Abstract: Firms acquiring durable assets face a decision to lease or purchase them. Under the collateral channel narrative, the extant literature argues that durability can either facilitate purchases by enhancing pledgeability or hinder them due to the large down payment required. Prior research has not considered that some durable assets such as real estate can persistently increase in price, and it does not endogenize a firm's decision to lease the assets. This paper explicitly factors this capital gains possibility and leasing option 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 as its down payment burden becomes substantial.

Abstract: The pandemic exacerbated store closings of anchor tenants. Anchor tenants attract customers to a center allowing smaller stores to benefit from spillover traffic. To provide rent relief from sales declines if an anchor tenant departs, tenants want a co-tenancy clause in their lease. Using arbitrage-pricing theory and reduced-form credit risk, we derive a price for this co-tenancy option. Ex-ante, the option price should increase with base rent and decrease with rent abatement, lease term, bond price, and default time. Using data from 238 centers, we find that an additional co-tenant (reference tenant) increases (decreases) the center's sold-list price ratio by 36.62% (16.39%)


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.