Univ., Habil.-Schr.--Erfurt, 2004.

Frankfurt (Main), University, Diss., 2007.

Zugl.: Zürich, Univ., Diss., 2001. / Buchhandelsausg. der Diss. Zürich, 2001. Literaturverz.

Der Risikobegriff bezieht sich auf die Wahrscheinlichkeit eines Schadens aufgrund einer Gefährdungsexposition, in der Finanzwelt meist finanzielle Verluste. Viele Risiken der globalen Finanzwirtschaft sind unbekannt. „Wir wissen es, wenn wir es sehen“, um Potter Stewart (1964) zu paraphrasieren. Der Financial Risk Meter (FRM) soll Aufschluss über die Entstehung systemischer Risiken geben. Durch Verwendung von Quantilregressionstechniken ist der FRM nicht nur ein Maß für finanzielle Risiken. Er bietet durch seine Netzwerktopologie einen tiefen Einblick in die Spill-over-Effekte, die sich als systemische Risikoereignisse manifestieren können. Das FRM-Framework wird in verschiedenen Märkten und Regionen entwickelt. Die FRM-Daten werden für Risiko-Prognose sowie für Portfoliooptimierung genutzt. In Kapitel 1 wird der FRM vorgestellt und auf die Aktienmärkte in den USA und Europa, sowie auch auf die Zinsmärkte und Credit-Default-Swaps angewendet. Der FRM wird dann verwendet, um wirtschaftliche Rezessionen zu prognostizieren. In Kapitel 2 wird der FRM auf den Markt der Kryptowährungen angewendet, um das erste Risikomaß für diese neue Anlageklasse zu generieren. Die errechneten FRM-Daten zu Abhängigkeiten, Spillover-Effekten und Netzwerkaufbau werden dann verwendet, um Tail-Risk-optimierte Portfolios zu erstellen. Der Portfoliooptimierungsansatz wird in Kapitel 3 weitergeführt, in dem der FRM auf die sogenannten Emerging Markets (EM)-Finanzinstitute angewendet wird, mit zwei Zielen. Einerseits gibt der FRM für EM spezifische Spillover-Abhängigkeiten bei Tail-Risk-Ereignissen innerhalb von Sektoren von Finanzinstituten an, zeigt aber auch Abhängigkeiten zwischen den Ländern. Die FRM-Daten werden dann wieder mit Portfoliomanagementansätzen kombiniert. In Kapitel 4 entwickelt den FRM for China ist, eines der ersten systemischen Risikomaße in der Region, zeigt aber auch Methoden zur Erkennung von Spill-Over-Kanälen in Nachbarländer und zwischen Sektoren. / The concept of risk deals with the exposure to danger, in the world of finance the danger of financial losses. In a globalised financial economy, many risks are unknown. "We know it when we see it", to paraphrase Justice Potter Stewart (1964). The Financial Risk Meter (FRM) sheds light on the emergence of systemic risk. Using of quantile regression techniques, it is a meter for financial risk, and its network topology offers insight into the spill-over effects risking systemic risk events. In this thesis, the FRM framework in various markets and regions is developed and the FRM data is used for risk now- and forecasting, and for portfolio optimization approaches. In Chapter 1 the FRM is presented and applied to equity markets in the US and Europe, but also interest rate and credit-default swap markets. The FRM is then used to now-cast and predict economic recessions. In Chapter 2 the FRM is applied to cryptocurrencies, to generate the first risk meter in this nascent asset class. The generated FRM data concerning dependencies, spill-over effects and network set-up are then used to create tail-risk optimised portfolios. In Chapter 3 the FRM is applied to the global market Emerging Market (EM) financial institutions. The FRM for EM gives specific spill-over dependencies in tail-risk events within sectors of financial institutions, but also shows inter-country dependencies between the EM regions. The FRM data is then combined with portfolio management approaches to create tail-risk sensitive portfolios of EM Financial institutions with aim to minimize risk clusters in a portfolio context. In Chapter 4 the Financial Risk Meter for China is developed as the first systemic risk meter in the region, but also derives methods to detect spill-over channels to neighbouring countries within and between financial industry sectors.

This thesis explores the application of Markowitz' Modern Portfolio Theory onto 220 years of financial returns for 13 metals and 21 poly-metallic ore types. The interdisciplinary research shows that poly-metallic ores can be described as naturally occurring portfolios that were diversified by natural geological processes. Safest and optimal portfolios for metals and ores can be computed for different time horizons using portfolio optimization algorithms. Results for optimized ore portfolios are thereby subject to geological constraints. The study revealed that commodity cycles last between six and twenty years and exhibit clockwise and counterclockwise motions in the risk-return framework. The cycle length differences for clockwise cycles are statistically significant and thus specific to all investigated metals and ores. By incorporating novel cycle parameters into decision making tools it is suggested that current industry decisions for resource development can be improved. Insights into the performance of metals and ores through the industrial cycles, as well as into the frequency of profitable super cycles can assist Metals & Mining executives in strategic planning and investment.:Introduction 1 Data 3 Metals & ore types studied 5 2.1 Metals.......................................... 5 2.2 Ore types ........................................ 5 2.3 Prices .......................................... 10 2.4 Summary ........................................ 12 II Analysis 13 3 Modern Portfolio Theory 15 3.1 Overview ........................................ 15 3.2 Definitions........................................ 15 3.3 Assumptions ...................................... 17 3.4 Discussion & Conclusion................................ 18 4 Poly-metallic ores as natural portfolios 19 4.1 Objectives........................................ 19 4.2 Results.......................................... 19 4.3 Summary & Discussion................................. 24 4.4 Conclusion ....................................... 25 5 Static portfolio optimization 27 5.1 Objectives........................................ 27 5.2 Assumptions ...................................... 27 5.3 Results.......................................... 27 5.4 Summary & Discussion................................. 31 5.5 Conclusion ....................................... 32 6 Dynamic portfolio optimization 33 6.1 Assumptions ...................................... 33 6.2 Results.......................................... 34 6.3 Summary & Discussion................................. 44 6.4 Conclusion ....................................... 45 7 Commodity cycles & metal assets 47 7.1 Commodity cycles ................................... 47 7.2 Commodity cycle observations ............................ 54 7.3 Summary ........................................ 76 7.4 Discussion........................................ 77 7.5 Conclusion ....................................... 78 III Application 81 8 Commodity cycles & resource development strategies 83 8.1 The timing of mine development and mining start-up................ 83 8.2 Lead times from discovery to operation........................ 88 8.3 Exploration....................................... 89 8.4 Project valuation considerations............................ 91 8.5 Summary & Discussion................................. 92 8.6 Conclusion ....................................... 93 9 Industrial cycles & modern history 95 9.1 The Metal Markets Indicator-MMI ......................... 95 9.2 The Metal Markets Indicator & the economy .................... 97 9.3 The MMI & military conflict ............................. 105 9.4 MMI cyclicality..................................... 115 9.5 Summary & Discussion................................. 122 9.6 Conclusion ....................................... 123 10 Industrial cycles & metal performance 125 10.1 Methodology ...................................... 125 10.2 Metal performance during technological epochs ................ 126 10.3 Discussion........................................ 133 10.4 Conclusion ....................................... 137 11 Industrial cycles & ore type preferences 139 11.1 Coal Age ........................................ 139 11.2 Oil Age ......................................... 142 11.3 Atomic Age....................................... 144 11.4 Discussion........................................ 146 11.5 Conclusion ....................................... 150 12 Industrial cycles & ore provinces 151 12.1 Ore genetic models and industrial cycles....................... 151 12.2 Ore geology and geography .............................. 154 12.3 Ore provenances and mining technology ....................... 156 12.4 Discussion........................................ 157 12.5 Conclusion ....................................... 157 13 The state and future of the M&M Industry 159 13.1 The current state.................................... 159 13.2 The dawn of a new Industrial Age .......................... 163 13.3 The future........................................ 164 13.4 Summary & Discussion................................. 167 13.5 Conclusion ....................................... 168 14 Summary 169 15 Conclusion 171 IV Appendix 173 Bibliography 233 Index 245