Abstract
The last half century has witnessed an unprecedented number of financial crises and episodes of great price and output volatility. Given the economic, financial, and trade inter-linkages of the global economy, both the US Subprime crisis and the 2010 Eurozone crisis spilled over into the emerging and developing economies and India was no exception. In this background, this chapter starts off with a whirlwind rundown of the existing literature, giving emphasis to the different generations of financial crisis models and shows how these have characterized the crises across the globe. Theoretically, the contribution of this chapter lies in proposing a macroeconomic model based on the Keynesian line of argument to explore the episodes of crises and the response of macro-variables in such a setup. This is in turn followed by the construction of a crisis index (CI), at monthly frequency which functions as the binary response variable in a probit model in conglomeration with some other macroeconomic variables which have played a role during the periods of crisis. Formulating the probit model empirically brings out the magnitude to which each macroeconomic variable have contributed to the probability of a crisis happening. The novelty of this chapter lies in the theoretical and the empirical portrayal of the crisis periods in conjunction with bringing out the most significant policy variables responsible in this regard.
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Notes
- 1.
The condition that depreciation will improve the position of trade balance of a country is known as the Bickerdike–Robinson–Metzler condition or, in particular, the Marshall–Lerner condition (for details see Rose and Yellen 1989).
- 2.
Newton’s forward and backward interpolation formula is used to interpolate the missing values and they look like,
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Newton’s forward interpolation formula:
\( {U}_{a+ rh}={U}_a+{r}_{C_1}\Delta {U}_a+{r}_{C_2}{\Delta}^2{U}_a+\dots {r}_{C_n}{\Delta}^n{U}_a;\kern1em r=\frac{x-{x}_0}{h} \)
where, h is the difference between two consecutive step values and x 0 is the initial value.
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Newton’s backward interpolation formula:
\( {U}_{n+ vh}={U}_n+v\Delta {U}_{n-1}+\frac{v\left(v+1\right)}{1\times 2}{\Delta}^2{U}_{n-2}+\dots \frac{v\left(v+1\right)\dots \left(v+n-1\right)}{1\times 2\times \dots \times n}{\Delta}^n{U}_a;\kern1em v=\frac{x-{x}_n}{h} \)
where, h is the difference between consecutive step two values and x n is the terminal value.
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References
Amadou, D. I. (2010). SKPROBIT: Stata module to perform Lagrange Multiplier Test for Normality for Probit model. Statistical Software Components.
Athukorala, P. C., & Kohpaiboon, A. (2009). East Asian exports in the global economic crisis: The decoupling fallacy and post-crisis policy challenges. The Australian National University, Arndt-Corden Department of Economics.
Barro, R. J., & Gordon, D. B. (1983). Rules, discretion and reputation in a model of monetary policy. Journal of Monetary Economics, 12(1), 101–121.
Breuer, J. B. (2004). An exegesis on currency and banking crises. Journal of Economic Surveys, 18(3), 293–320.
Connolly, M. B., & Taylor, D. (1984). The exact timing of the collapse of an exchange rate regime and its impact on the relative price of traded goods. Journal of Money, Credit and Banking, 16(2), 194–207.
Das, D. (2010). Financial globalization: Growth, integration, innovation and crisis. Springer.
Dullien, S., Kotte, D. J., Márquez, A., & Priewe, J. (2010). The financial and economic crisis of 2008-2009 and developing countries. UNCTAD, United Nations.
Edwards, S., & Susmel, R. (1999). Contagion and volatility in the 1990s (No. 153). Universidad del CEMA.
Eichengreen, R., & Rose, A. (1995). Wyplosz 1995. Speculative attacks on pegged exchange rates: An empirical exploration with special reference to the European monetary system. Federal Reserve Bank of San Francisco.
F&P (FitchRatings). (2011). Press note (May 24), New Delhi/Singapore. Accessed at https://www.fitchratings.com/site/pr/712961
Flood, R. P., & Garber, P. M. (1984). Collapsing exchange-rate regimes: Some linear examples. Journal of International Economics, 17(1–2), 1–13.
Frankel, J. A., & Rose, A. K. (1996). Currency crashes in emerging markets: An empirical treatment. Journal of International Economics, 41(3), 351–366.
Frankel, J., & Wei, S. (2005). Managing macroeconomic crises: Policy lessons. In J. Aizenman & B. Pinto (Eds.), Managing economic volatility and crises (pp. 315–405). Cambridge: Cambridge University Press.
GoI (Government of India). (2010). India’s external debt: A status report 2009-10. Press Information Bureau, Ministry of Finance, Government of India, India.
GoI (Government of India). (2012). India’s external debt: A status report 2011-12. Press Information Bureau, Ministry of Finance, Government of India, India.
GoI (Government of India). (2016). India’s external debt: A status report 2015-16. Press Information Bureau, Ministry of Finance, Government of India, India.
Helpman, E., Razin, A., & Sadka, E. (Eds.). (1988). Economic effects of the government budget. Cambridge, MA: MIT Press.
Kaminsky, G. L., & Reinhart, C. M. (1999). The twin crises: The causes of banking and balance-of-payments problems. American Economic Review, 89, 473–500.
Kaminsky, G. L., & Reinhart, C. M. (2000). On crises, contagion, and confusion. Journal of International Economics, 51(1), 145–168.
Karmakar, A. K. (2014). Contagious financial crises in the recent past and their implications for India. In Analytical issues in trade, development and finance (pp. 499–533). Springer India.
Karmakar, A. K., & Mukherjee, S. (2017). Mortgaging the future? Contagious financial crises in the recent past and their implications for BRICS economies. In Global financial crisis and its ramifications on capital markets (pp. 175–190). Springer International Publishing.
Kouri, P. J. (1976). The exchange rate and the balance of payments in the short run and in the long run: A monetary approach. The Scandinavian Journal of Economics, 78, 280–304.
Krugman, P. (1979). A model of balance-of-payments crises. Journal of Money, Credit and Banking, 11(3), 311–325.
Krugman, P. (2001, March). Crises: The next generation. In Razin conference, Tel Aviv University (pp. 25–26).
Lahiri, H., Ghosh, A., & Ghosh, C. (2015). India’s recent macroeconomic performance and foreign capital inflows. Contemporary Issues and Ideas in Social Sciences, 7, 1–53.
Lange, O. (1942). Say’s law: A restatement and criticism. In Lange, O., F. McIntyre, T.O. Matema (Eds) Studies in mathematical economics and econometrics (pp. 49–68). Chicago, IL: University of Chicago Press.
Liu, S., & Lindholm, C. K. (2006). Assessing early warning signals of currency crises: A fuzzy clustering approach. Intelligent Systems in Accounting, Finance and Management, 14(4), 179–202.
Maddala, G. S. (1986). Disequilibrium, self-selection, and switching models. Handbook of Econometrics, 3, 1633–1688.
Mundell, R. A. (1961). A theory of optimum currency areas. The American Economic Review, 51(4), 657–665.
Nicoletti, C., & Peracchi, F. (2001, July). Two-step estimation of binary response models with sample selection. In first British Household Panel Survey research conference held in Colchester.
Obstfeld, M. (1996). Models of currency crises with self-fulfilling features. European Economic Review, 40(3), 1037–1047.
RBI (Reserve Bank of India). (2012a). Handbook of statistics on the Indian economy. RBI database.
RBI (Reserve Bank of India). (2012b). The economy: Review and prospects. The annual report on the working of the Reserve Bank of India (pp. 16–74).
RBI (Reserve Bank of India). (2013). Handbook of statistics on the Indian economy. RBI database.
RBI (Reserve Bank of India). (2014). Handbook of statistics on the Indian economy. RBI Database.
RBI (Reserve Bank of India). (2015). Handbook of statistics on the Indian economy. RBI database.
Rose, A. K., & Yellen, J. L. (1989). Is there a J-curve? Journal of Monetary Economics, 24(1), 53–68.
Sachs, J. D. (1987). The Bolivian hyperinflation and stabilization. American Economic Review, 77(2), 279–283.
Tularam, G. A., & Subramanian, B. (2013). Modeling of financial crises: A critical analysis of models leading to the global financial crisis. Global Journal of Business Research, 7(3), 101–124.
Veall, M. R., & Zimmermann, K. F. (1992). Pseudo-R 2’s in the ordinal probit model. Journal of Mathematical Sociology, 16(4), 333–342.
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Appendices
Keywords and Definitions
US Subprime Crisis
In simple language, the mortgage meltdown in point of fact started with the bursting of the US housing bubble which began in 2001 and reached its peak in 2005 and eventually culminated into a crisis in the autumn of 2008. Following the Federal Reserve’s low interest rates, credit availability was made easy and most of the borrowers got credits without correctly examining their ability to pay. A sense of optimism about housing values led to a boom in the housing market and new houses started coming up. But, with supply more than demand, housing prices fell unexpectedly and borrowers who were planning to resale their houses at a higher price and settle the mortgage started defaulting. Global investors having mortgage-backed securities (including many of the banks) started incurring losses and eventually by the 4th quarter of 2008, the subprime mortgage market crisis had struck USA with repercussion effects being felt across the world.
Eurozone Crisis
The European debt crisis (often also referred to as the Eurozone crisis or the European sovereign debt crisis) is a multiyear phenomenon that started in the Eurozone since the end of 2009 and continued till the end of 2016. Quite a few Eurozone member countries like Greece, Portugal, Ireland, Spain, and Cyprus were unable to assist over-indebted banks under their national control without the help from third party financial institutions like other Eurozone countries, the European Central Bank (ECB), or the International Monetary Fund (IMF). The aftermath of the crisis had adverse impacts with towering unemployment rates in Greece and Spain reaching around 27% coupled with economic growth plummeting. Also, one of the political fallouts was a change of authority in 10 out of the 19 Eurozone countries, including Greece, Ireland, France, Italy, Portugal, Spain, Slovenia, Slovakia, Belgium, and the Netherlands, as well as outside of the Eurozone, in the UK.
Macroeconomic Modeling
A macroeconomic model is an analytical design to illustrate the operations of the different sectors of an economy and how such operations tend to influence the overall macroeconomic situation. This chapter makes use of a standard Keynesian open economy model with imperfect capital mobility at its core to understand how the behavior of certain macro-variables like growth, exchange rate movements, inflation, etc. affects the economy.
Probit Model
In econometrics, a probit model represents a type of a regression model where the dependent variable is an ordinal or binary response variable, i.e., it can take only two values, for example, say, a crisis period or a noncrisis period. The objective is to classify observations into two specific categories and thereby assess the predicted probabilities of the occurrence of a particular situation. A response variable, Y, is a binary variable, i.e., it can have only two probable outcomes which will be denoted as 1 or 0 otherwise. There exist a vector of regressors X, which are assumed to influence the outcome Y. Specifically, the model is of the form:
Trade Orientation Ratio
Trade orientation ratio, TOR, is a measure of the extent of the trade openness of a country. It is defined as
Budget Deficit
The term budget deficit represents a situation when the federal government’s expenditure goes beyond its revenue. A budget deficit is a pointer of the financial health of an economy. There are three types of budget deficit:
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1.
Revenue deficit = Total revenue expenditure − Total revenue receipts.
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2.
Fiscal deficit = Total expenditure − Total receipts excluding borrowings.
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3.
Primary deficit = Fiscal deficit − Interest payments.
External Debt
External debt (or foreign debt) can be termed as the total value of debt a country owes to foreign creditors, accompanied by internal debt owed to domestic lenders. The debt may include money owed to private commercial banks, central banks of other nations, governments of other nations, or the international financial institutions such as International Monetary Fund (IMF), World Bank, Asian Development Bank (ADB), etc.
Appendix A.1
The standard adjustment mechanisms are applicable and by using Taylor’s rule, Eqs. (2.2) and (2.6) have been linearized around the equilibrium values.
The Impact of a Fall in θ
Differentiating Eqs. (2.2), (2.3), and (2.6) with respect to θ and keeping other exogenous variables constant, given the adjustment conditions, the result is
Writing in matrix notation,
Using Cramer’s rule, the values of \( \frac{\partial Y}{\partial \theta } \) and \( \frac{\partial e}{\partial \theta } \) are
Since the objective is to look at the partials of Y and e with respect to θ, the authors have not calculated the value of the partial of D with respect to θ. Coming to the sign of the partials, if the perceptions of the foreigners improve regarding investment in India, capital inflows will come into the economy and the balance of payments position would improve so B θ > 0. An increase in income, i.e., a rise in Y will induce imports to rise so the balance of payments position deteriorates ⇒ B Y < 0. PL Y is the monetary value of the change in money demand as income changes, which is positive given the specifications of the money demand function. Moving onto the sign of E e , there is a doubt regarding the sign. To explain this, the authors have made an assumption that investment in India is more sensitive to exchange rate as compared to income based on Lahiri et al. (2015). An increase in the value of e raises prices of foreign goods in terms of the domestic currency. Since production and investment in India are heavily import dependent, an increase in e value not only leads to a depression of investor sentiments but also due to very high import intensities, imports fall and as a result NX improves. This kind of also justifies why B e > 0. But what about E e ? As argued by Lahiri et al. (2015), the negative impact on investor sentiments outweighs the positive effect of improvement in NX and thus E e < 0. Coming on to the sign of E Y , where E Y is the expenditure propensity. As Y rises, imports will rise along with a rise in C. So, on the one hand, there is a positive effect (C rises) and on the other there is a negative effect of a rise in imports. Here, also, it has been assumed that net effect of a rise in imports dominate the rise in C, given India’s heavy dependence on imports. So, the value of E Y < 0. Consequently, the results follow. As the exchange rate depreciates, inflation will rise given the specifications of the model and the stability condition:
An increase in the value of Y* has a positive effect both on the balance of payments positions as well as the expenditure function through an improvement in the value of net exports. One more result needs to be highlighted for use in Appendix A.2. Given the construction and specifications in Eqs. (2.2) and (2.6),
The magnitude of these ratios will be crucial in determining whether exchange rate rises or falls. So, the fluctuations in Fig. 2.2 during 2011–2012 and 2012–2013 clearly got reflected.
Appendix A.2
The Impact of a Fall in Y*
Differentiating Eqs. (2.2), (2.3), and (2.6) with respect to Y* and keeping other exogenous variables constant, and then similarly using Cramer’s rule, the result is
Writing in matrix notation,
Using Cramer’s rule, the values of \( \frac{\partial Y}{\partial {Y}^{\ast }} \) and \( \frac{\partial e}{\partial {Y}^{\ast }} \) can be derived as
Given the intuitive arguments mentioned earlier, the results are hence proved.
Appendix B.1
The Impact of a Rise in φ
Following the same line of attack and carrying out the differentiation with respect to φ, the result in the matrix form comes out to be
Using Cramer’s rule, the values of \( \frac{\partial Y}{\partial \varphi } \) and \( \frac{\partial e}{\partial \varphi } \) are
It should be noted that the sign of B φ is negative. The more the government places restrictions on FDI and FPI, the lesser will be the capital inflow. There are sectoral ceilings or caps on FDI, and this cap varies across sectors. For details, one can go through RBI (2013). The caps are on the amount of Foreign Institutional Investment (FIIs) in any Indian company. In addition to this, the “General Anti Avoidance Rule (GAAR)” and the “retrospective amendment to the income tax law” kind of hampered the prospects, so growth rate deteriorated further.
Appendix C.1
The Impact of a Rise in \( \overline{t} \)
Following the same line of attack and carrying out the differentiation with respect to \( \overline{t} \), the result in the matrix form comes out to be
Using Cramer’s rule, \( \frac{\partial Y}{\partial \overline{t}} \) and \( \frac{\partial e}{\partial \overline{t}} \) turns out to be
Rise in the indirect tax rates restrains production a bit, so the sign of \( {E}_{\overline{t}} \) < 0.
The signs of the other partials have already been explained in Appendix A.1. Hence, the results follow.
The sign of \( \frac{\partial e}{\partial \overline{t}} \) is ambiguous depending on the strength of the horizontal shifts, i.e., \( \frac{E_Y}{E_{\overline{t}}} \) and \( \frac{B_Y}{B_{\overline{t}}} \) in Eqs. (2.2) and (2.6) as a result of a unit increase in \( \overline{t} \). So this actually explains the fluctuations that took place at that point of time.
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Mukherjee, S., Karmakar, A.K. (2018). A Hardheaded Look: How Did India Feel the Tremors of Recent Financial Crises?. In: Dincer, H., Hacioglu, Ü., Yüksel, S. (eds) Global Approaches in Financial Economics, Banking, and Finance. Contributions to Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-78494-6_2
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