In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.

Forecasting, Structural Time Series Models and the Kalman Filter

The financial market turmoil in 2007 and 2008 has led to the most severe financial crisis since the Great Depression and threatens to have large repercussions on the real economy The bursting of the housing bubble forced banks to write down several hundred billion dollars in bad loans caused by mortgage delinquencies At the same time, the stock market capitalization of the major banks declined by more than twice as much While the overall mortgage losses are large on an absolute scale, they are still relatively modest compared to the $8 trillion of US stock market wealth lost between October 2007, when the stock market reached an all-time high, and October 2008 This paper attempts to explain the economic mechanisms that caused losses in the mortgage market to amplify into such large dislocations and turmoil in the financial markets, and describes common economic threads that explain the plethora of market declines, liquidity dry-ups, defaults, and bailouts that occurred after the crisis broke in summer 2007 To understand these threads, it is useful to recall some key factors leading up to the housing bubble The US economy was experiencing a low interest rate environment, both because of large capital inflows from abroad, especially from Asian countries, and because the Federal Reserve had adopted a lax interest rate policy Asian countries bought US securities both to peg the exchange rates at an export-friendly level and to hedge against a depreciation of their own currencies against the dollar, a lesson learned from the Southeast Asian crisis of the late 1990s The Federal Reserve Bank feared a deflationary period after the bursting of the Internet bubble and thus did not counteract the buildup of the housing bubble At the same time, the banking system underwent an important transformation The

/pdf/deciphering-the-liquidity-and-credit-crunch-2007-2008-3612pahtil.pdf

Deciphering the Liquidity and Credit Crunch 2007-2008

This paper surveys the most important developments in multivariate ARCH-type modelling. It reviews the model specifications, the inference methods, and the main areas of application of these models in financial econometrics.

Multivariate GARCH Models: A Survey

The Analysis of Time Series: An Introduction, 4th edn. By C. Chatfield. ISBN 0 412 31820 2. Chapman and Hall, London, 1989. 242 pp. £13.50.

The Analysis of Time Series: An Introduction

A rapidly growing literature has documented important improvements in financial return volatility measurement and forecasting via use of realized variation measures constructed from high-frequency returns coupled with simple modeling procedures. Building on recent theoretical results in Barndorff-Nielsen and Shephard (2004a, 2005) for related bi-power variation measures, the present paper provides a practical and robust framework for non-parametrically measuring the jump component in asset return volatility. In an application to the DM/$ exchange rate, the S&P500 market index, and the 30-year U.S. Treasury bond yield, we find that jumps are both highly prevalent and distinctly less persistent than the continuous sample path variation process. Moreover, many jumps appear directly associated with specific macroeconomic news announcements. Separating jump from non-jump movements in a simple but sophisticated volatility forecasting model, we find that almost all of the predictability in daily, weekly, and monthly return volatilities comes from the non-jump component. Our results thus set the stage for a number of interesting future econometric developments and important financial applications by separately modeling, forecasting, and pricing the continuous and jump components of the total return variation process.

/pdf/roughing-it-up-including-jump-components-in-the-measurement-35xg2w5blh.pdf

Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility

This paper develops a means of visualizing the vulnerability of complex systems of financial interactions around the globe using Neural Network clustering techniques. We show how time-varying spillover indices can be translated into two dimensional crisis maps. The crisis maps have the advantage of showing the changing paths of vulnerability, including the direction and extent of the effect between source and affected markets. Using equity market data for 31 global markets over 1998–2017 we provide these crisis maps. These tools help portfolio managers and policy makers to distinguish which of the available tools for crisis management will be most appropriate for the form of vulnerability in play.

/pdf/crisis-transmission-visualizing-vulnerability-3woq85l2gc.pdf

Crisis transmission: visualizing vulnerability

Quantile relationships between standard, diffusion and jump betas across Japanese banks

This paper examines the behavior of industry-level betas and its implications for industry-based portfolio diversification. With the recent advances in disentangling continuous and jump price movements using high frequency data, we are able to estimate the time-varying betas towards both the continuous and jump systematic risk for firms from different industries. Our results show that both beta estimates vary considerably over the sample period of 2003 to 2011. There is strong evidence of structural change in the continuous betas for most sectors during the global financial crisis. In general the continuous betas are usually much smaller than the jump betas. Distinct behaviors in both the continuous and jump betas between sectors suggest a certain degree of clustering of firms in the same sector. These findings provide new insights in cross-industry risk diversification.

Continuous and Jump Betas: Firm and Industry Level Evidence

Recent international financial crises highlight the advantages of understanding the global financial system as a network of economies in which cross-border financial linkages are fundamental to the spread of systemic risk. We investigate the changing network of financial markets for six periods from 1995–2016, constructing a network that captures the concepts of the direction of links between markets, the significance of these links, and their strength. Emphasis is placed on the transition of the networks before and after the Asian financial crisis of 1997–1998 and the global financial crisis of 2008–2009. The analysis demonstrates the increase in interconnectedness during periods of stress and the fall in the number of links in postcrisis periods. At the same time, the results reveal a general deepening of the connections of the Asian market with the rest of the world over the past 2 decades. They also suggest that many of these markets have transitioned from being primarily linked to developed non-Asian markets through key bridge markets (such as Hong Kong, China) to developing stronger direct links with these external markets, highlighting the importance of key geographical nodes in market development.

The Changing Network of Financial Market Linkages: The Asian Experience

This paper concentrates upon the short-term changes in the exchange rate that might be considered as 'clouding' more fundamental changes. We focus on week to week changes in nominal bilateral exchange rates. The main concern of this paper is an attempt to identify the sources of exchange rate volatility over the past decade. Specifically, the paper tries to separate international causes of volatility from those associated with a specific country.

Mardi Dungey

Papers

Crisis transmission: visualizing vulnerability

Quantile relationships between standard, diffusion and jump betas across Japanese banks

Continuous and Jump Betas: Firm and Industry Level Evidence

The Changing Network of Financial Market Linkages: The Asian Experience

A Multilateral Approach to Decomposing Volatility in Belateral Exchange Rates