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

Our article comprehensively reexamines the performance of variables that have been suggested by the academic literature to be good predictors of the equity premium. We find that by and large, these models have predicted poorly both in-sample (IS) and out-of-sample (OOS) for 30 years now; these models seem unstable, as diagnosed by their out-of-sample predictions and other statistics; and these models would not have helped an investor with access only to available information to profitably time the market.

/pdf/a-comprehensive-look-at-the-empirical-performance-of-equity-a8mudsid3i.pdf

A Comprehensive Look at the Empirical Performance of Equity Premium Prediction

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

I develop a general equilibrium model with sticky prices, credit constraints, nominal loans and asset prices. Changes in asset prices modify agents’ borrowing capacity through collateral value; changes in nominal prices affect real repayments through debt deflation. Monetary policy shocks move asset and nominal prices in the same direction, and are amplified and propagated over time. The “financial accelerator” is not constant across shocks: nominal debt stabilises supply shocks, making the economy less volatile when the central bank controls the interest rate. I discuss the role of equity, debt indexation and household and firm leverage in the propagation mechanism. Finally, I find that monetary policy should not target asset prices as a means of reducing output and inflation volatility.

/pdf/house-prices-borrowing-constraints-and-monetary-policy-in-53saibtjbe.pdf

House Prices, Borrowing Constraints, and Monetary Policy in the Business Cycle

This paper develops a simple equilibrium model of CEO pay. CEOs have different talents and are matched to firms in a competitive assignment model. In market equilibrium, a CEO’s pay changes one for one with aggregate firm size, while changing much less with the size of his own firm. The model determines the level of CEO pay across firms and over time, offering a benchmark for calibratable corporate finance. The sixfold increase of CEO pay between 1980 and 2003 can be fully attributed to the six-fold increase in market capitalization of large US companies during that period. We find a very small dispersion in CEO talent, which nonetheless justifies large pay differences. The data broadly support the model. The size of large fi rms explains many of the patterns in CEO pay, across firms, over time, and between countries. (JEL D2, D3, G34, J3)

/pdf/why-has-ceo-pay-increased-so-much-4wfm2lyooz.pdf

Why has CEO Pay Increased So Much

Many argue that home bias arises because home investors can predict home asset payoffs more accurately than foreigners can. But why does global information access not eliminate this asymmetry? We model investors, endowed with a small home information advantage, who choose what information to learn before they invest. Surprisingly, even when home investors can learn what foreigners know, they choose not to: Investors profit more from knowing information others do not know. Learning amplifies information asymmetry. The model matches patterns of local and industry bias, foreign investments, portfolio outperformance, and asset prices. Finally, we propose new avenues for empirical research. (This abstract was borrowed from another version of this item.)

Information Immobility and the Home Bias Puzzle

Many argue that home bias arises because home investors can predict home asset payoffs more accurately than foreigners can. But why does global information access not eliminate this asymmetry? We model investors, endowed with a small home information advantage, who choose what information to learn before they invest. Surprisingly, even when home investors can learn what foreigners know, they choose not to: Investors profit more from knowing information others do not know. Learning amplifies information asymmetry. The model matches patterns of local and industry bias, foreign investments, portfolio outperformance, and asset prices. Finally, we propose new avenues for empirical research.

/pdf/information-immobility-and-the-home-bias-puzzle-52zfx9d7nl.pdf

Evidence of stock-return predictability by financial ratios is still controversial, as docu- mented by inconsistent results for in-sample and out-of-sample regressions and by substan- tial parameter instability. This article shows that these seemingly incompatible results can be reconciled if the assumption of a fixed steady state mean of the economy is relaxed. We find strong empirical evidence in support of shifts in the steady state and propose simple methods to adjust financial ratios for such shifts. The in-sample forecasting relationship of adjusted price ratios and future returns is statistically significant and stable over time. In real time, however, changes in the steady state make the in-sample return forecastability hard to exploit out-of-sample. The uncertainty of estimating the size of steady-state shifts rather than the estimation of their dates is responsible for the difficulty of forecasting stock returns in real time. Our conclusions hold for a variety of financial ratios and are robust to changes in the econometric technique used to estimate shifts in the steady state. (JEL 12, 14)

/pdf/reconciling-the-return-predictability-evidence-2e8v0y9bkl.pdf

Reconciling the Return Predictability Evidence

In a model with housing collateral, the ratio of housing wealth to human wealth shifts the conditional distribution of asset prices and consumption growth. A decrease in house prices reduces the collateral value of housing, increases household exposure to idiosyncratic risk, and increases the conditional market price of risk. Using aggregate data for the US, we find that a decrease in the ratio of housing wealth to human wealth predicts higher returns on stocks. Conditional on this ratio, the covariance of returns with aggregate risk factors explains eighty percent of the cross-sectional variation in annual size and book-to-market portfolio returns.

https://papers.ssrn.com/sol3/Delivery.cfm/2451_27320.pdf?abstractid=1300215&mirid=1

Housing Collateral, Consumption Insurance and Risk Premia: An Empirical Perspective

In a model with housing collateral, the ratio of housing wealth to human wealth shifts the conditional distribution of asset prices and consumption growth. A decrease in house prices reduces the collateral value of housing, increases household exposure to idiosyncratic risk, and increases the conditional market price of risk. Using aggregate data for the United States, we find that a decrease in the ratio of housing wealth to human wealth predicts higher returns on stocks. Conditional on this ratio, the covariance of returns with aggregate risk factors explains 80% of the cross-sectional variation in annual size and book-to-market portfolio returns. HOUSE PRICE FLUCTUATIONS PLAY AN IMPORTANT ROLE in explaining the time-series and the cross-sectional variation in asset returns. Given the magnitude of the housing market, this is unsurprising. This paper shows that the way in which housing affects asset returns is through the role of housing as a collateral asset. We identify a novel collateral channel that transmits shocks in the housing market to risk premia. In a model with collateralized borrowing and lending, the ratio of housing wealth to human wealth, the housing collateral ratio, changes the conditional distribution of consumption growth across households. When the collateral ratio is low, the dispersion of consumption growth across households is more sensitive to aggregate consumption growth shocks, and this raises the market price of aggregate risk.

/pdf/housing-collateral-consumption-insurance-and-risk-premia-an-1oplpa6h2o.pdf

Stijn Van Nieuwerburgh

Papers

Information Immobility and the Home Bias Puzzle

Information Immobility and the Home Bias Puzzle

Reconciling the Return Predictability Evidence

Housing Collateral, Consumption Insurance and Risk Premia: An Empirical Perspective

Housing Collateral, Consumption Insurance, and Risk Premia: An Empirical Perspective