Abstract: Firms acquiring durable assets face a lease-or-purchase decision. The collateral channel narrative argues that durability can facilitate (hinder) purchases by enhancing pledgeability (requiring large down payment). Prior research hasn't recognized that some durable assets (e.g. property) can appreciate at a rate that exceeds operational income growth. It also doesn't endogenize a firm's decision to lease assets. We explicitly factor these into a firm's optimal financing and investment decision. A financially constrained firm purchases durable assets expecting to benefit from a profitable resale. If leasing is feasible, it reverts to renting if its down payment becomes burdensome.

New! Pricing the Upside Potential to Downside Risk (with Robert A. Jarrow, Daniel Lebret, Crocker H. Liu)

Abstract: The pandemic exacerbated store closings of anchor tenants whom neighborhood tenants depend on to attract customers. A landlord offers insurance called co-tenancy providing limited time rent relief and premature lease termination. We price this real option. Ex-ante, price should increase with base rent and decrease with rent abatement, lease term, bond price, and default time. Using 236 centers, we find adding a co-tenancy clause in leases increases a center's expected sales price by $5.4 million. Having at least one co-tenancy increases the odds of selling a center more than an asking price by six times.


New! Applying the Local Martingale Theory of Bubbles to Cryptocurrencies (with Robert A. Jarrow), International Journal of Theoretical and Applied Finance, 2022, Vol. 25 No. 03 2250013 1-25.

Abstract: Cryptocurrencies provide a natural setting to test for the existence of price bubbles using the local martingale theory of bubbles because cryptocurrencies have no cash flows. Using a robust statistical algorithm, we test for price bubbles in eight cryptocurrencies, Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Ripple (XRP), Bitcoin Cash (BCH), EOS (EOS), Monero (XMR), and Zcash (ZEC), from 1 January 2019 to 17 July 2019. The statistical test first estimates the cryptocurrencies’ volatilities as a function of the price level. Then, these estimates are extrapolated over the positive real line using power functions. Finally, these power functions underly a sequence of hypothesis tests for price bubbles that control for both Type I and Type II errors. Five of the eight currencies (BTC, BCH, EOS, XMR, ZEC) exhibit price bubbles, LTC does not, and the evidence for ETH and XRP is inconclusive. The paper provides strong evidence for the prevalence of bubbles in cryptocurrencies.