This research explores the impact of foreign exchange rate interventions on the behaviour of exchange rate returns and their volatility. In 2017, monetary interventions are actual as they have never been before. However, they have been criticised for not being effective and existing empirical evidence is mixed. The present research applies models from the Garch framework, while using monthly data from 2001 to 2017 on the Usdeur, Gbpreur, Jpyeur and Inreur rate.
The results indicate that monetary interventions have a higher impact on developed country currencies than on emerging markets currencies. In addition, evidence is found that the volatility increased after the financial crisis.
Table of Contents
Introduction
1. Literature Review
2. Data
2.1 Data Description
2.2 Volatility Clustering
2.3 Multiple Breakpoint - Bai - Perron test
2.4 Test for stationarity – Unit Root
2.5 Correlogram
2.6 Descriptive Statistics
3. Methodology
3.1 ARCH
3.2 GARCH(1,1)
3.3 GJR GARCH(1,1)
3.4 Exponential GARCH(1,1)
3.5 Integrated GARCH(1,1)
3.6 Volatility Forecasting
4. Empirical Results
4.1 Results of the ARCH Effects testing
4.2 Results of the GARCH(1,1) model
4.3 Results of the GJR GARCH(1,1) model
4.4 Results of the Exponential GARCH(1,1) model
4.5 Results of the Integrated GARCH(1,1) model
4.6 Results of Volatility Forecasting
Conclusion
References
Appendix
Objectives & Research Topics
This research aims to analyze the impact of central bank monetary interventions on the returns and volatility of foreign exchange rates, specifically considering the medium-term effects in the wake of the 2008 financial crisis. The central research question explores whether these interventions effectively influence currency volatility and exchange rate returns, and how the stability of these currencies evolved post-crisis.
- Analysis of monetary intervention effects on USDEUR, GBPREUR, JPYEUR, and INREUR.
- Application of the GARCH framework (GARCH, GJR-GARCH, EGARCH, IGARCH) to model volatility.
- Investigation of currency volatility behaviors pre- and post-2008 financial crisis using dummy variables.
- Comparative analysis of intervention effectiveness between developed markets (DMs) and emerging markets (EMs).
- Evaluation of forecasting performance using RMSE and MAE metrics.
Excerpt from the Book
2.2 Volatility Clustering
Brooks (2008) states that volatility clustering is a fundamental characteristic of financial data. Asteriou and Hall (2015) summarise volatility clustering by the size of prize changes. They explain further that large price changes are followed by price changes of similar size and therefore obtain higher volatility. In contrast, small price changes are followed by price changes of similar size while the volatility is low. Figure 1 illustrates the deviation of all currency rate returns and proves that the time series data used in this study exhibits volatility clustering.
All currency returns indicated a relative high level of deviation until 2003, which was caused by the early 2000s recession and under consideration of the dot-com bubble burst. As the chart of INREUR illustrates, the returns deviated much more than the currencies from DMs. USDEUR, GBPEUR and JPYEUR experienced a period of low volatility and stable markets until 2007, when the financial crisis started to impact. During the period from 2007 to 2010, all charts indicated high volatility, which decreased in 2011 to a moderate level. However, that was not the case for returns on INREUR, since the slowdown of economic growth in India caused higher volatility as pointed out by Riley & Yan (2013). Until the estimation of the present research, INREUR exhibited a high level of volatility, except for a short period in 2014. DMs recovered moderately from the volatility during the financial crisis and indicated low levels of volatility during 2011 until 2015. In 2015, DMs and EMs experienced periods of high return deviation, which was caused by lower commodity prices and the discontinuation of quantitative easing by the fed (De Bock & De Carvalho Filho, 2015).
Summary of Chapters
Introduction: Provides the motivation for the study, outlining the role of central banks and the controversy surrounding the effectiveness of monetary interventions in stabilizing exchange rates.
1. Literature Review: Discusses existing empirical evidence regarding central bank interventions, highlighting the shift in research methods from the Bretton Woods era to modern GARCH modeling.
2. Data: Examines the financial dataset (monthly returns of selected currency pairs) and validates the requirements for GARCH model application through stationarity and autocorrelation tests.
3. Methodology: Introduces the mathematical formulations for various GARCH models, including ARCH, GARCH(1,1), GJR-GARCH, EGARCH, and IGARCH, used to analyze return and volatility dynamics.
4. Empirical Results: Presents the estimation findings of the applied models, evaluates the impact of monetary interventions, and performs volatility forecasting to compare model efficiency.
Conclusion: Summarizes the key findings, confirming that while monetary interventions impact currency volatility, the effects vary between developed and emerging markets, and notes the potential for future complexity in the monetary system.
Keywords
Monetary Interventions, Exchange Rate Volatility, GARCH, GJR GARCH, EGARCH, IGARCH, Foreign Exchange Reserves, Financial Crisis, USDEUR, GBPREUR, JPYEUR, INREUR, Volatility Forecasting, Developed Markets, Emerging Markets.
Frequently Asked Questions
What is the core focus of this research?
This work investigates the impact of monetary interventions by central banks on foreign exchange rate returns and their associated volatility, specifically focusing on the medium-term timeframe.
Which currency pairs are analyzed in this study?
The research examines monthly data for the USDEUR, GBPREUR, JPYEUR, and INREUR exchange rates from 2001 to 2017.
What is the primary objective of this thesis?
The primary goal is to determine the effectiveness of central bank interventions in stabilizing currencies and to analyze how volatility has behaved, particularly following the 2008 financial crisis.
Which scientific methods are applied?
The study utilizes the GARCH (Generalized Autoregressive Conditional Heteroskedasticity) framework, specifically applying GARCH(1,1), GJR-GARCH(1,1), EGARCH(1,1), and IGARCH(1,1) models to capture volatility dynamics.
What does the main body of the work cover?
The main part covers data collection and diagnostic testing, the technical derivation of various GARCH variance equations, the empirical estimation of these models, and an evaluation of volatility forecasting accuracy.
Which keywords best describe this research?
The study is characterized by terms such as Monetary Interventions, Exchange Rate Volatility, GARCH, GJR GARCH, and Financial Crisis.
Why is the GJR GARCH(1,1) model highlighted in the results?
The GJR GARCH(1,1) model was determined to be the most efficient framework for the dataset based on information criteria like the Akaike Info Criterion (AIC) and the Schwarz Info Criterion (SIC).
How does the financial crisis affect the volatility results?
The research includes a dummy variable for the financial crisis, which indicated that volatility increased significantly for most developed market currencies, whereas the INREUR rate remained largely unaffected by the crisis event in the variance models.
- Citar trabajo
- Marvin Arras (Autor), 2017, Intervention and Foreign Exchange Volatility. New evidence from Developed and Emerging Countries, Múnich, GRIN Verlag, https://www.grin.com/document/387544
