You get what you give: theory and evidence of reciprocity in the sharing economy

We develop an analytical framework of peer interaction in the sharing economy that incorporates reciprocity, the tendency to increase (decrease) effort in response to others’ increased (decreased) effort. In our model, buyers (sellers) can induce sellers (buyers) to exert more effort by behaving well themselves. We demonstrate that this joint increased effort can improve the utility of both parties and influence the market equilibrium. We also show that bilateral reputation systems, which allow both buyers and sellers to review each other, are more responsive to reciprocity than unilateral reputation systems. By rewarding reciprocal behavior, bilateral reputation systems generate trust among strangers and informally regulate their behavior. We test the predictions of our model using data from Airbnb, a popular peer-to-peer accommodation platform. We show that Airbnb hosts that are more reciprocal receive higher ratings and that higher rated hosts can increase their prices. Therefore, reciprocity affects equilibrium prices on Airbnb through its impact on ratings, as predicted by our analytical framework.     

How important is the term structure in implied volatility surface modeling? Evidence from foreign exchange options

We claim that previously proposed parametric specifications that linearly approximate the term structure of the implied volatility surface (IVS) in option prices fail to capture important information regarding the expectations of market participants. This paper proposes a parametric specification for describing the IVS that allows flexible modeling of the term structure through a Nelson and Siegel (1987) factorization, recently proposed by Diebold and Li (2006) in the context of yield curve modeling. The specification is tested on implied volatilities from the over-the-counter foreign exchange options market, where contracts with long expiries are actively traded and thus the term structure dimension of the surface should be very important. We first show that the proposed volatility specification can consistently and remarkably improve our ability to describe the surface on any given day. We then establish the economic relevance of the incremental information captured by our proposed specification by showing that it can produce more accurate forecasts of implied volatility that can support long-term profitable trading strategies in the absence of transaction costs.     

On the relationship between exchange rates and equity returns: A new approach

This paper employs a new approach in order to investigate the underlying relationship between stock markets and exchange rates. Current approaches suggest that the relative equity market performance of two countries is linked to their exchange rate. In contrast, this study proposes an alternative approach where one global variable – global equity market returns – is believed to have an effect on exchange rates, with the relative interest rate level of a currency determining the sign of the relationship. Our empirical findings suggest that exchange rates and global stock market returns are strongly linked. The value of currencies with higher interest rates is positively related with global equity returns, whereas the value of currencies with lower interest rates is negatively related with global equity returns.     

The random-walk behavior of the Euro exchange rate

We use Generalized Andrews–Ploberger (GAP) tests to examine the random-walk behavior of 17 OECD countries’ euro exchange rates at daily frequencies. The GAP tests reject the hypothesis of random-walk behavior less often than do traditional tests. Moreover, the random-walk hypothesis cannot be rejected for the euro’s exchange rate against most of the major currencies. We also use the generalized Box–Pierce tests to produce evidence that corroborates the above findings. Finally, and in contrast to the traditional tests, the GAP tests produce results that are consistent during the great moderation and the recent global financial crisis periods.     

Modelling failure rates with machine-learning models: Evidence from a panel of UK firms

In this study, we investigate the ability of machine-learning techniques to predict firm failures and we compare them against alternatives. Using data on business and financial risks of UK firms over 1994–2019, we document that machine-learning models are systematically more accurate than a discrete hazard benchmark. We conclude that the random forest model outperforms other models in failure prediction. In addition, we show that the improved predictive power of the random forest model relative to its counterparts persists when we consider extreme economic events as well as firm and industry heterogeneity. Finally, we find that financial factors affect failure probabilities.     

Review: Time machines,ethics and sustainable development: accounting for inter-generational equity in public sector organizations

This review paper explores the key challenges associated with effective inter-generational equity accounts in relation to the governance of public sector organizations and sustainable development transformations. Three different approaches to inter-generational equity accounting are evaluated and an outline for future research is provided.     

Structural change in the forward discount: Implications for the forward rate unbiasedness hypothesis

It is a well-accepted empirical result that forward exchange rate unbiasedness is rejected in tests using the “differences regression” of the change in the logarithm of the spot exchange rate on the forward discount. We model the forward discount as an AR(1) process and argue that its persistence is exaggerated due to the presence of structural breaks. We show using a stochastic multiple break model that the forward discount persistence is substantially less if one allows for multiple structural breaks in the mean of the process. We argue that these breaks could be identified as monetary shocks to the central bank's reaction function. Using Monte Carlo simulations, we show that if we do not account for structural breaks that are present in the forward discount process, the forward discount coefficient in the “differences regression” is severely biased downward, away from its true value of 1.     

Predictable dynamics in implied volatility surfaces from OTC currency options

Recent empirical studies report predictable dynamics in the volatility surfaces that are implied by observed index option prices, such as those prescribed by general equilibrium models. Using an extensive data set from the over-the-counter options market, we document similar predictability in the factors that capture the daily variation of surfaces implied by options on 25 different foreign exchange rates. We proceed to demonstrate that simple vector autoregressive specifications for the factors can help produce accurate out-of-sample forecasts of the systematic component of the surface at short horizons. Profitable delta-hedged positions can be set up based on these forecasts; however, profits disappear when typical transaction costs are taken into account and when trading rules on wide segments of the surface are sought.