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.

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

Abstract: Shopping centers represent a rare example wherein prices reflect the internalization of externalities. The relatively lower rent anchors pay which other tenants subsidize proxies for externalities anchors create. A related proxy we theoretically model and empirically analyze are co-tenancy lease provisions triggered when an anchor leaves. This real option provides temporary rent relief and early lease termination. We show this option price increases (decreases) with base rent (rent abatement, lease term, bond price, and default time). Using 236 centers, we find co-tenancy increases a center’s expected sales price and the odds of selling it for more than its offering price.


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.