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This paper aims to test the finite sample properties of the automatic variance ratio (AVR) test and suggest suitable measure to improve its small sample properties under conditional heteroskedasticity and apply it to test the martingale hypothesis in the stock prices of the Portugal, Ireland, Italy, Greece and Spain (PIIGS economies) markets. This paper also seeks to investigate that “If the time series is not martingale, then what else?”

Monte Carlo experiments have been undertaken to test the small sample properties of automatic variance ratio (AVR) test. The study uses AVR test on daily and weekly data of the indices to investigate their martingale behaviour. It uses detrended fluctuation analysis (DFA) and BDS test statistics to answer, “If not martingale, then what else?”. The study also applies moving subsample approach to examine the dynamic behavior of stock prices and to obtain inferential findings robust to possible structural changes and presence of influential outliers.

The author finds that weighted bootstrap procedure significantly improves the small sample properties of AVR tests under conditional heteroskedasticity. The results provide evidence in support of the weak‐form efficiency of Italy and Spain. But Portugal, Ireland and Greece exhibit signs of long memory in the stock prices. All indices also exhibit chaotic characteristics.

This paper has both methodological and empirical originality. On the methodological aspect, the author proposes weighted bootstrap procedure on AVR test to improve its small sample properties. On the empirical side, the study finds that all stocks exhibit dynamic behavioral characteristics which change over time.

The purpose of this study is to investigate empirically the pattern of co-movement between prices and implied volatility in the future markets for crude oil.

The tool of non-parametric quantile regression is applied to daily price returns and implied volatility changes from 2007 to 2018.

For the total sample period, the link between price returns and forward-looking volatility expectations is contemporaneous, negative and asymmetric, and it exhibits an (approximately) inverted U-shaped pattern suggesting that: the pricing of implied volatility is heavier for large (in absolute value terms) changes relative to small ones and it is lighter for large positive changes relative to large negative ones. The pattern of co-movement, therefore, appears to be in line with the theoretical postulates of fear, exuberance and loss aversion. The main characteristics of the relationship are present in some (but not in all) sub-periods, which are also considered in this study.

Less than a handful of works have assessed the link between implied volatility and prices for commodity ETFs. This is the first one relying on flexible non-parametric quantile regressions.

This study aims to provide insights on the importance of reconfiguring new and existing enterprise resources in a heterogeneous manner. This will lead to improved efficiencies, strategies and resource usage as such leading to more synergetic and innovative outcomes. This study highlights the importance of dynamic capabilities (DC) during the process of resources recombination (RR). It suggests that DC can be a source of competitive advantage, but the effect is contingent on the RR capabilities of enterprises.

Data were obtained from 349 faculty members of higher education institutions (HEIs) from seven states in the United Arab Emirates (UAE). Partial least squares structural equation modeling (PLS-SEM) using SmartPLS was employed as a statistical tool to analyze the structural model.

The findings confirm the proposed role of DC in the realization of RR, in integrating and reconfiguring internal and external organizational skills and resources for efficiency and performance, since DC helps RR to reconfigure the resource base by extending, creating, and modifying innovative RRs.

The study has important implications for resource managers and policymakers of HEIs. By prioritizing DC, firms can develop novel products and services as a result of a heterogenous mix of new RR. Additionally, since firms have limited resources in ever-changing, complex environmental conditions, this study provides explicit directions on how enterprises can strategically manage their resources in an innovative manner to attain a sustainable competitive advantage.

Insights from the DC and RR perspective in HEI sectors, particularly in the Middle East region, are scarce. This is the first empirical study to delve in this area and exemplify the relationship between these significant constructs.

This paper aims to further the knowledge of what effect destination and tourist social media communications have on destination brand equity.

The authors performed a quantitative study with 433 international tourists and social media users using an online survey and structural equation modeling.

The results show that user-generated content (UGC) and destination-generated content (DGC) both positively affect tourist behavior through the mediating role of destination brand equity. Of the two, UGC is more important for building a positive destination image and more valuable for improving perceived destination quality and value. The results also show that affective image is a powerful predictor of tourist behavior.

The findings provide useful insights for destination management organizations (DMOs) and social media marketing strategies. DMOs need to generate content that was highly relatable and evokes emotion, and encourage tourists to share their own experiences to improve destination brand equity and future behavior.

The study was conducted in the passive, pretrip stage before a travel decision is taken, which offers unique insight into how social media communications affect: destination brand equity and users’ decisions to choose certain destinations over others.

本文旨在进一步了解目的地和旅游社交媒体传播对目的地品牌资产的影响。

本论文使用在线调查和结构方程模型对 433 名国际游客和社交媒体用户进行了定量研究。

结果表明, 用户生成的内容（UGC）和目的地生成的内容（DGC）都通过目的地品牌资产的中介作用对游客行为产生积极影响。两者中, UGC对于建立积极的目的地形象更为重要, 对于提高感知的目的地质量和价值更有价值。结果还表明, 情感形象是旅游行为的有力预测因素。

研究结果为目的地管理组织 (DMO) 和社交媒体营销策略提供了有用的见解。 DMO 需要生成具有高度相关性和唤起情感的内容, 并鼓励游客分享自己的经验, 以提高目的地品牌资产和未来行为。

该研究是在做出旅行决定之前的被动旅行前阶段进行的, 它提供了关于社交媒体传播如何影响：(i) 目的地品牌资产, 以及 (ii) 用户的选择决定某些目的地优于其他目的地的独特见解。

The purpose of this paper is to investigate the impact of various employment characteristics on the health of Canadian caregiver-employees (CEs), who are working full-time in the labor market while also providing informal/family care to adults.

Framed with Pearlin et al.’s (1990) stress model and using data from Statistic Canada’s General Social Survey Cycle 26 (2012), several work-related variables for caregivers were considered, including the availability of various forms of caregiver-friendly workplace policies (CFWPs), and a series of work interferences (WIs) experienced as a result of the caregiving role.

This study provides evidence for the value of CFWPs in all workplaces. Counter-intuitively, family and other forms of support were found to negatively relate to both physical and mental health.

This suggests that CFWPs will not only have an impact on CEs’ physical health outcomes, but will likely decrease the effect of the WIs experienced.

The current study proposes an integrative framework for examination of determinants of stock returns in US market based on the five-factor Fama and French (FF) model, macroeconomic variables and investor sentimental factors. The study is based on both value weighted and equally weighted monthly portfolio returns of all CRSP firms which are incorporated in the United States and listed on the NYSE, AMEX or NASDAQ.

The study applies PLS-SEM methodology to examine the major determinants of portfolio return.

The study suggests that investor sentiments are the major driving forces which positively influence the portfolio stock returns. The macroeconomic factors, the FF Factors and Momentum factor have negative influences on portfolio stock returns.

The study is the first of its kind which aim to determine the determinants of portfolio returns using the PLS-SEM methodology.

The purpose of this paper is to develop a more robust methodology for asset allocation for the property investment market which takes into account inherent valuation and data issues.

The methodology applied is that of a bootstrap, borrowed from Carlstein, and is applied to an investment universe consisting of UK equities, gilts and property. The bootstrap selectively re‐samples the return time series by maintaining the economic cycle. The resulting return series is then used in the standard mean‐variance optimisation (MVO) on an unconstrained basis. Finally, a “sanity” test is applied on the correlation matrix to ensure that spurious instances do not skew the results.

The bootstrapped optimisation provides a range within which the portfolio weights can be manoeuvred instead of a static point under the standard MVO. It provides a more robust methodology for asset allocation and without giving any undue significance to one year of extreme result.

The current analysis is based on unconstrained portfolio optimisation, with a very limited investment universe. Additionally, by conforming with the MVO methodology, normality of asset returns is implicitly assumed, which is clearly not the case in the data used. Future work will also focus on an all‐property portfolio.

The proposed methodology will prove to be useful for making asset allocation decisions, particularly in turbulent financial markets.

The paper focuses solely on bootstrapping with the IPD UK annual index and is particularly significant after one year of extremely poor performance of UK property. The results will be of use to fund managers and portfolio analysts.

Much farm financial risk research has involved the application of “credit‐scoring” models. We approach the issue of measuring financial risk by using the actual interest rates charged on agricultural loans reported in the USDA’s ARMS survey as market‐based measures of the financial risk associated with individual farm operations. A simultaneous equations model relates rates to several farm, producer, and lender characteristics. Because individual loans in our sample have different dates of origination, deviations of individual rates from market rates are considered. Our results indicate that risk (as perceived by lenders) tends to be higher for farms with less wealth (net worth) and more loans. Farm operators who live on their operations are considered by lenders to be less risky. Farm diversification appears to be correlated with less financial risk. Significant differences in agricultural lending rates across different types of lenders were also revealed, with the highest rates being charged by commercial banks and savings and loans. A key element of our analysis is development of a probability‐weighted bootstrap estimator that permits consistent inferences to be drawn from the stratified ARMS data.

Turvey (2007, Physica A) introduced a scaled variance ratio procedure for testing the random walk hypothesis (RWH) for financial time series by estimating Hurst coefficients for a fractional Brownian motion model of asset prices. The purpose of this paper is to extend his work by making the estimation procedure robust to heteroskedasticity and by addressing the multiple hypothesis testing problem.

Unbiased, heteroskedasticity consistent, variance ratio estimates are calculated for end of day price data for eight time lags over 12 agricultural commodity futures (front month) and 40 US equities from 2000-2014. A bootstrapped stepdown procedure is used to obtain appropriate statistical confidence for the multiplicity of hypothesis tests. The variance ratio approach is compared against regression-based testing for fractionality.

Failing to account for bias, heteroskedasticity, and multiplicity of testing can lead to large numbers of erroneous rejections of the null hypothesis of efficient markets following an independent random walk. Even with these adjustments, a few futures contracts significantly violate independence for short lags at the 99 percent level, and a number of equities/lags violate independence at the 95 percent level. When testing at the asset level, futures prices are found not to contain fractional properties, while some equities do.

Only a subsample of futures and equities, and only a limited number of lags, are evaluated. It is possible that multiplicity adjustments for larger numbers of tests would result in fewer rejections of independence.

This paper provides empirical evidence that violations of the RWH for financial time series are likely to exist, but are perhaps less common than previously thought.

The purpose of this paper is to be an academic inquiry into rating issues confronted by the US Federal Crop Insurance program stemming from changes in participation rates as well as the weighting of data to reflect longer‐run weather patterns.

The authors investigate two specific approaches that differ from those adopted by the Risk Management Agency, building upon standard maximum likelihood and Bayesian estimation techniques that consider parametric densities for the loss‐cost ratio.

Both approaches indicate that incorporating weights into the priors for Bayesian estimation can inform the distribution.

In most cases, the authors' results indicate that including weighting into priors for Bayesian estimation implied lower premium rates than found using standard methods.