Effect of Central Bank Intervention in Estimating Exchange Rate Exposure: Evidence from an Emerging Market:
TL;DR: In this paper, the authors examined the relationship between the value of the firm and unanticipated changes in exchange rate and found that the intervention by central bank has a major impact on the level of Indian firms' exchange rate exposure.
Abstract: This study examines the relationship between the value of the firm and unanticipated changes in exchange rate. Using a sample of 651 Indian firms over the period from 2001 to 2013, this study finds that unanticipated changes in exchange rates are more appropriate than actual changes to discover statistically significant and economically important exchange rate exposure. Using a vector error correction model (VECM) to generate unanticipated exchange rate changes, this study provides new evidence that the intervention by central bank has a major impact on the level of Indian firms’ exchange rate exposure.
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TL;DR: In this article, a simple method of calculating a heteroskedasticity and autocorrelation consistent covariance matrix that is positive semi-definite by construction is described.
Abstract: This paper describes a simple method of calculating a heteroskedasticity and autocorrelation consistent covariance matrix that is positive semi-definite by construction. It also establishes consistency of the estimated covariance matrix under fairly general conditions.
18,117 citations
"Effect of Central Bank Intervention..." refers methods in this paper
...Autocorrelation and heteroskedasticity are eliminated by correcting the OLS standard errors using Newey and West (1987) method....
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Book•
01 Jan 2009
8,216 citations
"Effect of Central Bank Intervention..." refers background in this paper
...equations have the same regressors, single equation-by-equation OLSs is the efficient estimator (Greene, 2002)....
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...When all equations have the same regressors, single equation-by-equation OLSs is the efficient estimator (Greene, 2002)....
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TL;DR: In this article, the authors examine the different methods used in the literature and explain when the different approaches yield the same (and correct) standard errors and when they diverge, and give researchers guidance for their use.
Abstract: In both corporate finance and asset pricing empirical work, researchers are often confronted with panel data. In these data sets, the residuals may be correlated across firms and across time, and OLS standard errors can be biased. Historically, the two literatures have used different solutions to this problem. Corporate finance has relied on clustered standard errors, while asset pricing has used the Fama-MacBeth procedure to estimate standard errors. This paper examines the different methods used in the literature and explains when the different methods yield the same (and correct) standard errors and when they diverge. The intent is to provide intuition as to why the different approaches sometimes give different answers and give researchers guidance for their use.
7,647 citations
Book•
21 Nov 1994
TL;DR: In this article, the authors present an alternative solution method for Deterministic Processes by iteratively solving homogeneous difference equation and finding particular solutions for deterministic processes, and conclude that the proposed solution is the best solution.
Abstract: PREFACE. ABOUT THE AUTHOR. Chapter DIFFERENCE EQUATIONS . 1 Time-Series Models. 2 Difference Equations and Their Solutions. 3 Solution by Iteration. 4 An Alternative Solution Methodology. 5 The Cobweb Model. 6 Solving Homogeneous Difference Equations. 7 Finding Particular Solutions for Deterministic Processes. 8 The Method of Undetermined Coefficients. 9 Lag Operators. Summary and Conclusions. Questions and Exercises. Endnotes. Appendix 1 Imaginary Roots and de Moivre's Theorem. Appendix 2 Characteristic Roots in Higher-Order Equations. Chapter 2 STATIONARY TIME-SERIES MODELS . 1 Stochastic Difference Equation Models. 2 ARMA Models. 3 Stationarity. 4 Stationarity Restrictions for an ARMA(p, q) Model. 5 The Autocorrelation Function. 6 The Partial Autocorrelation Function. 7 Sample Autocorrelations of Stationary Series. 8 Box-Jenkins Model Selection. 9 Properties of Forecasts. 10 A Model of the Interest Rate Spread. 11 Seasonality. 12 Parameter Instability and Structural Change. Summary and Conclusions. Questions and Exercises. Endnotes. Appendix 1 Estimation of an MA(1) Process. Appendix 2 Model Selection Criteria. Chapter 3 MODELING VOLATILITY . 1 Economic Time Series The Stylized Facts. 2 ARCH Processes. 3 ARCH and GARCH Estimates of Inflation. 4 Two Examples of GARCH Models. 5 A GARCH Model of Risk. 6 The ARCH-M Model. 7 Additional Properties of GARCH Processes. 8 Maximum Likelihood Estimation of GARCH Models. 9 Other Models of Conditional Variance. 10 Estimating the NYSE International 100 Index. 11 Multivariate GARCH. Summary and Conclusions. Questions and Exercises. Endnotes. Appendix 1 Multivariate GARCH Models. Chapter 4 MODELS WITH TREND . 1 Deterministic and Stochastic Trends. 2 Removing the Trend. 3 Unit Roots and Regression Residuals. 4 The Monte Carlo Method. 5 Dickey-Fuller Tests. 6 Examples of the ADF Test. 7 Extensions of the Dickey-Fuller Test. 8 Structural Change. 9 Power and the Deterministic Regressors. 10 Tests with More Power. 11 Panel Unit Root Tests. 12 Trends and Univariate Decompositions. Summary and Conclusions. Questions and Exercises. Endnotes. Appendix 1 The Bootstrap. Chapter 5 MULTIEQUATION TIME-SERIES MODELS . 1 Intervention Analysis. 2 Transfer Function Models. 3 Estimating a Transfer Function. 4 Limits to Structural Multivariate Estimation. 5 Introduction to VAR Analysis. 6 Estimation and Identification. 7 The Impulse Response Function. 8 Testing Hypothesis. 9 Example of a Simple VAR Terrorism and Tourism in Spain. 10 Structural VARs. 11 Examples of Structural Decompositions. 12 The Blanchard and Quah Decomposition. 13 Decomposing Real and Nominal Exchange Rate Movements An Example. Summary and Conclusions. Questions and Exercises. Endnotes. Chapter 6 COINTEGRATION AND ERROR-CORRECTION MODELS . 1 Linear Combinations of Integrated Variables. 2 Cointegration and Common Trends. 3 Cointegration and Error Correction. 4 Testing for Cointegration The Engle-Granger Methodology. 5 Illustrating the Engle-Granger Methodology. 6 Cointegration and Purchasing-Power Parity. 7 Characteristic Roots, Rank, and Cointegration. 8 Hypothesis Testing. 9 Illustrating the Johansen Methodology. 10 Error-Correction and ADL Tests. 11 Comparing the Three Methods. Summary and Conclusions. Questions and Exercises. Endnotes. Appendix 1 Characteristic Roots Stability and Rank. Appendix 2 Inference on a Cointegrating Vector. Chapter 7 NONLINEAR TIME-SERIES MODELS . 1 Linear Versus Nonlinear Adjustment. 2 Simple Extensions of the ARMA Model. 3 Regime Switching Models. 4 Testing For Nonlinearity. 5 Estimates of Regime Switching Models. 6 Generalized Impulse Responses and Forecasting. 7 Unit Roots and Nonlinearity. Summary and Conclusions. Questions and Exercises. Endnotes. STATISTICAL TABLES. A. Empirical Cumulative Distributions of the tau. B. Empirical Distribution of PHI . C. Critical Values for the Engle-Granger Cointegration Test. D. Residual Based Cointegration Test with I (1) and I (2) Variables. E. Empirical Distributions of the lambda max and lambda trace Statistics. F. Critical Values for beta 1 = 0 in the Error-correction Model. G. Critical Values for Threshold Unit Roots. REFERENCES. SUBJECT INDEX.
6,373 citations
"Effect of Central Bank Intervention..." refers background in this paper
...For monthly data, the lag length of 12 should be appropriate (Enders, 2009)....
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TL;DR: In this article, the authors examined the exposure of U.S multinationals to foreign currency risk and found that the relationship between stock returns and exchange rates differs systematically across multinationals.
Abstract: This article examines the exposure of U.S multinationals to foreign currency risk. Evidence is presented that the relationship between stock returns and exchange rates differs systematically across multinationals. Given these results, the study focuses on the determinants of exchange-rate exposure. The comovements between stock returns and the value of the dollar is found to be positively related to the percentage of foreign operations of U.S. multinationals. Copyright 1990 by the University of Chicago.
1,143 citations
"Effect of Central Bank Intervention..." refers background or methods or result in this paper
...The standard market model of Jorion (1990) used in this study to estimate the exposure does not include firm-specific controls which might be a possible alternative for future research....
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...Examining exposure to the trade-weighted exchange rate index is a standard practice followed in literature (Bodnar & Gentry, 1993; Choi & Prasad, 1995; Dominguez & Tesar, 2006; Jorion, 1990)....
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...While some studies report strong evidence of exposure (Bacha, Mohamad, Raihan, & Mohd, 2013; Kiymaz, 2003; Parsley & Popper, 2006; Tsai, Chiang, Tsai, & Liou, 2014; Ye, Hutson, & Muckley, 2014), a large number of studies reveal that only a small number of firms are significantly affected by exchange rate changes (Chue & Cook, 2008; Jorion, 1990; Lin, 2011; Muller & Verschoor, 2007)....
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...In spite of this definition, most previous literature has used actual or realised changes in exchange rates as a proxy for unanticipated currency movements in estimating the exposure (Dominguez & Tesar, 2006; He & Ng, 1998; Hutson & Laing, 2014; Hutson & Stevenson, 2010; Jorion, 1990)....
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...Most studies are focused on US multinational firms and report a small number of firms with significant exposure (Amihud, 1994; Bartov & Bodnar, 1994; Chaieb & Mazzotta, 2013; Chang, Hsin, & Shiah-Hou, 2013; Hutson & Laing, 2014; Jorion, 1990)....
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