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.

/pdf/market-liquidity-and-funding-liquidity-h8pfs6l9wc.pdf

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.

/pdf/deciphering-the-liquidity-and-credit-crunch-2007-08-3myt1ozynd.pdf

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

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

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

Econometric policy evaluation: A critique

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

We explore the cross-sectional pricing of volatility risk by decomposing equity market volatility into short- and long-run components. Our finding that prices of risk are negative and significant for both volatility components implies that investors pay for insurance against increases in volatility, even if those increases have little persistence. The short-run component captures market skewness risk, which we interpret as a measure of the tightness of financial constraints. The long-run component relates to business cycle risk. Furthermore, a three-factor pricing model with the market return and the two volatility components compares favorably to benchmark models. WHEN MARKET VOLATILITY IS STOCHASTIC, intertemporal models predict that asset risk premia are not only determined by covariation of returns with the mar ket return, but also by covariation with the state variables that govern market volatility. To study this prediction, we model the log-volatility of the market portfolio as the sum of a short- and a long-run volatility component. This ap proach parsimoniously captures shocks to systematic risk at different horizons. Market volatility is a significant cross-sectional asset pricing factor as shown by Ang et al. (2006).l Their two-factor model with the market return and market volatility reduces pricing errors compared to the capital asset pricing model * Joshua Rosenberg and Tobias Adrian are with the Capital Markets Function of the Research and Statistics Group at the Federal Reserve Bank of New York. We would like to thank Robert Stambaugh (the editor), two anonymous referees, John Campbell, Frank Diebold, Robert Engle, Arturo Estrella, Eric Ghysels, Til Schuermann, Kevin Sheppard, Jiang Wang, and Zhenyu Wang for comments. We also thank seminar participants and discussants at the Federal Reserve Bank of

/pdf/stock-returns-and-volatility-pricing-the-short-run-and-long-2173282qm8.pdf

Stock Returns and Volatility: Pricing the Short-Run and Long-Run Components of Market Risk

The availability of credit varies over the business cycle through shifts in the leverage of financial intermediaries. Empirically, we find that intermediary leverage is negatively aligned with the banks' Value-at-Risk (VaR). Motivated by the evidence, we explore a contracting model that captures the observed features. Under general conditions on the outcome distribution given by Extreme Value Theory (EVT), intermediaries maintain a constant probability of default to shifts in the outcome distribution, implying substantial deleveraging during downturns. For some parameter values, we can solve the model explicitly, thereby endogenizing the VaR threshold probability from the contracting problem.

Procyclical Leverage and Value-at-Risk

The availability of credit varies over the business cycle through shifts in the leverage of financial intermediaries. Empirically, we find that intermediary leverage is negatively aligned with the banks’ value-at-risk (VaR). Motivated by the evidence, we explore a contracting model that captures the observed features. Under general conditions on the outcome distribution given by Extreme Value Theory (EVT), intermediaries maintain a constant probability of default to shifts in the outcome distribution, implying substantial deleveraging during downturns. For some parameter values, we can solve the model explicitly, thereby endogenizing the VaR threshold probability from the contracting problem.

/pdf/procyclical-leverage-and-value-at-risk-3mb3sazczz.pdf

Tobias Adrian

Papers

Liquidity and Leverage

Liquidity and Leverage

Stock Returns and Volatility: Pricing the Short-Run and Long-Run Components of Market Risk

Procyclical Leverage and Value-at-Risk

Procyclical Leverage and Value-at-Risk