International Journal of Theoretical and Applied Finance

Short sell bans are often imposed during a financial crisis as a desperate measure to stabilize financial markets. Yet, the impact of short sell bans on option pricing and hedging is not well studied, at least quantitatively, until very recently when Guo & Zhu [(2017) Equal risk pricing under convex trading constraints, Journal of Economic Dynamics and Control 76, 136–151] and He & Zhu [(2020) A revised option pricing formula with the underlying being banned from short selling, Quantitative Finance 20 (6), 935–948] formulated a new pricing framework with the underlying being either completely or partially banned from short selling. However, no empirical results were provided to substantiate the usefulness of the formulae, as well as to deepen our understanding on the effects of short sell bans. This paper provides a comprehensive empirical study on the effects of short sell bans to the standard option pricing theory by carrying out both cross-sectional and options time series model calibration of the model devised by He & Zhu (2020) [A revised option pricing formula with the underlying being banned from short selling, Quantitative Finance 20 (6), 935–948]. Overall, our empirical results indicate that the alternative option pricing formula considering short sell restrictions has the ability to capture highly-quoted implied volatility, with an evident improvement of 39% out-of-sample performance compared to the benchmark Black–Scholes model during the period of short sell ban.

This study introduces a stochastic multi-period dividend discount model (DDM) that includes (i) a compound nonhomogenous Poisson process for dividend growth and (ii) the probability of firm default. We obtain maximum likelihood (ML) estimators and confidence interval formulas of our model parameters. We apply the model to a set of firms from the S&P 500 index using historical dividend and price data over a 42-year period. Interestingly, stock price estimations calculated with the model are close to the observable prices. Overall, we prove that the model can be a useful tool for stock pricing.

In this paper, we show that all local martingales with respect to the initially enlarged natural filtration of a vector of multivariate point processes can be weakly represented up to the minimum among the explosion times of the components. We also prove that a strong representation holds if any multivariate point process of the vector has almost surely infinite explosion time and discrete marks space. Then we provide a condition under which the components of the multidimensional local martingale driving the strong representation are pairwise orthogonal.

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.

In this paper, we study optimal mortgage decisions in a cross-currency setting. In particular, we address the question on how a household should optimally split its mortgage portfolio in a fixed rate mortgage in the domestic currency and an adjustable rate mortgage denominated in a foreign currency subject to some risk constraints. We propose an affine factor model which allows to jointly investigate the impact of variations in interest rates as well as of exchange rate fluctuations on mortgage decisions. As a case study, we apply our model to real data on Swiss and German mortgage markets and we estimate parameters using a quasi-Kalman filter approach. We then study the impact of different income splits, risk attitudes, and mortgage specifications on the household’s portfolio choice in a mean–variance optimization approach.