Semistatic hedging and pricing American floating strike lookback options

We price an American floating strike lookback option under the Black–Scholes model with a hypothetic static hedging portfolio (HSHP) composed of nontradable European options. Our approach is more efficient than the tree methods because recalculating the option prices is much quicker. Applying put–call duality to an HSHP yields a tradable semistatic hedging portfolio (SSHP). Numerical results indicate that an SSHP has better hedging performance than a delta-hedged portfolio. Finally, we investigate the model risk for SSHP under a stochastic volatility assumption and find that the model risk is related to the correlation between asset price and volatility.     

互联网下高校外卖物流配送的现状与发展

随着互联网的快速发展,我国的手机愈发成为一部掌上电脑,通过手机进行外卖订餐已经成为现代高校大学生的日常状态,外卖的物流配送大大改变了学生的生活饮食方式。但是高校周围的外卖物流配送也容易导致很多问题,影响大学生的生活。针对高校外卖的特殊性,文章以互联网下逐渐形成的O2O模式来研究高校外卖物流配送的现状与发展,并且针对存在的问题提出相关建议。     

A truly global crisis? Evidence from contagion dependence across international REIT markets

This study examines the global nature of the recent crisis under bivariate Markov-switching models for pre- and post-crisis periods using the breakpoint of August 9, 2007. It quantifies international synchronization of boom-bust regime switches to investigate contagion-type dynamic comovements of Real Estate Investment Trusts (REITs). Global REIT markets display persistent bust regimes from September 2008 to May 2009, whereas the regime-switching patterns are not significant in the pre-crisis period. The results provide new evidence for global REIT contagion phenomena and suggest greater difficulties in diversifying risks across global REIT markets during the post-crisis period.     

A Statistical Model to Investigate the Reproducibility Rate Based on Replication Experiments

The reproducibility crisis, that is, the fact that many scientific results are difficult to replicate, pointing to their unreliability or falsehood, is a hot topic in the recent scientific literature, and statistical methodologies, testing procedures and p‐values, in particular, are at the centre of the debate. Assessment of the extent of the problem–the reproducibility rate or the false discovery rate–and the role of contributing factors are still an open problem. Replication experiments, that is, systematic replications of existing results, may offer relevant information on these issues. We propose a statistical model to deal with such information, in particular to estimate the reproducibility rate and the effect of some study characteristics on its reliability. We analyse data from a recent replication experiment in psychology finding a reproducibility rate broadly coherent with other assessments from the same experiment. Our results also confirm the expected role of some contributing factor (unexpectedness of the result and room for bias) while they suggest that the similarity between original study and the replica is not so relevant, thus mitigating some criticism directed to replication experiments.     

EMPIRICAL PERFORMANCE OF GARCH MODELS WITH HEAVY‐TAILED INNOVATIONS

We introduce a new type of heavy‐tailed distribution, the normal reciprocal inverse Gaussian distribution (NRIG), to the GARCH and Glosten‐Jagannathan‐Runkle (1993) GARCH models, and compare its empirical performance with two other popular types of heavy‐tailed distribution, the Student's t distribution and the normal inverse Gaussian distribution (NIG), using a variety of asset return series. Our results illustrate that there is no overwhelmingly dominant distribution in fitting the data under the GARCH framework, although the NRIG distribution performs slightly better than the other two types of distribution. For market indexes series, it is important to introduce both GJR‐terms and the NRIG distribution to improve the models’ performance, but it is ambiguous for individual stock prices series. Our results also show the GJR‐GARCH NRIG model has practical advantages in quantitative risk management. Finally, the convergence of numerical solutions in maximum‐likelihood estimation of GARCH and GJR‐GARCH models with the three types of heavy‐tailed distribution is investigated.     

Covariance breakdowns and connectedness of crude oil futures markets with non-synchronous data

ABSTRACT

This article identifies the breakdowns in the covariance of three benchmark crude oil futures markets (WTI, Brent and Dubai) and investigates the changes of market connectedness across the breakdown periods. As the crude oil futures are traded in different regions, this article eliminates the non-synchronous trading data by employing the Vector Moving Average structure and the Bayesian data augmentation approach, which keeps the integrity of original data without changing its properties. The results show that there are significant breaks in the covariance structure of crude oil futures markets. The breakdown periods are consistent with the periods when the market volatilities are at high level and the returns are volatile. The changes of market connectedness are independent of the covariance states, which supports the globalization hypothesis for the crude oil market. The results also suggest that there is more information flow out of the WTI than to the WTI during the sample period, particularly during the breakdown periods in 2008–2009.     

Cost comparison of continued anticoagulation with rivaroxaban versus placebo based on the 1-year EINSTEIN-Extension trial efficacy and safety results

Aims: The EINSTEIN-Extension trial (EINSTEIN-EXT) found that continued treatment with rivaroxaban for an additional 6 or 12 months (vs placebo) after 6–12 months of initial anticoagulation significantly reduced the risk of recurrent venous thromboembolism (VTE) with a small non-significant increased risk of major bleeding (none fatal or in critical site). This study aimed to compare total healthcare cost between rivaroxaban and placebo, based on the EINSTEIN-EXT event rates.

Methods: Total healthcare cost was calculated as the sum of treatment and clinical event costs from a US managed care perspective. Treatment duration and event rates were obtained from the EINSTEIN-EXT study. Adjustment on treatment duration was made by assuming a 10% non-adherence rate. Drug costs were based on wholesale acquisition costs. Cost estimates for clinical events (i.e. recurrent deep vein thrombosis [DVT], recurrent pulmonary embolism, major bleeding, clinically relevant non-major bleeding) were determined from the literature. Results were examined over a ±20% range of each cost component and over 95% confidence intervals (CIs) of event rate differences in deterministic (one-way) and probabilistic sensitivity analyses (PSA).

Results: Total healthcare cost was $1,454 lower for rivaroxaban-treated (vs placebo-treated) patients in the base-case, with a lower clinical event cost fully offsetting drug cost. The cost savings of recurrent DVT alone (–$3,102) was greater than drug cost ($2,723). Total healthcare cost remained lower for rivaroxaban in the majority (73%) of PSA (cost difference [95% CI]?=?–$1,454 [–$2,396, $1,231]).

Limitations: This study was conducted over the 1-year observation period of the EINSTEIN-EXT trial, which limited “real-world” applicability and examination of long-term economic impact. Assumptions on drug and clinical event costs were US-based and, thus, not applicable to other healthcare systems.

Conclusions: Total healthcare costs were estimated to be lower for patients continuing rivaroxaban therapy compared to those receiving placebo in VTE patients who had completed 6–12 months of VTE treatment.     

The impact of influencers' multi-SNS use on followers’ behavioral intentions: An integration of cue consistency theory and social identity theory

The considerable marketing potential brought by the rapid growth of social media has been extensively examined in the literature, particularly in terms of the impact of influencers' recommendations on their followers' decisions. However, little attention has been given to how the individual influencers' use of multiple social networking sites affects their followers' behavioral intentions. Hence, this academic gap warrants further exploration. Drawing on cue consistency theory, social identity theory, and stimulus-organism-response framework, this research proposes three research questions and constructs an integrated research model to explore how the influencers' cue consistency affects the social identification of their followers, subsequently influencing their behavioral intentions. A structural equation modeling technique is performed to analyze the data of 510 valid respondents. The findings show that influencers' consistent cues (i.e., information and image consistency) across social media have positive impacts on followers' cognitive and affective identification toward the influencers. Furthermore, the determinants for followers' purchase intention and electronic word-of-mouth intention are affirmed to serve as both cognitive and affective identification. Especially, the results indicate that the relationships between followers’ social identifications and behavioral intentions are moderated by the type of influencer (i.e., lifestyle vs. review). Accordingly, the results indicate how the use of multiple social networking sites by influencers impacts the behavioral intentions of their followers. The findings offer new insights into influencer marketing and provide important lessons for marketers.     

Evaluating discounts as a dimension of customer behavior analysis

Today, increased competition between organizations has led them to seek a better understanding of customer behavior through identifying valuable customers. Customers’ expectations about the price and quality of products and services play an important role in their selection process. In online businesses, competition and price differences between suppliers is high, so discounts will attract different customers. As a result, discounts and the frequency and amount of purchases can lead to better understanding of customer behavior. Customer segmentation and analysis is essential for identifying groups of customers. Hence, this study uses a model based on RFM called RdFdMd, in which d is the level of discount used to analyze customer purchase behavior and the importance of discounts on customers’ purchasing behavior and organizational profitability. The CRISP-DM and k-mean algorithm were used for clustering. The results indicate that using the R_dF_dM_d model achieves better customer clustering and valuation, and discounts were identified as an important criterion for customer purchases.     

Plausible versus implausible tensile price claim: Selective accessibility model approach

Retailers frequently use exaggerated price discount advertisements with a tensile price claim (TPC; e.g., “Save up to 70%”) to attract consumers because they expect that once consumers enter a store, they will purchase low‐ or medium‐discounted products. Drawing on the selective accessibility model, this study investigated the way in which an implausibly high maximum level of savings stated in a TPC influences consumers’ expected price discount (EPD) and perceptions of actual price discounts across different types of TPCs (i.e., TPC stating a maximum level and TPC stating a range of savings). This study also investigated two situations in which consumers have previous knowledge of a product’s price discount versus when they have less or no knowledge of the discount. For both conditions, a single‐anchor TPC (i.e., “Save up to Y%”) that stated an implausible maximum level of savings led to a higher EPD and lower perceptions of the deal (i.e., perceived savings, price fairness, and perceived value) with respect to the actual price discount than did a TPC with a plausible maximum level of savings. In contrast, when the TPC stated two anchors (i.e., “Save X–Y%”) and consumers had knowledge of the price discount, their EPDs assimilated only toward the plausible anchor (X), and ignored the implausibly high maximum price discount (Y), resulting in a lower EPD and higher perceptions of the deal of the actual price discounts than a TPC that stated a plausibly high maximum level of savings. In contrast, when consumers had no knowledge of the price discount, their EPDs only adjusted toward the more plausible anchor (X), regardless of whether they perceived the maximum anchor as plausible or implausible. Thus, there was no difference in consumers’ perceptions of “Save X–Y%” between implausibly and plausibly high Y%.