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

The article reviews the book "Monetary Policy, Inflation, and the Business Cycle: An Introduction to the New Keynesian Framework," by Jordi Gali.

Monetary Policy, Inflation, and the Business Cycle: An Introduction to the New Keynesian Framework

The existing literature promotes a number of alternative methods to test for the presence of contagion during Þnancial market crises. This paper reviews those methods, and shows how they are related in a uniÞed framework. A number of extensions are also suggested which allow for multivarite testing, endogeneity issues and structural breaks.

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Empirical Modelling of Contagion: A Review of Methodologies

We develop an 11-variable structural VAR for the Australian economy over the period 1980 to 1998. The VAR methodology has only relatively recently been applied in the Australian context, despite its popularity in quantitative macroeconomics internationally. Our model includes an overseas sector which distinguishes between goods and asset markets so as to disentangle the effects of shocks emanating from each source. We utilize our model to dissect the Australian growth cycle into its separate influences and to study the Asian crisis. Throughout there is a strong emphasis upon identifying the impact of monetary policy.

A Structural VAR Model of the Australian Economy

An empirical model of multiple asset classes across countries is formulated in a latent factor framework. A special feature of the model is that financial market linkages during periods of financial crises, including spillover and contagion effects, are formally specified. The model also captures a range of common factors including global shocks, country and market shocks, and idiosyncratic shocks. The framework is applied to modelling linkages between currency and equity markets during the East Asian financial crisis of 1997-98. The results provide strong evidence that cross-market links are important. Spillovers have a relatively larger effect on volatility than contagion, but both are statistically significant.

Unravelling financial market linkages during crises

https://www.luiss.it/sites/www.luiss.it/files/1paper.pdf

Mardi Dungey

Papers

Monetary Policy, Inflation, and the Business Cycle: An Introduction to the New Keynesian Framework

Empirical Modelling of Contagion: A Review of Methodologies

A Structural VAR Model of the Australian Economy

Unravelling financial market linkages during crises

Contagion in International Bond Markets during the Russian and LTCM Crises