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

We provide a model that links an asset's market liquidity - i.e., the ease with which it is traded - and traders' funding liquidity - i.e., the ease with which they can obtain funding. Traders provide market liquidity, and their ability to do so depends on their availability of funding. Conversely, traders' funding, i.e., their capital and the margins they are charged, depend on the assets' market liquidity. We show that, under certain conditions, margins are destabilizing and market liquidity and funding liquidity are mutually reinforcing, leading to liquidity spirals. The model explains the empirically documented features that market liquidity (i) can suddenly dry up, (ii) has commonality across securities, (iii) is related to volatility, (iv) is subject to “flight to quality¶, and (v) comoves with the market, and it provides new testable predictions. Keywords: Liquidity Risk Management, Liquidity, Liquidation, Systemic Risk, Leverage, Margins, Haircuts, Value-at-Risk, Counterparty Credit Risk

Market liquidity and funding liquidity

We provide a model that links a security’s market liquidity — i.e., the ease of trading it — and traders’ funding liquidity — i.e., their availability of funds. Traders provide market liquidity and their ability to do so depends on their funding, that is, their capital and the margins charged by their financiers. In times of crisis, reductions in market liquidity and funding liquidity are mutually reinforcing, leading to a liquidity spiral. The model explains the empirically documented features that market liquidity (i) can suddenly dry up (i.e. is fragile), (ii) has commonality across securities, (iii) is related to volatility, (iv) experiences “flight to liquidity” events, and (v) comoves with the market. Finally, the model shows how the Fed can improve current market liquidity by committing to improve funding in a potential future crisis.

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Market Liquidity and Funding Liquidity

This paper summarizes and explains the main events of the liquidity and credit crunch in 2007-08. Starting with the trends leading up to the crisis, I explain how these events unfolded and how four different amplification mechanisms magnified losses in the mortgage market into large dislocations and turmoil in financial markets.

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Deciphering the Liquidity and Credit Crunch 2007-08

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

In a financial system in which balance sheets are continuously marked to market, asset price changes appear immediately as changes in net worth, eliciting responses from financial intermediaries who adjust the size of their balance sheets. We document evidence that marked-to-market leverage is strongly procyclical. Such behavior has aggregate consequences. Changes in dealer repos—the primary margin of adjustment for the aggregate balance sheets of intermediaries—forecast changes in financial market risk as measured by the innovations in the Chicago Board Options Exchange Volatility Index (VIX). Aggregate liquidity can be seen as the rate of change of the aggregate balance sheet of the financial intermediaries.

Liquidity and Leverage

What are the welfare effects of enhanced dissemination of public information through the media and disclosures by market participants with high public visibility? For instance, is it always desirable to have frequent and timely publications of economic statistics by government agencies and the central bank? We examine the impact of public information in a setting where agents take actions appropriate to the underlying fundamentals, but they also have a coordination motive arising from a strategic complementarity in their actions. When the agents have no private information, greater provision of public information always increases welfare. However, when agents also have access to independent sources of information, the welfare effect of increased public disclosures is ambiguous.

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Social Value of Public Information

https://www.imf.org/external/np/seminars/eng/2008/fincycl/pdf/adsh.pdf

Today's financial regulatory systems assume that regulations which make individual banks safe also make the financial system safe. The eleventh Geneva Report on the World Economy shows that this thinking is flawed. Actions that banks take to make themselves safer can - in times of crisis - undermine the system's stability. The Report argues for a different approach. What is needed is micro-prudential (i.e. bank-level) regulation, macro-prudential (i.e. system-wide) regulation, and careful coordination of the two. Macro-prudential regulation in particular needs reform to ensure it countervails the natural decline in measured risk during booms and its rise in subsequent collapses. "Counter-cyclical capital charges" are the way forward; regulators should adjust capital adequacy requirements over the cycle by two multiples - the first related to above-average growth of credit expansion and leverage, the second related to the mismatch in the maturity of assets and liabilities. Changes to mark-to-market procedures are also needed. Macro- and micro-prudential regulation should be carried out by separate institutions since they differ in focus and expertise required. Central Banks should be tasked with macro-prudential regulation, Financial Services Authorities with micro-prudential regulation. Improved international coordination is also important. Since financial and asset-price cycles differ from country to country, counter-cyclical regulatory policy needs to be implemented mainly by the "host" rather than the "home" country.

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The fundamental principles of financial regulation

Many economic problems are naturally modeled as a game of incomplete information, where a player’s payoff depends on his own action, the actions of others, and some unknown economic fundamentals. For example, many accounts of currency attacks, bank runs, and liquidity crises give a central role to players’ uncertainty about other players’ actions. Because other players’ actions in such situations are motivated by their beliefs, the decision maker must take account of the beliefs held by other players. We know from the classic contribution of Harsanyi (1967–1968) that rational behavior in such environments not only depends on economic agents’ beliefs about economic fundamentals, but also depends on beliefs of higher-order – i.e., players’ beliefs about other players’ beliefs, players’ beliefs about other players’ beliefs about other players’ beliefs, and so on. Indeed, Mertens and Zamir (1985) have shown how one can give a complete description of the “type” of a player in an incomplete information game in terms of a full hierarchy of beliefs at all levels. In principle, optimal strategic behavior should be analyzed in the space of all possible infinite hierarchies of beliefs; however, such analysis is highly complex for players and analysts alike and is likely to prove intractable in general. It is therefore useful to identify strategic environments with incomplete information that are rich enough to capture the important role of higher-order beliefs in economic settings, but simple enough to allow tractable analysis. Global games, first studied by Carlsson and van Damme (1993a), represent one such environment. Uncertain economic fundamentals are summarized by a state θ and each player observes a different signal of the state with a small amount of noise. Assuming that the noise technology is common knowledge among the players, each player’s signal generates beliefs about fundamentals, beliefs about other players’ beliefs about fundamentals, and so on. Our purpose in this paper is to describe how such models work, how global game reasoning can be applied to economic problems, and how this analysis relates to more general analysis of higher-order beliefs in strategic settings.

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Hyun Song Shin

Papers

Liquidity and Leverage

Social Value of Public Information

Liquidity and Leverage

The fundamental principles of financial regulation

Global Games: Theory and Applications