Mapping Financial Stability

verfasst von: Peter Sarlin

Verlag: Springer Berlin Heidelberg

Buchreihe : Computational Risk Management

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This book approaches macroprudential oversight from the viewpoint of three tasks. The focus concerns a tight integration of means for risk communication into analytical tools for risk identification and risk assessment. Generally, this book explores approaches for representing complex data concerning financial entities on low-dimensional displays. Data and dimension reduction methods, and their combinations, hold promise for representing multivariate data structures in easily understandable formats. Accordingly, this book creates a Self-Organizing Financial Stability Map (SOFSM), and lays out a general framework for mapping the state of financial stability. Beyond external risk communication, the aim of the visual means is to support disciplined and structured judgmental analysis based upon policymakers' experience and domain intelligence.

Inhaltsverzeichnis

Frontmatter

Chapter 1. Introduction

Abstract

The ongoing global financial crisis has demonstrated the importance of a system-wide, or macroprudential, approach to safeguarding financial stability. This book concerns macroprudential oversight from the viewpoint of three tasks: Within analytical tools for risk identification and risk assessment, the focus concerns a tight integration of means for risk communication. This introduction is structured as follows. First, it presents the background of macroprudential oversight and briefly positions the topic of this book. Then, it discusses the two key objectives of this book and untangles them into five research questions, which is followed by a chapter-specific overview of the book.

Peter Sarlin

Chapter 2. Macroprudential Oversight

Abstract

Paraphrasing Milton Friedman’s statement about Keynesians, Borio (2011) stated “We are all macroprudentialists now”. This chapter provides an overview of macroprudential oversight. It focuses first on the definition of financial systems and financial (in)stability, as well as fragilities in financial systems, and the concept of systemic risk. After this, we briefly summarize some theoretical and empirical underpinnings of three identified forms of systemic risk. Then, the main focus of this chapter is to give an overview of the state of the art of risk assessment and identification tools used by macroprudential policymakers, especially the use of visualization tools. Finally, we relate the fragilities, risks and tools to the macroprudential oversight process, and summarize key implications of this chapter for the rest of the book.

Peter Sarlin

Chapter 3. Macroprudential Data

Abstract

An understanding of all elements in the macroprudential oversight process is obviously crucial for safeguarding financial stability. While providing a basis, such a framework is still highly dependent on the underlying data. Access to complete, accurate, and timely data is central not only for policymakers to make good economic policy, but also for businesses and investors alike to make good financial decisions. However, data for macroprudential purposes are, not surprisingly, as complex as the system they describe. Alas, complexity oftentimes implies challenges. Gathering, synthesizing, understanding and analyzing these data is hence not an entirely unproblematic task. With the aim of having a holistic view of the financial system to ensure system-wide stability, rather than only being concerned about individual financial institutions, a macroprudential approach to oversight has a wide range of data demands and needs. As early-warning models were at the core of the previous chapter’s ending note, the key focus herein is also on input data for early-warning exercises. Yet, as macro stress-testing and contagion models will throughout this book be touched upon, this chapter will still provide a brief discussion on data needs for risk assessment tools as well.

Peter Sarlin

Chapter 4. Data and Dimension Reduction

Abstract

Data and dimension reduction techniques hold promise for representing data in easily understandable formats, as has been shown by their wide scope of applications. Data reductions provide summarizations of data by compressing information into fewer partitions, whereas dimension reductions provide low-dimensional overviews of similarity relations in data. Thus, these techniques provide means for exploratory data analysis (EDA). From a broader perspective, EDA is only one approach out of many in data mining, and knowledge discovery includes data mining as only one of its steps. To provide a holistic view in a top-down manner, we start by the broader concepts, and end with discussions of data and dimension reductions and their combination. As the aim of Chap. 5 is to provide a comparison of early dimension reduction methods, the focus of this chapter is also on more detailed presentations of so-called first-generation methods, including Multidimensional Scaling (MDS), Sammon’s mapping and the Self-Organizing Map (SOM).

Peter Sarlin

Chapter 5. Data-Dimension Reductions: A Comparison

Abstract

Data and dimension reduction techniques, and particularly their combination for Data-Dimension Reductions (DDR), have in many fields and tasks held promise for representing data in an easily understandable format. However, comparing methods and finding the most suitable one is a challenging task. In the previous chapter, we discussed the aim of dimension reduction in terms of three tasks. This chapter compares DDR combinations to financial performance analysis. To this end, after a general review of the literature on comparisons of data and dimension reduction methods, we discuss the aims and needs of DDR combinations in general and for the task at hand in particular.

Peter Sarlin

Chapter 6. Extending the SOM

Abstract

The standard Self-Organizing Map (SOM), while having merit for the task at hand, may be extended in multiple directions, not the least to better meet the demands set by macroprudential oversight and data. Along these lines, with a key focus on temporality, this chapter first discusses the literature on time in SOMs. This is followed by extensions to the standard SOM paradigm. In general, the chapter presents extensions to the SOM paradigm for processing data from the cube representation, i.e., along multivariate, temporal and cross-sectional dimensions, where a focus of emphasis is on a better processing and visualization of time. The motivation and functioning of the extensions is demonstrated with a number of illustrative examples.

Peter Sarlin

Chapter 7. Self-Organizing Financial Stability Map

Abstract

This chapter ties together most of the previous parts of this book. Macroprudential oversight and data alike not only motivate, but also provide guidelines for building tools with visual capabilities. Data and dimension reductions, as well as their combinations, provide means for creating visual displays for a wide range of tasks, whereas a qualitative comparison shows that the Self-Organizing Map (SOM) is suitable for the task we have at hand. This chapter unifies the above discussed topics by creating a SOM-based financial stability map, coined the Self-Organizing Financial Stability Map (SOFSM). The task involves five key building blocks: the SOM, crisis dates, vulnerability indicators, a model training framework and a model evaluation framework.

Peter Sarlin

Chapter 8. Exploiting the SOFSM

Abstract

This chapter exploits the Self-Organizing Financial Stability Map (SOFSM) for tasks in macroprudential oversight. The SOFSM was created in Chap. 7, whereas the Self-Organizing Map (SOM) extensions used for exploiting it were introduced in Chap. 6. The tasks performed with the SOFSM are two, risk identification and assessment, of which the former is supported by early-warning models and the latter by macro stress-testing and contagion or spillover models. The three models target the three respective forms of systemic risk: widespread imbalances, aggregate shocks and contagion and spillover risk. Drawing upon Sarlin and Peltonen (2013) and Sarlin (2013), the SOFSM is exploited by the means of the eight approaches.

Peter Sarlin

Chapter 9. Decomposing Financial Crises with SOTMs

Abstract

The provided models for macroprudential oversight have thus far concerned assessing the cross-sectional or temporal dimensions close to isolation. In this chapter, we turn the focus to exploring cross-sectional dynamics. The Self-Organizing Time Map (SOTM) provides means for visual dynamic clustering and thus also for illustrating dynamics in cross sections of multivariate macro-financial indicators. This is one of the key tasks in risk identification, when the focus is on build-up phases of imbalances in the entire cross section, such as the global dimension in country-level risks and a system-wide focus on data concerning individual financial intermediaries. With respect to the visual analytics mantra, the SOTM can be positioned similarly as the previously discussed Self-Organizing Financial Stability Map (SOFSM). The first decomposition applies the standard SOTM to describing the global financial crisis that started in 2007 in a manner that would be applicable for real-time surveillance. The second section uses a SOTM on time-to-event data to generalize patterns before, during and after financial crises.

Peter Sarlin

Chapter 10. Conclusions, Limitations and the Future

Abstract

Early identification of financial instabilities is of interest for a wide spectrum of decision-makers for a wide range of reasons. It is needless to say that the recent occurrences of instability have stimulated efforts in understanding and predicting financial stress. The work in this book has provided a wide range of tools for macroprudential oversight, whose common denominator is a visual representation. The tools focus on risk identification and assessment, with an ultimate aim to aid in risk communication. It is worth noting that the relevance of visual representations of tools for safeguarding financial stability lies not only in external risk communication, but also in generating insights in internal use to support risk identification and assessment. This chapter summarizes the key findings of the work in this book, discusses the limitations of the findings, and presents ideas for future research.

Peter Sarlin

Titel: Mapping Financial Stability
verfasst von: Peter Sarlin
Verlag: Springer Berlin Heidelberg
Electronic ISBN: 978-3-642-54956-4
Print ISBN: 978-3-642-54955-7
DOI: https://doi.org/10.1007/978-3-642-54956-4