The Journal of Finance

Stock Market Liquidity and the Business Cycle

Published: 1/6/2011,  Volume: 66,  Issue: 1  |  DOI: 10.1111/j.1540-6261.2010.01628.x  |  Cited by: 329

RANDI NÆS, JOHANNES A. SKJELTORP, BERNT ARNE ØDEGAARD

In the recent financial crisis we saw liquidity in the stock market drying up as a precursor to the crisis in the real economy. We show that such effects are not new; in fact, we find a strong relation between stock market liquidity and the business cycle. We also show that investors' portfolio compositions change with the business cycle and that investor participation is related to market liquidity. This suggests that systematic liquidity variation is related to a “flight to quality” during economic downturns. Overall, our results provide a new explanation for the observed commonality in liquidity.

The Statistical and Economic Role of Jumps in Continuous‐Time Interest Rate Models

Published: 2/2004,  Volume: 59,  Issue: 1  |  DOI: 10.1111/j.1540-6321.2004.00632.x  |  Cited by: 380

Michael Johannes

This paper analyzes the role of jumps in continuous‐time short rate models. I first develop a test to detect jump‐induced misspecification and, using Treasury bill rates, find evidence for the presence of jumps. Second, I specify and estimate a nonparametric jump‐diffusion model. Results indicate that jumps play an important statistical role. Estimates of jump times and sizes indicate that unexpected news about the macroeconomy generates the jumps. Finally, I investigate the pricing implications of jumps. Jumps generally have a minor impact on yields, but they are important for pricing interest rate options.

Asymmetric Information about Collateral Values

Published: 5/11/2016,  Volume: 71,  Issue: 3  |  DOI: 10.1111/jofi.12288  |  Cited by: 116

JOHANNES STROEBEL

I empirically analyze credit market outcomes when competing lenders are differentially informed about the expected return from making a loan. I study the residential mortgage market, where property developers often cooperate with vertically integrated mortgage lenders to offer financing to buyers of new homes. I show that these integrated lenders have superior information about the construction quality of individual homes and exploit this information to lend against higher quality collateral, decreasing foreclosures by up to 40%. To compensate for this adverse selection on collateral quality, nonintegrated lenders charge higher interest rates when competing against a better‐informed integrated lender.

The Impact of Collateralization on Swap Rates

Published: 1/11/2007,  Volume: 62,  Issue: 1  |  DOI: 10.1111/j.1540-6261.2007.01210.x  |  Cited by: 83

MICHAEL JOHANNES, SURESH SUNDARESAN

Interest rate swap pricing theory traditionally views swaps as a portfolio of forward contracts with net swap payments discounted at LIBOR rates. In practice, the use of marking‐to‐market and collateralization questions this view as they introduce intermediate cash flows and alter credit characteristics. We provide a swap valuation theory under marking‐to‐market and costly collateral and examine the theory's empirical implications. We find evidence consistent with costly collateral using two different approaches; the first uses single‐factor models and Eurodollar futures prices, and the second uses a formal term structure model and Treasury/swap data.

International Taxation and the Direction and Volume of Cross‐Border M&As

Published: 5/20/2009,  Volume: 64,  Issue: 3  |  DOI: 10.1111/j.1540-6261.2009.01463.x  |  Cited by: 179

HARRY P. HUIZINGA, JOHANNES VOGET

We show that the parent‐subsidiary structure of multinational firms created by cross‐border mergers and acquisitions is affected by the prospect of international double taxation. Specifically, the likelihood of parent firm location in a country following a cross‐border takeover is reduced by high international double taxation of foreign‐source income. At the same time, countries with high international double taxation attract smaller numbers of parent firms. A unilateral elimination of worldwide taxation by the United States is simulated to increase the proportion of parent firms locating in the United States following cross‐border mergers and acquisitions from 53% to 58%.

Model Specification and Risk Premia: Evidence from Futures Options

Published: 5/8/2007,  Volume: 62,  Issue: 3  |  DOI: 10.1111/j.1540-6261.2007.01241.x  |  Cited by: 584

MARK BROADIE, MIKHAIL CHERNOV, MICHAEL JOHANNES

This paper examines model specification issues and estimates diffusive and jump risk premia using S&P futures option prices from 1987 to 2003. We first develop a time series test to detect the presence of jumps in volatility, and find strong evidence in support of their presence. Next, using the cross section of option prices, we find strong evidence for jumps in prices and modest evidence for jumps in volatility based on model fit. The evidence points toward economically and statistically significant jump risk premia, which are important for understanding option returns.

Sequential Learning, Predictability, and Optimal Portfolio Returns

Published: 3/17/2014,  Volume: 69,  Issue: 2  |  DOI: 10.1111/jofi.12121  |  Cited by: 185

MICHAEL JOHANNES, ARTHUR KORTEWEG, NICHOLAS POLSON

This paper finds statistically and economically significant out‐of‐sample portfolio benefits for an investor who uses models of return predictability when forming optimal portfolios. Investors must account for estimation risk, and incorporate an ensemble of important features, including time‐varying volatility, and time‐varying expected returns driven by payout yield measures that include share repurchase and issuance. Prior research documents a lack of benefits to return predictability, and our results suggest that this is largely due to omitting time‐varying volatility and estimation risk. We also document the sequential process of investors learning about parameters, state variables, and models as new data arrive.

The Impact of Jumps in Volatility and Returns

Published: 5/6/2003,  Volume: 58,  Issue: 3  |  DOI: 10.1111/1540-6261.00566  |  Cited by: 1260

Bjørn Eraker, Michael Johannes, Nicholas Polson

AbstractThis paper examines continuous‐time stochastic volatility models incorporating jumps in returns and volatility. We develop a likelihood‐based estimation strategy and provide estimates of parameters, spot volatility, jump times, and jump sizes using S&P 500 and Nasdaq 100 index returns. Estimates of jump times, jump sizes, and volatility are particularly useful for identifying the effects of these factors during periods of market stress, such as those in 1987, 1997, and 1998. Using formal and informal diagnostics, we find strong evidence for jumps in volatility and jumps in returns. Finally, we study how these factors and estimation risk impact option pricing.

Learning about Consumption Dynamics

Published: 3/18/2016,  Volume: 71,  Issue: 2  |  DOI: 10.1111/jofi.12246  |  Cited by: 100

MICHAEL JOHANNES, LARS A. LOCHSTOER, YIQUN MOU

This paper characterizes U.S. consumption dynamics from the perspective of a Bayesian agent who does not know the underlying model structure but learns over time from macroeconomic data. Realistic, high‐dimensional macroeconomic learning problems, which entail parameter, model, and state learning, generate substantially different subjective beliefs about consumption dynamics compared to the standard, full‐information rational expectations benchmark. Beliefs about long‐run dynamics are volatile, with counter‐cyclical conditional volatility, and drift over time. Embedding these beliefs in a standard asset pricing model significantly improves the model's ability to match the stylized facts, as well as the sample path of the market price‐dividend ratio.

Lender Automation and Racial Disparities in Credit Access

Published: 1/25/2024,  Volume: 79,  Issue: 2  |  DOI: 10.1111/jofi.13303  |  Cited by: 65

SABRINA T. HOWELL, THERESA KUCHLER, DAVID SNITKOF, JOHANNES STROEBEL, JUN WONG

Process automation reduces racial disparities in credit access by enabling smaller loans, broadening banks' geographic reach, and removing human biases from decision making. We document these findings in the context of the Paycheck Protection Program (PPP), where private lenders faced no credit risk but decided which firms to serve. Black‐owned firms obtained PPP loans primarily from automated fintech lenders, especially in areas with high racial animus. After traditional banks automated their loan processing procedures, their PPP lending to Black‐owned firms increased. Our findings cannot be fully explained by racial differences in loan application behaviors, preexisting banking relationships, firm performance, or fraud rates.