance sheet data I focus on the capital requirements and the liquidity characteristics of the banking sector by the convenient Tier 1 ratio and the Basel III long-term Net Stable Funding Ratio (NSFR), respectively. The empirical findings of the paper underline the important role that the NSFR has as a preventive tool for potential bank failures while addresses the incentives behind the enforcement of higher Tier 1 ratios as a way for more risk averting profiles mainly during turbulent periods.


  Basel III; NSFR; banking efficiency; financial crisis


Bank of International Settlements, 2012, Basel III regulator consistency assessment (Level 2) Preliminary report: European Union, October 2012.Bank of International Settlements, 2013, Basel III: The Liquidity Coverage Ratio and liquidity risk monitoring tools, January 2013.Bank of International Settlements, 2014, “Consultative Document”, Basel III: The Net Stable Funding Ratio, April 2014.Delis, M. and Panagiotis Staikouras, 2011, Supervisory Effectiveness and Bank Risk, Review of Finance, Vol. 15, pp. 511-543.

Demirguc-Kunt, A., Enrica Detragiache and Ouarda Merrouche, 2013, Bank Capital: Lessons from the Financial Crisis, Journal of Money, Credit and Banking, Vol. 45, No. 6, pp. 1147-1164.Dietrich A., Kurt Hess, Gabrielle Wanzenried, 2014, The good and bad news about the new liquidity rules of Basel III in Western European counties, Journal of Banking and Finance, Vol. 44, pp. 13-25.Feess, E. and Ulrich Hege, 2012, The Basel Accord and the Value of Bank Differentiation, Review of Finance, Vol. 16, pp. 1043–1092.Gaston, G. A., and Ingmar, Schumacher, 2013, Bank liquidity risk and monetary policy. Empirical Evidence on the impact of Basel III liquidity standards, International Review of Applied Economics, Vol. 27, No. 5, pp. 633-655.Otker-Robe, I., Pazarbasioglu, C., 2010, Impact of Regulatory Reforms on Large and Complex Financial Institutions, IMF Staff Position Note.Valascas, F. and Jens Hagendorff, 2013, The Risk Sensitivity of Capital Requirements: Evidence from an International Sample of Large Banks, Review of Finance, Vol. 17, pp. 1947-1988.

The impact of public policies and institutions on economic growth in developing countries: New empirical evidence

Minh Quang Dao

Correspondence: Minh Quang Dao,

Eastern Illinois University, USA

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This paper examines the impact of public policies and institutions on economic growth in developing countries. Based on data from the World Bank for the 2000-2015 period and a sample of thirty-nine low-income and lower middle-income economies we find that the growth rate of GDP is dependent on a country’s economic management of its debt policy, its structural policies regarding the financial sector and the business regulatory environment, and its policies for social inclusion and equity dealing with gender equality, with building human resources, and with social protection and labor, along with the growth rates of inputs such as land, physical capital, general government consumption, and net exports. We observe that the coefficient estimates of two explanatory variables, namely, the structural policies regarding the financial sector and the policies for social inclusion and equity dealing with gender equality, do not have their expected sign, possibly to the collinearity between the structural policies regarding the financial sector and the debt policy variable, the business regulatory environment variable, the building human resources variable, and the social protection and labor variable and that between the gender equality variable and the business regulatory environment variable, the building human resources variable, and the social protection and labor variable. We also note that the business regulatory variable is not significant using the t-test, but its exclusion from the model results in a decrease in its explanatory power as measured by the adjusted coefficient of determination. We suspect that this is also due to the collinearity between this variable and three policies for social inclusion and equity variables. Statistical results of such empirical examination will assist governments in developing countries focus on appropriate policies dealing with the economic management of debt policy, those of a structural nature regarding the financial sector and the business regulatory environment, and those for social inclusion and equity such as improving gender equality, building human resources and providing social protection and labor in order to foster economic growth. Public sector management and institutions, on the other hand, do not seem to influence a developing country’s rate of economic growth.


  Public Policies and Institutions, GDP Growth, Developing Countries.


Acemoglu, D., Johnson, S. and Robinson, J.A. (2001), “The Colonial Origins of Comparative Development: An Empirical Investigation,” American Economic Review, 91 (5), 1369-1401.Dao, M.Q. (2011), “Institutions and Development in Developing Countries: An Empirical Assessment,” Perspectives on Global Development and Technology, 10 (2), 327-338.________ (2013), “Public Policies, Business Environment, and Economic Growth in Developing Countries,” International Journal of Research in Commerce, Economics &Management, Vol. No. 3, Issue No. 6 (June 2013): 1-4.Djankov, S., McLiesh, C., and Ramalho, R. M. (2006), “Regulation and Growth,” Economics Letters 92 (3), 395-401.Frankel, J.A, and Romer, D. (1999), “Does Trade Cause Growth?” American Economic Review 89 (3), 379-399.Gallup, J.L., Sachs, J.D., and Mellinger, A. (1999), “Geography and Economic Development,” CID Working Papers 1, Center for International Development at Harvard University, March.Gillanders, R. and Whelan, K. (2010), “Open for Business? Institutions, Business Environment and Economic Development,” University College Dublin School of Economics Working Papers No. 20104, December.Glaeser, E.L., La Porta, R., Lopez-de-Silanes, F., and Shleifer, A. (2004), “Do Institutions Cause Growth?” NBER Working Paper 10568.Hall, R.E. and Jones, C.I. (1999), “Why Do Some Countries Produce So Much More Output per Worker than Others?” Quarterly Journal of Economics 114 (1), 83-116.

Kaufmann, D., Kraay, A., and Zoido-Lobatόn (1999), “Governance Matters,” Washington, D.C.: World Bank Policy Research Working Paper No. 2196.Knack, S. and Keefer, P. (1995), “Institutions and Economic Performance: Cross-country Test Using Alternative Institutional Methods,” Economics and Politics, Vol. 7, No. 3, pp. 207-27.Mauro, P. (1995), “Corruption and Economic Growth,” Quarterly Journal of Economics, Vol. 110, No. 3, pp. 682-712.McArthur, J.W. and Sachs, J.D. (2001). “Institutions and Geography: Comment on Acemoglu, Johnson and Robinson (2000),” Cambridge, MA: National Bureau of Economic Research Working Paper No. 8114.Rodrik, D., Subramanian, A., and Trebbi, F. (2004), “Institutions Rule: The Primacy of Institutions over Geography and Integration in Economic Development,” Journal of Economic Growth 9 (2), 131-165.Sachs, J.D. (2003) "Institutions Don’t Rule: Direct Effects of Geography on Per Capita Income," Cambridge, MA: National Bureau of Economic Research Working Paper No. 9490.Tanzi, V. and Davoodi, H. (1998), “Does Corruption Affect Income Inequality and Poverty?” Washington, D.C.: International Monetary Fund Working Paper No. 98/76.World Bank.(2016), World Development Indicators, Oxford University Press: New York.__________ (2013), World Development Report 2013: Jobs, Oxford University Press: New York.

On the efficiency of various expansionary fiscal policies and cuts in taxation rates in order to sustain economic activity

Séverine Menguy

Correspondence: Séverine Menguy,

Université Paris Descartes, France

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We use a simple DSGE model in order to evaluate the efficiency of various fiscal policies intended to sustain economic activity and growth. A decrease in the consumption taxation rate appears as the most efficient fiscal policy. Indeed, as goods are then less expansive, it would imply an increase in the same proportion of all components of economic activity: private consumption and investment, as well as public expenditure. Besides, it would also strongly favor public investment in the composition of public expenditure, in order to increase the productivity of private factors and to satisfy the higher global demand. In comparison, a decrease in the capital taxation rate would reduce the capital cost, and it would favor private and public investment. However, the effect would be minor on private consumption and even negative on public consumption expenditure; the increase in global economic activity would then be more moderate. Finally, a decrease in the labor taxation rate would not be able to increase private economic activity, in the framework of our model, and it would favor public consumption to the detriment of the most productive public investment expenditure.


  DSGE model, budgetary policy, consumption taxation rate, capital taxation rate, labor taxation rate.


Alesina A. and S. Ardagna (2010) Large Changes in Fiscal Policy: Taxes versus Spending. In J. R. Brown (ed.), Tax Policy and the Economy, vol.24, 35-68.Ardagna S. (2004) Fiscal Stabilizations: When do they Work and Why?. European Economic Review, vol.48, n°5, 1047-1074.Bhattarai K. and D. Trzeciakiewicz (2017) Macroeconomic Impacts of Fiscal Policy Shocks in the UK: A DSGE Analysis. Economic Modelling, vol.61, February, 331-338.Baxter M. and R. King (1993) Fiscal Policy in General Equilibrium, American Economic Review, vol.83, n°3, June, 315-334.Blanchard O. and R. Perotti (2002) An Empirical Characterization of the Dynamic Effects of Changes in Government Spending and Taxes on Output. The Quarterly Journal of Economics, vol.117, n°4, 1329-68.Bouakez H. and N. Rebei (2007) Why does Private Consumption Rise after a Government Spending Shock?. Canadian Journal of Economics, vol.40, n°3, 954–979.Burnside C., M. Eichenbaum and J. Fisher (2004) Fiscal Shocks and their Consequences. Journal of Economic Theory, vol.115, n°1, March, 89-117.Carvalho V. M. and M. M. F. Martins (2011) Macroeconomic Effects of Fiscal Consolidations in a DSGE Model for the Euro Area: Does Composition Matter?. FEP Working Papers, n°421, Universidade do Porto, Facultade de Economia do Porto.Coenen G. and R. Straub (2005) Does Government Spending Crowd in Private Consumption? Theory and Empirical Evidence for the Euro Area. International Finance, vol.8, n°3, 435-470.Coenen G., M. Mohr and R. Straub (2008) Fiscal Consolidation in the Euro Area: Long-Run Benefits and Short-Run Costs. Economic Modelling, vol.25, n°5, 912-932.Davig T. and E. M. Leeper (2011) Monetary-Fiscal Policy Interactions and Fiscal Stimulus. European Economic Review, vol.55, n°2, February, 211–227.De Haan J. and W. Romp (2005) Public Capital and Economic Growth: A Critical Survey. European Investment Bank Papers, vol.10, n°1, 40-71.Drautzberg T. and H. Uhlig (2011) Fiscal Stimulus and Distortionary Taxation. The Milton Friedman Institute for Research in Economics, Discussion Paper, n°2011-005.Edelberg W., M. Eichenbaum and J. Fisher (1999) Understanding the Effects of a Shock to Government Purchases. Review of Economics Dynamics, vol.2, n°1, January, 166-206.Fatas A. and I. Mihov (2001) The Effects of Fiscal Policy on Consumption and Employment: Theory and Evidence. CEPR Discussion Papers, n°2760.Finn M. G. (1998) Cyclical Effects of Government’s Employment and Goods Purchases. International Economic Review, vol.39, n°3, August, 635-657.Forni L., L. Monteforte and L. Sessa (2009) The General Equilibrium Effects of Fiscal Policy: Estimates for the Euro Area. Journal of Public Economics, vol.93, n°3-4, 559-585.Furlanetto F. (2007) Fiscal Shocks and the Consumption Response when Wages are Sticky. Cahiers de Recherches Economiques du Département d'Econométrie et d'Economie Politique (DEEP) 07.11, Université de Lausanne, Faculté des HEC.Galí J. (2008) Monetary Policy, Inflation, and the Business Cycle: An Introduction to the New Keynesian Framework. Princeton: Princeton University Press.Galí J., J. D. López-Salido and J. Vallés (2007) Understanding the Effects of Government Spending on Consumption. Journal of the European Economic Association, vol.5, n°1, 227-270Leeper E. M., T. B. Walker and S.-C. S. Yang (2010) Government Investment and Fiscal Stimulus. Journal of Monetary Economics, vol.57, n°8, 1000–1012.Leeper E. M., N. Traum and T. B. Walker (2011) Clearing up the Fiscal Multiplier Morass. NBER Working paper, n°17444, September.

Ludvigson S. (1996) The Macroeconomic Effects of Government Debt in a Stochastic Growth Model. Journal of Monetary Economics, vol.38, n°1, August, 25-45.Mertens K. and M. O. Ravn (2011) Understanding the Aggregate Effects of Anticipated and Unanticipated Tax Policy Shocks. Review of Economic Dynamics, vol.14, n°1: 27-54.Pappa E. (2004) New Keynesian or RBC Transmission? The Effects of Fiscal Policy in Labor Markets. Working Papers 293, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.Pappa E. (2009) The Effects of Fiscal Shocks on Employment and the Real Wage. International Economic Review, vol.50, n°1, February, 217-244.Perotti R. (2004) Public Investment: Another (Different) Look. Working Papers 2977, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.Sims E. and J. Wolff (2013) The Output and Welfare Effects of Government Spending Shocks over the Business Cycle. NBER Working Papers, n°19749, December.Smets F. and R. Wouters (2003) An Estimated Stochastic Dynamic General Equilibrium Model of the Euro Area. Journal of the European Economic Association, vol.1, n°5, 1123-1175.Straub R. and I. Tchakarov (2007) Assessing the Impact of Change in the Composition of Public Spending: A DSGE Approach. ECB Working Paper Series, n°795, European Central Bank, August.Woodford M. (2003) Interest and Prices: Foundations of a Theory of Monetary Policy, Princeton: Princeton University Press.Zubairy S. (2014) On Fiscal Multipliers: Estimates from a Medium Scale DSGE Model. International Economic Review, vol.55, n°1, February, 169-195.

VIX Index and Stock Returns Following Large Price Moves

Andrey Kudryavtsev

Correspondence: Andrey Kudryavtsev,

Department of Economics and Management, The Max Stern Yezreel Valley College, Israel

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My study explores the effect of future volatility expectations, embedded in VIX index, on large daily stock price changes and on subsequent stock returns. Following both psychological and financial literature claiming that good (bad) mood may cause people to perceive positive (negative) future outcomes as more probable and that the changes in the value of VIX may be negatively correlated with contemporaneous investors’ mood, I hypothesize that if a major positive (negative) stock price move takes place on a day when the value of VIX falls (rises), then its magnitude may be amplified by positive (negative) investors' mood, creating price overreaction to the initial company-specific shock, which may result in subsequent price reversal. In line with my hypothesis, I document that both positive and negative large price moves accompanied by the opposite-sign contemporaneous changes in VIX are followed by significant reversals on the next two trading days and over five- and twenty-day intervals following the event, the magnitude of the reversals increasing over longer post-event windows, while large stock price changes taking place on the days when the value of VIX moves in the same direction are followed by non-significant price drifts. The results remain robust after accounting for additional company (size, beta, historical volatility) and event-specific (stock's return and trading volume on the event day) factors, and are stronger for small and volatile stocks.


  Behavioral Finance; Large Price Changes; Mood; Overreaction; Stock Price Reversals; Volatility Expectations; VIX.


Atkins, A.B., and E.A. Dyl, 1990, Price Reversals, Bid-Ask Spreads and Market Efficiency, Journal of Financial and Quantitative Analysis, 25(4), 535-547.

Avramov, D., T. Chordia, and A. Goyal, 2006, Liquidity and Autocorrelations in Individual Stock Returns, Journal of Finance, 61(5), 2365-2394.

Baker, M., and J. Wurgler, 2006, Investor Sentiment and the Cross-Section of Stock Returns, Journal of Finance, 61(4), 1645-1680.

Bremer, M., T. Hiraki, and R.J. Sweeney, 1997, Predictable Patterns after Large Stock Price Changes on the Tokyo Stock Exchange, Journal of Financial and Quantitative Analysis, 32(3), 345-365.

Bremer, M., and R.J. Sweeney, 1991, The Reversal of Large Stock-Price Decreases, Journal of Finance, 46(2), 747-754.

Brown, K.C., W.V. Harlow, and S.M. Tinic, 1988, Risk Aversion, Uncertain Information, and Market Efficiency, Journal of Financial Economics, 22(2), 355-385.

Cao, M., and J. Wei, 2005, Stock Market Returns: A Note on Temperature Anomaly, Journal of Banking and Finance, 29(6), 1559-1573.

Chan, W.S., 2003, Stock Price Reaction to News and No-News: Drift and Reversal after Headlines, Journal of Financial Economics, 70(2), 223-260.

Conrad, J.S., A. Hameed, and C. Niden, 1994, Volume and Autocovariances in Short-Horizon Individual Security Returns, Journal of Finance, 49(4), 1305-1329.

Constans, J.I., and A.M. Mathews, 1993, Mood and Subjective Risk of Future Events, Cognition and Emotion, 7(6), 545-560.

Cooper, M., 1999, Filter Rules Based on Price and Volume in Individual Security Overreaction, Review of Financial Studies, 12(4), 901–935.

Cox, D.R., and C.R. Peterson, 1994, Stock Returns following Large One-Day Declines: Evidence on Short-Term Reversals and Longer-Term Performance, Journal of Finance, 49(1), 255-267.

Daniel, K., D. Hirshleifer, and A. Subrahmanyam, 1998, Investor Psychology and Security Market Under- and Overreactions, Journal of Finance, 53(6), 1839-1885.

DeBondt, W.F.M., and R. Thaler, 1985, Does the Stock Market Overreact? Journal of Finance, 40(3), 793–805.

DeBondt, W.F.M., and R. Thaler, 1987, Further Evidence on Investor Overreaction and Stock Market Seasonality, Journal of Finance, 42(3), 557-580.

Dichev, I.D., and T.D. Janes, 2003, Lunar Cycle Effects in Stock Returns, Journal of Private Equity, 6(4), 8-29.

Fehle, F., and V. Zdorovtsov, 2003, Large Price Declines, News, Liquidity and Trading Strategies: An Intraday Analysis, Working Paper, University of South Carolina.

Forgas, J.P., 1992, Affect in Social Judgment and Decisions: A Multi-Process Model, Advances in Experimental Psychology, 25, 227-275.

Hamelink, F., 1999, Systematic Patterns Before and After Large Price Changes: Evidence from High Frequency Data from the Paris Bourse, Working Paper, Groupe HEC Paris.

Hilgard’s Introduction to Psychology, Thirteenth Edition, 2000, Rita L. Atkinson, Richard C. Atkinson, Edward E. Smith, Daryl J. Bern and Susan Nolen-Hoeksema, Harcourt College Publishers.

Hirshleifer, D., and T. Shumway, 2003, Good Day Sunshine: Stock Returns and the Weather, Journal of Finance, 58(3), 1009-1032.

Hong, H., and J.C. Stein, 1999, A Unified Theory of Underreaction, Momentum Trading, and Overreaction in Asset Markets, Journal of Finance, 54(6), 2143-2184.

Howe, J.S., 1986, Evidence on Stock Market Overreaction, Financial Analysts Journal, 42(3), 74-77.

Ikenberry, D.L., and S. Ramnath, 2002, Underreaction to Self-Selected News Events: The case of Stock Splits, Review of Financial Studies, 15(2), 489–526.

Isen, A.M., 2000, Positive Affect and Decision Making, In M. Lewis & J. M. Havieland (Eds.), Handbook of Emotions, 2, 417-435, London: Guilford.

Isen, A.M., T.E. Shalker, M. Clark, and L. Karp, 1978, Affect, Accessibility of Material in Memory, and Behavior: A cognitive Loop? Journal of Personality and Social Psychology, 36(1), 1-12.

Jegadeesh, N., and S. Titman, 1993, Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency, Journal of Finance, 48(1), 65-91.

Johnson, E.J., and A. Tversky, 1983, Affect, Generalization, and the Perception of Risk, Journal of Personality and Social Psychology, 45(1), 20-31.

Kahneman, D., and J. Riis, 2006, Living and Thinking About it: Two Perspectives on Life, The Science of Well-Being, F. Huppert, B. Keverne, & N. Baylis (Eds.), Oxford University Press.

Kamstra, M.J., L.A. Kramer, and M.D. Levi, 2000, Losing Sleep at the Market: The Daylight-Savings Anomaly, American Economic Review, 90(4), 1005–1011.

Kamstra, M.J., L.A. Kramer, and M.D. Levi, 2003, Winter Blues: A Sad Stock Market Cycle, American Economic Review, 93(1), 324–343.

Kliger, D., G. Gurevich, and A. Haim, 2012, When Chronobiology Met Economics – Seasonal Affective Disorder and the Demand for Initial Public Offerings, Journal of Neuroscience, Psychology and Economics, 5(3), 131-151.

Kliger, D, and A. Kudryavtsev, 2013, Volatility Expectations and the Reaction to Analyst Recommendations, Journal of Economic Psychology, 37(1), 1-6.

Kliger, D., and O. Levy, 2003a, Mood-induced Variation in Risk Preferences, Journal of Economic Behavior and Organization, 52(4), 573-584.

Kliger, D., and O. Levy, 2003b, Mood and Judgment of Subjective Probabilities: Evidence from the U.S. Index Option Market, European Finance Review, 7(2), 235-248.

Kliger, D., and O. Levy, 2008, Mood Impacts on Probability Weighting Functions: “Large-Gamble” Evidence, Journal of Socio-Economics, 37(4), 1397-1411.

Krivelyova, A., and C. Robotti, 2003, Playing the Field: Geomagnetic Storms and International Stock Returns, Federal Reserve Bank of Atlanta, Working Paper 2003-5b.

Larson, S.J., and J. Madura, 2003, What Drives Stock Price Behavior Following Extreme One-Day Returns, Journal of Financial Research, 26,(1), 113-27.

Lasfer, M.A., A. Melnik, and D. Thomas, 2003, Stock Price Reaction in Stressful Circumstances: An International Comparison, Journal of Banking and Finance, 27(10), 1959-1977.

Lehmann, B.N., 1990, Fads, Martingales and Market Efficiency, Quarterly Journal of Economics, 105(1), 1-28.

Lo, A.W., and A.C. MacKinlay, 1990, An Econometric Analysis of Nonsynchronous Trading, Journal of Econometrics, 45(2), 181-212.

Loewenstein, G.F., C.K. Hsee, E.U. Weber, and N. Welch, 2001, Risk as Feelings, Psychological Bulletin, 127(2), 267-286.

Mazouz, K., L.J. Nathan, and J. Joulmer, 2009, Stock Price Reaction Following Large One-Day Price Changes: UK Evidence, Journal of Banking and Finance, 33(8), 1481-1493.

Mehra, R., and R. Sah, 2002, Mood Fluctuations, Projection Bias, and Volatility of Equity Prices, Journal of Economic Dynamics and Control, 26(5), 869-887.

Michaely, R., and K.L. Womack, 1999, Conflict of Interest and the Credibility of Underwriter Analyst Recommendations, Review of Financial Studies, 12(4), 653-686.

Park, J., 1995, A Market Microstructure Explaining for Predictable Variations in Stock Returns following Large Price Changes, Journal of Financial and Quantitative Analysis, 30(2), 241-256.

Pritamani, M., and V. Singal, 2001, Return Predictability Following Large Price Changes and Information Releases, Journal of Banking and Finance, 25(4), 631-656.

Ratner, M., and R. Leal, 1998, Evidence of Short-Term Price Reversals Following Large One Day Movements in the Emerging Markets of Latin America and Asia, Working paper, Rider University, New Jersey, US.

Renshaw, E.F., 1984, Stock Market Panics: A Test of the Efficient Market Hypothesis, Financial Analysts Journal, 40(3), 48-51.

Saunders, E.M., 1993, Stock Prices and Wall Street Weather, American Economic Review, 83(5), 1137-1145.

Savor, P., 2012, Stock Returns after Major Price Shocks: The Impact of Information, Journal of Financial Economics, 106(3), 635-659.

Schwarz, N., 1990, Feelings as Information. Informational and Motivational Functions of Affective States, in R. Sorrentino and ET Higgins, eds.: Handbook of Motivation and Cognition (Guilford Press, New York).

Schwarz, N., 2002, Feelings as Information: Moods Influence Judgments and Processing Strategies, In T. Gilovich, D. Griffin & D. Kahneman (Eds.). Heuristics and Biases: The Psychology of Intuitive Judgment, 534-547, New York: Cambridge University Press.

Schwarz, N., and G.L. Clore, 1983, Mood, Missatribution, and Judgments of Well-Being: Informative and Directive Functions of Affective States, Journal of Personality and Social Psychology, 45(3), 513-523.

Sturm, R.R., 2003, Investor Confidence and Returns Following Large One-Day Price Changes, The Journal of Behavioral Finance, 4(4), 201-216.

Tetlock, P. C., 2010, Does Public Financial News Resolve Asymmetric Information? Review of Financial Studies, 23(9), 3520-3557.

Vega, C., 2006, Stock Price Reaction to Public and Private Information, Journal of Financial Economics, 82(1), 103-133.

Whaley, R.E., 1993, Derivatives on Market Volatility: Hedging Tools Long Overdue, Journal of Derivatives, 1(1), 71-84.

Whaley, R.E., 2000, The Investor Fear Gauge, Journal of Portfolio Management, 26(3), 12-17.

Whaley, R.E., 2008, Understanding VIX, Working Paper, Vanderbilt University.

Wright, W.F., and G.H. Bower, 1992, Mood Effects on Subjective Probability Assessment, Organizational Behavior and Human Decision Processes, 52(2), 276-291.

Yuan, K., L. Zheng, and Q. Zhu, 2006, Are Investors Moonstruck? Lunar Phases and Stock Returns, Journal of Empirical Finance, 13(1), 1-23.

Zarowin, P., 1989, Short-Run Market Overreaction: Size and Seasonality Effects, Journal of Portfolio Management, 15(3), 26-29.

Efficiency of the UK Stock Exchange

Vasilios Sogiakas

Correspondence: Vasilios Sogiakas,

Adam Smith Business School, University of Glasgow, United Kingdom

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This paper investigates the dynamics of the factors of the Fama & French (1993) model using data from the UK financial market. Since financial markets are exposed to exogenous and endogenous structural changes due to the implementation of new regulative guidelines and/or the fluctuation of investors’ behavior or the unanticipated financial crises, my analysis is based on an econometric methodology that accounts for structural breaks and regimes shifts. According to the empirical results of the paper, although the functioning of the conventional risk premiums seems to adequately explain the cross-sectionality of share returns, there exists instability on the parameter set, which is associated with the fundamentals of the UK economy. Finally, the implications of these results shed much light on the contribution of the recent financial crisis into the informational efficiency of the UK financial market. Thus, although the current liquidity crisis is linked with unanticipated imbalances in the economic environment, it might have been a good opportunity for individual and institutional investors to revise their investing strategies, since the excess returns’ risk premia have reached more informative regimes.


  Efficient Market Hypothesis, Three Factor model, Regime Shift, Financial Crises


Alexander, S. (1961) “Price Movements in Speculative Markets: Trends or Random Walks.” Industrial Management Review, Vol.2, pp.7-26.Bachelier, L. (1900) “Theorie de la Speculation.” Annales Scientifiques de l’Ecole Normale Superieure Ser., 17, pp. 21–86.Banz, R. (1981) “The Relationship Between Return and Market Value of Common Stocks.” Journal of Financial Economics, Vol. 9, pp.3-18.Basu, S. (1977) “The Investment Performance of Common Stocks in Relation to their Price to Earnings Ratio: A Test of the Efficient Market Hypothesis.” Journal of Finance, Vol. 32, pp.663-682.Carhart, M. (1997) “On persistence of mutual fund performance.” Journal of Finance 52:57-82.Cowles, A. 3rd (1933) “Can Stock Market Forecasters Forecast?” Econometrica 1:309-324.Cowles, A. 3rd Jones, H. (1937) “Some A Posteriori Probabilities in Stock Market Action.” Econometrica 5:280-294.Dimson, E. (1979) “Risk Measurement When Shares are Subject to Infrequent Trading.” Journal of Financial Economics 7:197-226.Fama, E. F. (1965) “The Behavior of Stock Market Prices.” Journal of Business 38:34-105.

Fama, E. F. (1970) “Efficient capital markets: a review of theory and empirical work.” Journal of Finance 25:383-417.Fama, E. F. (1976) “Foundations of Finance.” Basic Books, New York.Fama, E. F. and French, K. R. (1993) “Common Risk Factors in the Returns on Stocks and Bonds.” Journal of Financial Economics 33:3-56.Guidolin, M. and Timmermann, A. (2008) “Size and Value Anomalies under Regime Shifts.” Journal of Financial Econometrics 1-48.Hamilton, J. D. (1988) “Rational-Expectations Econometric Analysis of Changes in Regime: An Investigation of the Term Structure of Interest Rates.” Journal of Economic Dynamics and Control 12:385-423.Karathanasis, G. Kassimatis, K. and Spyrou S. (2010) “Size and Momentum in European Equity Markets: Empirical findings from varying beta Capital Asset Pricing Model.” Accounting and Finance 50:143-169.Lam, K. S. Li, F. K. So, M. S. (2010) “On the Validity of the Augmented Fama and French’s (1993) model: Evidence from the Hong Kong Stock Market.” Review of Quantitative Finance and Accounting 35:89-111.Lewellen, J. Shanken, J. (2002) “Learning, Asset-Pricing Tests, and Market Efficiency.” Journal of Finance LVII: 1113-1145.Liew, J. Vassalou, M. (2000) “Can Book-to-Market, Size and Momentum be Risk Factors that Predict Economic Growth?” Journal of Financial Economics 57:221-245.Lo, A. W. (2004) “The adaptive markets hypothesis: market efficiency from an evolutionary perspective.” Journal of Portfolio Management 30:15-29.Lo, A. W. (2005) “Reconciling efficient markets with behavioral finance: the adaptive markets hypothesis.” Journal of Investment Consulting 7:21-44.MacDonald, R. Power, D. M. (1993) “Persistence in UK Share Returns: Some Evidence from Disaggregated Data.” Applied Financial Economics 3:27-38.Malin, M. Veeraraghavan, M. (2004) “On the Robustness of Fama and French Multifactor Model: Evidence from France, Germany, and the United Kingdom.” International Journal of Business and Economics 3:155-176.Malkiel, B. G. (2003) “The Efficient Market Hypothesis and its Critics.” Journal of Economic Perspectives 17:59–82.Malkiel, B. G. (2005) “Reflections on the Efficient Market Hypothesis: 30 Years Later.” The Financial Review 40:1-9.Merton, R. C. (1973) “An Intertemporal Capital Asset Pricing Model.” Econometrica 41:867-887.Osborne, M. F. M. (1959) “Brownian Motion in the Stock Market.” Operations Research 7:145-173.Pesaran, M. H. (2010) “Predictability of Asset Returns and the Efficient Market Hypothesis.” Forthcoming in Handbook of Empirical Economics and Finance, edited by Aman Ullah and D.E. Giles, Taylor & Francis.Roberts, H. (1959) “Stock Market Patterns’ and Financial Analysis: Methodological Suggestions.” Journal of Finance 44:1-10.Rubinstein, M. (2001) “Rational Markets: Yes or No? The Affirmative Case.” Financial Analysts Journal 57:15–29.Samuelson, P. (1965) “Proof that Property Anticipated Prices Fluctuate Randomly.” Industrial Management Review 6: 41-49.Schwert, W. (1983) “Size and Stock Returns, and other Empirical Regularities.” Journal of Financial Economics 12:3-12.Self, J. K. and Mathur, I. (2006) “Asymmetric stationarity in national stock market indices: an MTAR analysis.” Journal of Business 79:3153–3174.Timmermann, A. Granger, C. W. J. (2004) “Efficient Market Hypothesis and Forecasting.” International Journal of Forecasting 20: 15-27.

Working, H. (1934) “A Random Difference Series for use in the Analysis of Time Series.” Journal of the American Statistical Association 29:11-24.Working, H. (1960) “Note on the Correlation of First Differences of Averages in a Random Chain.” Econometrica 28: 916-918.

GARCH model and fat tails of the Chinese stock market returns – New evidences

Michael Day, Mark Diamond, Jeff Card, Jake Hurd and Jianping Xu

Correspondence: Michael Day,

Department of Economics, Saint Louis University, USA

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The Chinese stock market is unique in which it is moved more by individual retail investors than institutional investors. Therefore, for economic and political stability it is more important to efficiently manage the risk of the Chinese stock market. We investigate its volatility dynamics through the GARCH model with three types of heavy-tailed distributions, the Student’s t, the NIG and the NRIG distributions. Our results show that estimated parameters for all the three types of distributions are statistical significant and the NIG distribution has the best empirical performance in fitting the Chinese stock market index returns.


  generalized hyperbolic distribution, GARCH model, SHA


Bollerslev, T. (1987) "A conditional heteroskedastic time series model for security prices and rates of return data," Review of Economics and Statistics, vol. 69, pp.542-547.Cont, R. (2001) “Empirical properties of asset returns: stylized facts and statistical issues.” Quantitative Finance, vol. 1, pp. 223-236.

Diebold, F. (1986) "Testing for serial correlation in the presence of ARCH," Proceedings of the Business and Economic Statistics Section of the American Statistical Association, vol. 3, pp.323-328.Guo, Z. (2017) “Empirical Performance of GARCH Models with Heavy-tailed Innovations,” mimeo.Guo, Z. (2017) “GARCH models with the heavy-tailed distributions and the Hong Kong stock market returns,” International Journal of Business and Management, vol. 12.Kang, S., C. Cheong and S. Yoon (2010) “Long memory volatility in Chinese stock markets.” Physica A: Statistical Mechanics and its Applications, vol. 389, no. 7, pp. 1425-1433.Politis, D. (2004) “A heavy-tailed distribution for ARCH residuals with application to volatility prediction.” Annals of Economics and Finance, vol. 23, pp. 34-56.Prause, K. (1999) "The generalized hyperbolic model: estimation, financial derivatives, and risk measures," Ph.D. Dissertation.Su, J. and J. Hung (2011) “Empirical analysis of jump dynamics, heavy-tails and skewness on value-at-risk estimation.” Economic Modelling, vol. 28, no. 3, pp. 1117-1130.Sua, D. and B. Fleisherb (1999) “Why does return volatility differ in Chinese stock markets?” Pacific-Basin Finance Journal, vol. 7, no. 5, pp. 557-586.Tavares, A., J. Curto and G. Tavares (2008) “Modelling heavy tails and asymmetry using ARCH-type models with stable Paretian distributions.” Nonlinear Dynamics, vol. 51, no. 1, pp. 231-243.Xu, W., C. Wu, Y. Dong and W. Xiao (2011) “Modeling Chinese stock returns with stable distribution.” Mathematical and Computer Modelling, vol. 54, no. 1, pp. 610-617.Zakoian, J. (1994) “Threshold heteroskedastic models.” Journal of Economic Dynamics and Control, vol. 18, no. 5, pp. 931-955.Zhang, B. and X. Li (2008) “The asymmetric behaviour of stock returns and volatilities: evidence from Chinese stock market.” Applied Economics Letters, vol. 15, no. 12, pp. 959-962.

Heavy-tailed Distributions and Risk Management of Equity Market Tail Events

Zi-Yi Guo

Correspondence: Zi-Yi Guo,

Corporate Model Risk Management Group, Wells Fargo Bank, N.A

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Traditional econometric modelling typically follows the idea that market returns follow a normal distribution. However, the concept of tail risk indicates that the distribution of returns is not normal, but skewed and has heavy tails. Thus, a heavy-tailed distribution, which accurately estimates the tail risk, would significantly improve quantitative risk management practice. In this paper, we compare four widely used heavy-tailed distributions using the S&P 500 daily returns. Our results indicate that the Skewed t distribution in Hansen (1994) has the superior empirical performance compared with the Student’s t distribution, the normal reciprocal inverse Gaussian distribution and the generalized hyperbolic distribution. We further showed the Skewed t distribution could generate the VaR estimates closest to the nonparametric historical VaR estimates compared with other heavy-tailed distributions.


  Tail risk; Value at Risk; Goodness of fit.


Akaike, H. (1973), “Information theory and an extension of the maximum likelihood principle”, in Petrov, B.N.; Csáki, F., 2nd International Symposium on Information Theory, Tsahkadsor, Armenia, USSR, pp. 267–281.

Barndorff-Nielsen, O.(1977), “Exponentially decreasing distributions for the logarithm of particle size,” Proceedings of the Royal Society, vol. 353, pp. 401-419.

Barndorff-Nielsen, O.(1997),“Normal inverse Gaussian distributions and stochastic volatility modeling,” Scandinavian Journal of Statistics, vol. 24, pp.1-13.

Bauer, C. (2000), “Value at Risk using hyperbolic distributions,” Journal of Economics and Business, vol. 52, pp. 455-467.

Cont, R. (2001), “Empirical properties of asset returns: stylized facts and statistical issues,” Quantitative Finance, vol. 223-236.

Dokov, J., S. Stoyanov and S. Rachev (2008), “Computing VaR and AVaR of skewed-T distribution,” Journal of Applied Functional Analysis, vol. 3, pp. 189-207.

Duffie, D. and J. Pan (1997), “An overview of Value at Risk,” Journal of Derivatives, vol. 4, pp. 7-49.

Fajardo, J., A. Farias and J. Ornelas (2005), “Analyzing the use of generalized hyperbolic distributions to Value at Risk calculations,” Brazilian Journal of Applied Economics, vol. 9, pp. 25-38.

Figueroa-Lopez, J., S. Lancette, K. Lee and Y. Mi (2011), “Estimation of NIG and VG models for high frequency financial data,” in Handbook of Modeling High-Frequency Data in Finance, edited by F. G. Viens, M.C. Mariani and I. Florescu, John Wiley & Sons, Inc., USA.

Guo, Z. (2017), “Empirical performance of GARCH models with heavy-tailed innovations,” Wells Fargo Securities, working paper.

Huber-Carol, C., N. Balakrishnan, M. Nikulin and M. Mesbah (2002), Goodness-of-Fit Tests and Model Validity, Springer.

Hansen, B. (1994), “Autoregressive conditional density estimation,” International Economic Review, vol. 35, pp. 705-730.

Huang, C., C. Knowledge, C. Huang and H. Jahvaid (2014), “Generalized hyperbolic distributions and Value-At-Risk estimation for the South African mining index,” International Business & Economics Research Journal, vol. 13, pp. 319-332.

Mabitsela, L., E. Mare and R. Kufakunesu (2015), “Quantification of VaR: a note on VaR valuation in the South African equity market,” Journal of Risk and Financial Management, vol. 8, pp. 103-126.

Mandelbrot, B. (1963), “New methods in statistical economics,” Journal of Political Economy, vol. 71, pp. 421-440.

Prause, K. (1999), “The generalized hyperbolic model: estimation, financial derivatives, and risk measures,” Ph.D. Dissertation.

Rradley, O. and M. Taqqu (2003), “Financial risk and heavy tails,” Handbook of Heavy Tailed Distributions in Finance, edited by S. Rachev, Elsevier.

Socgnia, V. and D. Wilcox (2014), “A comparison of generalized hyperbolic distribution models for equity returns,” Journal of Applied Mathematics, vol. 2014, pp. 23-38.

Taeger, D. and S. Kuhnt (2014), “Goodness-of-fit tests,” Statistical Hypothesis Testing with SAS and R, Wiley Online Library.

Venter, J. and P. de Jongh (2002), “Risk estimation using the normal inverse Gaussian distribution,” Journal of Risk, vol. 4, pp.1-24.

Wilhelmsson, A. (2009), “Value at Risk with time varying variance, skewness and kurtosis - the NIG-ACD model,” Econometrics Journal, vol. 12, pp. 82-104.

Zhu, D. and J. Galbraith (2012), “A generalized asymmetric Student-t distribution with application to financial econometrics,” Journal of Econometrics, vol. 157, pp. 297- 305.

Determinants of the size of government in high-income countries

Minh Quang Dao

Correspondence: Minh Quang Dao,

Eastern Illinois University, USA

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This paper empirically assesses the determinants of the share of government consumption in the GDP in high-income countries at two points in time, namely the year 2000 and 2014 while taking into consideration the major issue of potential simultaneity bias by introducing interaction variables. Based on data from the World Bank and using a sample of twenty-six high-income economies in 2000, we find that the share of government consumption in the GDP growth is dependent upon the log of population, its square, the log of the labor force, and interaction terms between the square of the log of the labor force and the log of population, between the log of the labor force and its square, and between the log of population and the log of the labor force. For the year 2014 and using a sample of forty-five high-income countries we find that the size of government as measured by the ratio of government consumption in the GDP is dependent upon the log of per capita gross national income, the log of the labor force, the log of population, the log of urbanization (measured as the share of the urban population in the total population), and the interaction terms between the log of per capita gross income and that of urbanization, the log of urbanization and that of the labor force, and between the log of urbanization and that of population. Statistical results of such empirical examination will contribute towards a better understanding of the determinants of the size of government in high-income economies. Data for all variables are from the 2016 World Development Indicators. We specify and estimate a semi log and quadratic model and observe that some coefficient estimates do not have the expected sign due to possible collinearity among some independent variables.


  Government Consumption Expenditure, Per Capita Gross National Income, Urbanization, Labor Force, High-Income Countries.


Abizadeh, S and Gray, J (1985), “Wagner’s Law: A Pooled Time Series, Cross-Section Comparison”, National Tax Journal, Vol. 8, No. 2 (June), pp. 209-18.

Bird, RM (1971), “Wagner’s ‘Law’ of Expanding State Activity,” Public Finance, Vol. 26, No. 1 (January), pp. 1-26.

Borcherding, TE (1977), “The Sources of Growth of Public Expenditures in the United States, 1902-1970,” in Borcherding, TE (ed.) Budgets and Bureaucrats: The Sources of Government Growth, Durham, N.C.: Duke University Press.

Dao, M Q (1994a), “Determinants of the Size of Government,” Journal for Studies in Economics and Econometrics, Vol. 18, No. 2, pp. 1-14.

________ (1994b), “Government Consumption, Economic Growth and Co-integration: An Empirical Test of Wagner’s Law,” Studi Economici. No. 53, 1994/2, pp. 55-65.

________ (2014), “Exports, Imports, Government Consumption and Economic Growth in Upper-Middle Income Countries”, Progress in Development Studies, Vol. 14, Issue 2, pp. 197-204.

Dao, MQ and Esfahani, HS (1999), “Tests of a Competitive Model of the Size and Growth of Government”, Journal of Economic Studies, Vol. 26, No. 3 (June), pp. 209-220.

Graziani, A (1887), Intorno all’aumento progressive delle spese publiche, Modena.

Musgrave, RA and Musgrave, P (1976), Public Finance in Theory and Practice, New York, NY: McGraw-Hill.

Wagner, A (1958), “Three Extracts on Public Finance,” in Musgrave, RA and Peacock, AT, editors, Classics in the Theory of Public Finance.

World Bank (2016), World Development Indicators 2016. World Bank, Washington, DC.

On Origins of Bubbles

Zura Kakushadze

Correspondence: Zura Kakushadze ,

Quantigic Solutions LLC, USA

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We discuss – in what is intended to be a pedagogical fashion – a criterion, which is a lower bound on a certain ratio, for when a stock (or a similar instrument) is not a good investment in the long term, which can happen even if the expected return is positive. The root cause is that prices are positive and have skewed, long-tailed distributions, which coupled with volatility results in a long-run asymmetry. This relates to bubbles in stock prices, which we discuss using a simple binomial tree model, without resorting to the stochastic calculus machinery. We illustrate empirical properties of the aforesaid ratio. Log of market cap and sectors appear to be relevant explanatory variables for this ratio, while price-to-book ratio (or its log) is not. We also discuss a short-term effect of volatility, to wit, the analog of Heisenberg’s uncertainty principle in finance and a simple derivation thereof using a binary tree.


  Bubble, Skewed Distribution, Stock Price, Volatility, Return, Dividends, Buybacks, Binomial Tree, Brownian Motion, Uncertainty Principle, Market Cap, Price-To-Book, Sectors, Time Ordering.


Baaquie, B.E. (2007) Quantum Finance: Path Integrals and Hamiltonians for Options and Interest Rates. Cambridge, UK: Cambridge University Press, pp. 99-101.

Biane, P. (2010) Itˆo’s stochastic calculus and Heisenberg commutation relations. Stochastic Processes and their Applications 120(5) (2010) 698-720.

Black, F. and Scholes, M. (1973) The pricing of options and corporate liabilities. Journal of Political Economy 81(3): 637-659.

Heisenberg, W. (1927) ¨Uber den anschaulichen Inhalt der quantentheoretischen Kinematik und Mechanik. Zeitschrift fu¨r Physik 43(3-4): 172-198.

Kakushadze, Z. (2015a) On Origins of Alpha. The Hedge Fund Journal 108: 47-50. Available online:

Kakushadze, Z. (2015b) Path Integral and Asset Pricing. Quantitative Finance 15(11): 1759-1771. Available online:

Kennard, E.H. (1927) Zur Quantenmechanik einfacher Bewegungstypen. Zeitschrift fu¨r Physik 44(4-5): 326-352.

Miller, M.H. and Modigliani, F. (1961) Dividend Policy, Growth and the Valuation of Shares. Journal of Business 34(4): 411-433.

Powers, M.R. (2010) Uncertainty principles in risk finance. The Journal of Risk Finance 11(3): 245-248.

Protter, P. (2013) A Mathematical Theory of Financial Bubbles. ParisPrinceton Lectures on Mathematical Finance (Springer Lecture Notes in Mathematics) 2081: 1-108.

Sharpe, W.F. (1966) Mutual Fund Performance. Journal of Business 39(1): 119-138.

Sharpe, W.F. (1994) The Sharpe Ratio. The Journal of Portfolio Management 21(1): 49-58.

Soloviev, V. and Saptsin, V. (2011) Heisenberg uncertainty principle and economic analogues of basic physical quantities. Computer Modelling and New Technologies 15(3): 21-26.

Sornette, D. and Cauwels, P. (2014) Financial Bubbles: Mechanisms and Diagnostics. Swiss Finance Institute Research Paper, No. 14-28.

Taleb, N.N. The Black Swan: The Impact of the Highly Improbable. New York, NY: Random House (expanded 2nd edition).

Weyl, H. (1928) Gruppentheorie und Quantenmechanik. Leipzig, Germany: Hirzel.

Wick, G.C. (1954) Properties of Bethe-Salpeter Wave Functions. Phys. Rev. 96(4): 1124-1134.