Ward, Felix: Essays in International Macroeconomics and Financial Crisis Forecasting. - Bonn, 2018. - Dissertation, Rheinische Friedrich-Wilhelms-Universität Bonn.
Online-Ausgabe in bonndoc: https://nbn-resolving.org/urn:nbn:de:hbz:5-51488
@phdthesis{handle:20.500.11811/7467,
urn: https://nbn-resolving.org/urn:nbn:de:hbz:5-51488,
author = {{Felix Ward}},
title = {Essays in International Macroeconomics and Financial Crisis Forecasting},
school = {Rheinische Friedrich-Wilhelms-Universität Bonn},
year = 2018,
month = aug,

note = {This thesis contributes long-run perspectives to the research on international macroeconomics and macro-finance. Chapters 2 and 3, analyze international financial linkages and their evolution over the past 150 years. Chapter 4 analyzes external adjustment under the pre-1914 Gold Standard – a fixed exchange rate regime in many ways reminiscent of today's euro area. Finally, chapter 5 uses the accumulated financial crisis experience since 1870 to evaluate the financial crisis forecasting performance of modern machine learning algorithms.
Chapter 2, titled "Global risk-taking, exchange rates and monetary policy", revisits one of the core ideas in international macroeconomics, the idea that floating exchange rates help to decouple local interest rates from foreign rates. I find that this is only the case for safe rates, but not for risky rates. For risky rates, I find that their co-movement has increased over the 20th century, regardless of exchange rate regime. Why have floating exchange rates become less effective in decoupling risky rates? I argue that the growing role of leverage-constrained banks in global asset markets is key. More specifically, I introduce an international banking model in which banks' leverage constraints induce excessive volatility into risky rates, and their arbitrage activity spreads this volatility internationally, thus overwhelming floating exchange rates, which are already pinned down by safe rates.
In chapter 3, which is joint work with Òscar Jordà, Alan M. Taylor and Moritz Schularick, we analyze the international co-movement of financial cycles and the effect of U.S. monetary policy on global asset prices. We show that the co-movement of financial variables has increased in the long run. The sharp increase in the co-movement of global equity markets in the past three decades is particularly notable. We demonstrate that fluctuations in risk premiums, and not risk-free rates and dividends, account for most of the observed equity price synchronization post-1980. We also show that U.S. monetary policy has come to play an important role as a source of fluctuations in risk appetite across global equity markets.
Chapter 4, titled "When do fixed exchange rates work? Evidence from the Gold Standard" explores the circumstances under which a fixed exchange rate regime works. In joint work with Yao Chen, we empirically and theoretically analyze one of the world's largest and most durable fixed exchange rate regimes, the Gold Standard. External adjustment under the Gold Standard was associated with few, if any, output costs. In this chapter, we evaluate how flexible prices, international migration, and monetary policy contributed to this benign adjustment experience. For this purpose, we build and estimate an open economy model for the Gold Standard (1880-1913). We find that the output resilience of Gold Standard members that underwent external adjustment was primarily a consequence of flexible prices. When hit by a shock, quickly adjusting prices induced import- and export responses that stabilized incomes. Crucial in this regard was a historical contingency: namely large primary sectors, whose flexibly priced products drove the export booms that stabilized output during major external adjustments.
Finally, chapter 5 contributes to the literature on financial crisis forecasting, using high dimensional data and modern machine learning algorithms. In this chapter, titled "Spotting the danger zone: Forecasting financial crises with classification tree ensembles and many predictors", I introduce classification tree ensembles (CTEs) to the banking crisis forecasting literature. I show that CTEs substantially improve out-of-sample forecasting performance over best practice early-warning systems. CTEs enable policymakers to correctly forecast 80% of crises with a 20% probability of incorrectly forecasting a crisis. These findings are based on a long-run sample (1870 - 2011), and two broad post-1970 samples which together cover almost all known systemic banking crises. More particular, I show that the marked improvement in forecasting performance over conventional best practice models results from the combination of many classification trees into an ensemble, and the use of many predictors.},

url = {http://hdl.handle.net/20.500.11811/7467}
}

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