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

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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.

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

Since the deregulation of the Australian dollar market in December 1983, considerable effort has been devoted by the central bank to understanding movements in the value of the currency. As the Reserve Bank of Australia (RBA) has a pivotal role to play in currency markets, attention has been focussed on the modelling techniques used by the Bank's researchers in this process. This paper examines the ancestral development of the current model of the Australian Trade Weighted Index (rtwi) used at the RBA, as specified in Beechey et al. (2000). Estimates and forecasting evaluations of the various models imply that only the relationships between the rtwi, the terms of trade and interest differentials hold consistently, providing the empirical foundation for the current RBA model.

A Perspective on Modelling the Australian Real Trade Weighted Index since the Float

R&D and wholesale trade are critical to the economy: Identifying dominant sectors from economic networks☆

We develop a theoretical and empirical framework to model the international connections between financial institutions and sovereign debt markets. By allowing for both good and poor returns on the investments of financial institutions in real economy firms and the potential for haircuts in sovereign debt-markets we show how shock transmission and the default probabilities of these entities are affected through a network of these institutions. To model the financial network empirically, directional edges are established via Granger causality tests between CDS spreads, while the weights of the edges are obtained from variance decompositions. The empirical framework nests both tests of contagion and changes in the structure of the network itself. The network is found to be "robust but fragile" meaning that either a large enough single shock, or a number of small contemporaneous shocks can result in the propagation of crises. Between 2003 and 2013 for a global model of 67 financial institutions and 40 sovereigns we show that the completeness of the network changes substantially, reflecting changes in both the number and strengths of the links between them. The resulting changes in the probability of default for sovereigns and institutions demonstrate the fragility of the combined system when under stress from alternative sources.

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The Changing International Network of Sovereign Debt and Financial Institutions

Detecting contagion during financial crises requires the demarcation of crisis periods. We develop a method for endogenously dating both the start and finish of crises, along with measuring contagion effects. Identification is achieved by coupling smooth transition functions with structural GARCH. In an application to US equity, bond and REITs returns for 2001-2010, we identify four phases; a pre-crisis period to July 2007, two phases of crisis up to and following October 2008, and a post-crisis phase from mid-May 2009. We detect significant contagion during the crisis and find evidence that the post-crisis period has not returned to pre-crisis relations.

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Endogenous crisis dating and contagion using smooth transition structural GARCH

Monetary and fiscal policy actions are designed to influence economic outcomes. Their interactions may have important, and sometimes contradictory, impacts. This paper incorporates fiscal and monetary policy shocks into a small open economy SVAR model of the Australian economy, incorporating identification by combining sign restrictions, exclusion restrictions and cointegration, while directly recognising the mixed non-stationary and stationary nature of the relevant variables. The results over the period to 2006 show the impact of government expenditure and revenue shocks on output and debt particularly, indicating the changes in interest rates occuring in response to fiscal policy shocks. Contractionary monetary policy shocks result in reduced government revenue, but are also associated with reduced debt to GDP ratios. Ongoing work examines the contribution of policy responses to the behaviour of this economy during the Global Financial crisis.

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Mardi Dungey

Papers

A Perspective on Modelling the Australian Real Trade Weighted Index since the Float

R&D and wholesale trade are critical to the economy: Identifying dominant sectors from economic networks☆

The Changing International Network of Sovereign Debt and Financial Institutions

Endogenous crisis dating and contagion using smooth transition structural GARCH

Fiscal and Monetary Policy in Australia: an SVAR Model