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Leeds University Business School Empirical Investigation of Monetary Models for GBP/USD Exchange Rate in Cointegrating VAR with Exogenous I(1) Variables.

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Presentation on theme: "Leeds University Business School Empirical Investigation of Monetary Models for GBP/USD Exchange Rate in Cointegrating VAR with Exogenous I(1) Variables."— Presentation transcript:

1 Leeds University Business School Empirical Investigation of Monetary Models for GBP/USD Exchange Rate in Cointegrating VAR with Exogenous I(1) Variables and Structural Break Viet Hoang Nguyen Leeds University Business School The University of Leeds

2 Monetary Models of Exchange Rate Determination Motivations for Our Research Econometric Framework The Examined Models & Main Considerations Data Empirical Results Conclusions Introduction

3 Development in the Literature of Exchange Rate Modelling Testing Foreign Exchange Market Efficiency Monetary Models of Exchange Rate Determination Models of Exchange Rate Volatility Nonlinear Models of Exchange Rate Foreign Exchange Market Microstructure (New Micro Exchange Rate Economics) Monetary Models of Exchange Rate

4 Monetary models of exchange rate are often called structural models since they are derived from a system of equations representing equilibrium relationships in monetary markets. They emerged as the dominant exchange rate models since the breakdown of the Bretton Woods agreement in the early 1970s. These models focus on the relationship between exchange rate and (macro) economic fundamental variables, and examine the explanatory power of economic fundamentals in forecasting exchange rate. Monetary Models of Exchange Rate

5 Popular models: Meese and Rogoff (1983a) examined three representative models: –The flexible-price (Frenkel-Bilson) model –The sticky-price (Dornbusch-Frankel) model –The sticky-price (Hooper-Morton) model (which also takes into account the current account) Mark (1995) initiated: –The long-horizon regression Monetary Models of Exchange Rate

6 1. The Flexible-price (Frenkel-Bilson) Model (Model 1): Based on Purchasing Power Parity (PPP) Monetary equilibria in domestic and foreign economies: The derived domestic and foreign price levels: The model is based on the assumption of continuous PPP: Monetary Models of Exchange Rate Derivation

7 Monetary Models of Exchange Rate Derivation )()( ***** ttttttt rryymme Thus, In empirical studies, researchers often imposed two following restrictions: And estimated the following model: Notations: e t : Exchange rate; p t : Price level; m t : Money supply; y t : Output; r t : Interest rate; t : Inflation; TB t : Cumulative trade balances; (*): Foreign variables

8 2. The Sticky-price (Dornbusch-Frankel) Model (Model 2): Allow for deviations from PPP by adding long-run inflation differential 3. The Sticky-price (Hooper-Morton) Model (Model 3): Allow for long-run changes in Real Exchange Rate by adding cumulative trade balances of domestic and foreign economies Monetary Models of Exchange Rate Derivation

9 4. The Long-horizon Regression (Model 4): Based on the flexible-price model and further assumes that the UIP (Uncovered Interest rate Parity) holds: where are deviations from the fundamental value and the fundamental value, respectively. k = 1, 2 … represents the forecast horizons. * Employed econometric frameworks: Linear regression, Unrestricted VAR, Panel Data Monetary Models of Exchange Rate Derivation

10 Mixed results: Early gloomy results: Meese and Rogoff (1983a,b), Smith and Wickens (1986), Meese and Rose (1991) suggested the failure of monetary models of exchange rate to beat the random walk in forecasting exchange rate. Suggested causes: –(1) Instability due to oil price shocks & changes in macroeconomic policy regime; (2) Misspecification of money demand function; (3) Difficulties in modelling expectations of explanatory variables - Meese & Rogoff (1983a). –The breakdown of PPP - Smith and Wickens (1986) Monetary Models of Exchange Rate Mixed Results

11 Mixed results: Recent encouraging results: Groen (2000, 2005), Mark and Sul (2001), Rapach and Wohar (2002), Engle et. al. (2007). –Improving samples: Groen (2000, 2005), Mark and Sul (2001) used pooled time series in panel data, Rapach and Wohar (2002) used long-span data. –Examining the cointegration analysis among variables. –Results suggest that there exists a long-run relationship between exchange rate and monetary fundamentals, and that monetary fundamentals do have predictive power for exchange rate. –However, researchers still imposed arbitrary restrictions. Monetary Models of Exchange Rate Mixed Results

12 Is there a long-run relationship between exchange rate and macroeconomic fundamental variables without imposing arbitrary restrictions? How does this long-run relationship response to shocks? How do variables within the system response to shocks? How do macroeconomic fundamentals help to forecast exchange rate in in- and out-of-sample forecast exercises? Motivations

13 Cointegrating Vector Auto-Regression (VAR) with Exogenous I(1) Variables and Structural Break Initiated by Pesaran et al (2000), Pesaran and Shin (2002) Include both endogenous and exogenous variables Include unrestricted intercept and restricted trend Allow for cointegration between endogenous and exogenous I(1) variables Allow for structural break (change in macroeconomic policy regime) Econometric Framework

14 Four models under examination: The Flexible-price (Frenkel-Bilson) Model (1) The Sticky-price (Dornbusch-Frankel) Model (2) The Sticky-price (Hooper-Morton) Model (3) The Long-run/Long-horizon Regression (4) Examined Models

15 Variables in the systemModel 1Model 2Model 3Model 4 1. Oil price (po) 2. Foreign Interest Rate (r * ) 3. Foreign Output (y * ) 4. Foreign Inflation ( * ) 5. Domestic Interest Rate (r) 6. Exchange Rate (e) 7. Domestic Output (y) 8. Relative Money Supply (m - m * ) 9. Domestic Inflation ( ) 10. Relative CA Balances (ca – ca * )

16 Break Dummy for Black-Wednesday Event (1992) We include a trend-shift dummy to account for this event. Mervyn King (1997) – In October 1992, following sterlings departure from the Exchange Rate Mechanism, Britain adopted a new framework for monetary policy. One of the two main components is an explicit target for inflation. Soderlind (2000), Svensson (1994). Main Considerations Long-run relationship between exchange rate and macro-fundamentals Impulse response analysis with respect to shocks of interest In-sample forecast: Directional change forecast Out-of-sample forecast: Central forecast & Event probability forecast Main Considerations

17 Theory-suggested Long-run Relationships (Cointegrating Vectors - CV) in the system: Exchange Rate Equation (ExR) - Relationship between exchange rate and macroeconomic/monetary fundamental variables. Output Gap (OG) - Relationship between domestic and foreign outputs. Interest Rate Parity (UIP) - Relationship between domestic and foreign interest rates. Real Interest Rate (Fisher Equation) - Relationship between domestic nominal interest rate and inflation. Main Considerations

18 Data Variables in the systemExplanationSource 1. Oil price (po)USD/BarrelIFS 2. Foreign Interest Rate (r * )US 3-Month Treasury Bill RateIFS 3. Foreign Output (y * )US real GDPIFS 4. Foreign Inflation ( * ) US CPI InflationIFS 5. Domestic Interest Rate (r)UK 3-Month Treasury Bill RateIFS 6. Exchange Rate (e)GBP/USDOECD 7. Domestic Output (y)UK real GDPIFS 8. Relative Money Supply (m - m * )UKs M4 – USs M3OECD 9. Domestic Inflation ( ) UK CPI InflationIFS 10. Relative CA Balances (ca – ca * )UKs CA – USs CAOECD Examined period: 1980Q1 – 2006Q4

19 Data

20 Empirical Results Model 1Model 2Model 3Model 4 Cointegration Test Number of Cointegrating Relations2331 Suggested Cointegrating Vectors Exchange Rate Equation Interest Rate Parity Fisher Equation Output Gap Restrictions to Identify CVs Number of Restrictions

21 Empirical Results Variables in the modelExRUIPFisher 1. Oil price (po) Foreign Interest Rate (r * )*0 3. Foreign Output (y * )*00 4. Foreign Inflation ( * ) *00 5. Domestic Interest Rate (r)*11 6. Exchange Rate (e) Domestic Output (y)*00 8. Relative Money Supply (m - m * )00 9. Domestic Inflation ( ) *0 10. Relative CA Balances (ca – ca * )*00 Example: Restrictions on Cointegrating Vectors in Model 3

22 Conditional Vector-Error Correction Model w t : vector of endogenous variables. x t : vector of exogenous variables. z t = (x t, w t ) : vector of both exogenous and endogenous variables. b t : break dummy (trend shift). d t : break dummy (intercept shift). Most equations in four models are well-specified with relatively high R 2, especially exchange rate equation: Model 1: R 2 = 0.31 Model 2: R 2 = 0.63 Model 3: R 2 = 0.61 Model 4: R 2 = 0.40 Empirical Results Vector Error Correction Model

23 Empirical Results Persistence Profiles of CVs Persistence Profiles of Cointegrating Vectors in Exactly-identified Case Model 1 Model 2 Model 3 Model 4

24 Empirical Results Persistence Profiles of CVs Persistence Profiles of Cointegrating Vectors in Over-identified Case Model 1 Model 2 Model 3 Model 4

25 Empirical Results Impulse Response Analysis Impulse Responses w.r.t Oil Price Shock – Model 3 (Benchmark) Impulse Responses w.r.t. Domestic Monetary Policy Shock – Model 3

26 Forecast of Directional Changes: Estimate models from 1980Q Q4, leave 8 observations from 2005Q1 to 2006Q4 for in-sample forecast evaluation. Four Events: UD DD DU UU –U: up; D: down. –1 st letter denotes forecast direction; 2 nd letter denotes actual change. –Based on the results of the four events for all variables in the system, we compute three statistics: Hit ratio, Kuipers Score, and Pesaran-Timmermann statistic Empirical Results In-sample Forecast Evaluation

27 Hit ratio = (DD+UU)/(UD+DD+DU+UU), ratio of correctly-predicted events over the total events. Kuipers Score = H-F, where H = UU/(UU+UD) is the proportion of ups that were correctly predicted to occur, and F = DU/(DU+DD) is the proportion of downs that were incorrectly predicted. In the case where the outcome is symmetric, in the sense that we value the ability to forecast ups and downs equally, then the score statistic of zero means no accuracy, whilst high positive and negative values indicate high and low predictive power, respectively. Pesaran-Timmermann Statistic is defined as: PT=(P^ - P*)/(V(P^) - V(P*))^(1/2), where P^ is the proportions of correctly predicted movements, P* is the estimate of the probability of correctly predicting the events under the null that forecasts and realizations are independently distributed, and V(P^) and V(P*) are the consistent estimates of the variances. The null hypothesis of independence between forecasts and realizations implies the null hypothesis of the proposed test of predictive failure. Empirical Results In-sample Forecast Evaluation

28 Empirical Results In-sample Forecast Evaluation Variables in the systemUDDDDUUU 1. Oil price (po) Foreign Interest Rate (r * ) Foreign Output (y * ) Foreign Inflation ( * ) Domestic Interest Rate (r) Exchange Rate (e) Domestic Output (y) Relative Money Supply (m - m * ) Domestic Inflation ( ) Relative CA Balances (ca – ca * )1511 Example: In-sample forecast of Model 3

29 Results in four models: Hit ratios: for Model for Model for Model for Model 4 Kuipers Score: for Model for Model for Model for Model 4 Pesaran-Timmermann test statistics: for Model for Model for Model for Model 4 All of these Pesaran-Timmermann test statistics (have a standard normal distribution under the null) are statistically significant and the greater value indicates higher accuracy. Empirical Results In-sample Forecast Evaluation

30 Empirical Results Out-of-sample Forecast Evaluation Central Forecasts (based on 4-quarter moving average series) Model 1 Model 2 Model 3 Model 4

31 Empirical Results Out-of-sample Forecast Evaluation Central Forecasts of Changes (based on 4-quarter MA series) Model 1 Model 2 Model 3 Model 4

32 Empirical Results Predictive Distribution Function Absolute changes in Exchange Rate range from 0% - 14% Model 1 Model 2 Model 3 Model 4

33 Empirical Results Extreme Exchange Rate Changes Absolute changes in Exchange Rate > 15% - 20% Model 1 Model 2 Model 3 Model 4

34 There exists a long-run relationship between exchange rate and macroeconomic fundamentals; The conditional vector error correction equation of exchange rate provides relatively high R 2 ; This suggests that the usual imposition of restrictions on the exchange rate equation, which are not suggested by theory, might have made it misspecified. Reasonable impulse responses of key variables with respect to oil price and domestic monetary policy shock; Results also show how sensitive exchange rate and the exchange rate equation are with respect to shocks. Promising in-sample forecasts (directional changes), which show the predictive power of macro fundamentals for exchange rate. Out-of-sample forecast provide a broad picture of exchange rate forecasting. More interesting single events of exchange rate and joint events between exchange rate and other variables (such as current account balances, inflation) will be considered in future research. Concluding Remarks

35 Engle C., Mark N. C. and Kenneth W. (2007) Exchange Rate Models are not as bad as you think, Working Paper, University of Wisconsin and NBER Groen, Jan J. J. (2000) The Monetary Exchange Rate Model as a Long-run Phenomenon, Journal of International Economics, vol. 52, Groen, Jan J. J. (2005) Exchange Rate Predictability and Monetary Fundamentals in a Small Multi- Country Panel, Journal of Money, Credit, and Banking, vol. 37, no. 3, pp Lars E. O. Svensson (1994) Fixed Exchange Rates as a Means to Price Stability: What we have learned?, European Economic Review, vol. 38, Mark, N. (1995) Exchange Rates and Fundamentals: Evidence on Long-horizon Predictability, American Economic Review, vol. 85, Mark, Nelson C. and D. Sul (2001), Nominal Exchange Rates and Monetary Fundamentals: Evidence from a Small Pots-Bretton Woods Panel, Journal of International Economics, vol. 53, pp Meese, R. and Rogoff, K. (1983a) Empirical Exchange Rate Models of the Seventies: Do They Fit Out of Sample? Journal of International Economics, vol.14, pp Meese, R. and Rogoff, K. (1983b) The Out-of-.Sample Failure of Empirical Exchange Rate Models: Sampling Error or Misspecification? in J.A. Frenkel (ed.) Exchange Rates and International Macroeconomics, Chicago: Chicago University Press and National Bureau of Economic Research Mervyn King (1997) Change in UK Monetary Policy: Rules and Discretion in Practice, Journal of Monetary Economics, vol. 39, Key References

36 Paul Soderlind (2000) Market Expectations in the UK before and after the ERM crisis, Economica, 67, 1-18 Pesaran, M. H. and Y. Shin (2002) Long-run Structural Modelling, Econometric Review, vol. 21, pp Pesaran, M. H., Y. Shin and R. J. Smith (2000) Structural Analysis of Vector Error Correction Models with Exogenous I(1) Variables, Journal of Econometrics, vol. 97, pp Pesaran, M. H. and A. Timmermann (1992) A Simple Nonparametric Test of Predictive Performance, Journal of Business & Economic Statistics, vol. 10, no. 4, pp Rapach, D. and M. Wohar (2002) Testing the Monetary Model of Exchange Rate Determination: New Evidence from a Century of Data, Journal of International Economics, vol. 58, pp Shin, Y. (2007) The Cointegrating VAR Model of the Korean Macro-economy, Working Paper, University of Leeds Smith, P. N. and M. R. Wikens (1986) An Empirical Investigation into the Causes of Failures if the Monetary Model of the Exchange Rate, Journal of Applied Econometrics, vol. 1, no. 2, pp Key References

37 THANK YOU FOR YOUR ATTENTION!


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