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Value Added Intellectual Coefficient (VAIC) method has become the most efficient method for measuring intellectual capital (IC). However, there are inconsistencies in the mechanism of adopting the VAIC method to measure IC, thus questioning the robustness of established relationships between IC and firm performance. Therefore, this study attempts to examine the diversity in estimates of the impact of IC on firm performance as a result of adopting the VAIC method in a diverse manner. The study also proposes a modification to the basic VAIC method to eliminate its key limitation. Findings of the study indicate that there are differences in the estimates of the impact of IC on firm performance as a result of varied VAIC adoptions. The proposed modification to the basic VAIC method incorporates theoretically agreed value compositions of IC (human capital, structural capital and relational capital) replacing traditional value compositions (human capital and structural capital) in the basic VAIC method.

This study attempts to contribute to the literature on risk exposures by investigating the dynamic volatility spillover transmissions and volatility co-movements between oil-risk factors and sectoral stocks in BRICS countries. A spillover index and DCC-GARCH estimation techniques are applied to identify the volatility transmission mechanism and co-movement among the series using daily data from 5th May 2007 to December 31st, 2016. To provide practical implications of the volatility transmissions, the estimated results are in turn used to compute and analyze the optimal weights and hedge ratios for oil-stock portfolio holdings. Our findings indicate the existence of significant volatility spillover interdependences and a time-varying volatility co-movement between oil-risk factors and sectoral stocks. However, the direction of spillover is shown to be somewhat unidirectional mainly from some selected sectors to oil-risk factors. Thus, we show that although volatility spillovers from oil-risk factors to sectors exist, the effect is at best, marginal. Finally, the optimal weights and hedge ratios show that oil-risk factors would be better suited as instruments for portfolio diversification rather than hedging to minimize oil price and portfolio risks which is important for risk management and diversification benefits.

Objective - The vital role of entrepreneurial orientation (EO) on firm performance has been widely studied in developed countries. Most studies have found a positive linear relationship between EO and firm performance. However, the empirical findings are inconclusive. According to the recent literature, one possible reason for these inconsistent findings may be the nature of the relationship between EO and firm performance is not linear as assumed by many researchers. The objective of this study is to examine the relationship between EO and firm performance and specially to determine the best representation is whether linear or curvilinear. Methodology/Technique - This study utilizes a sample of 580 small medium enterprises (SME's) from two provinces in Sri Lanka and hierarchical regression analyses was employed for analyzing the relationships. Findings - Findings demonstrate anS-shape curvilinear relationship between EO and firm performance in the context of SME's in Sri Lanka. Type of Paper - Empirical Paper Keywords: Entrepreneurial Orientation; Firm Performance; Small Medium Enterprises; Curvilinear Relationship.

This chapter studies the performance of Asian mutual funds in Singapore, Malaysia, Hong Kong, and the Philippines using absolute and relative performance metrics in comparison with benchmark indices over time and also during calm and crises periods. The chapter further explores the factors that explain their performance. In the full sample analysis, we find that Asian funds generally under perform their benchmarks and adjusting for risk factors even makes their performance worse. However, the subsample analysis shows that fund performance based on alpha values improves over time and the performance in the post-crisis period are generally much better than the pre-crisis and crisis periods although it remains negative for most of the funds. Exclusively, Hong Kong funds outperform the benchmark during the post-crisis period.

The bitter experience of the subprime crisis of 2007, the Global Financial crisis of 2008, and the extremely slow and painful ensuing recovery, has raised systemic risk to the center stage of global economic discourses. The crisis has brought home the urgent need for a thorough assessment of the dependence and interaction between banking sectors, from which most of the trouble began. This study investigates patterns and trends in absolute and tail dependence over time using daily returns for banking sectors from 12 Asian economies during the period 2000-2012. Static and time-varying Copula models, Gaussian copula, Symmetrized Joe-Clayton copulas, are employed to study the tail co-movements among the selected markets. The paper assumes a skew-t distribution for the innovation process of the marginal models. The results of the marginal models suggest strong volatility persistence in all twelve markets. There is high persistence in the absolute dependence of among market pairs. The evidence from the empirical analysis suggests that the cross-sectional average copula correlations generally remain at moderate levels with slight upward trend for all twelve markets. Correlation among the banking sectors of the advanced Asian markets economies are generally higher compared with the Emerging markets economies. The tail dependence is asymmetric across most of the market pairs; tail dependence at the lower side of the joint distributions is mostly higher than tail dependence at the upper side of the joint distributions. The results show that tail dependence is not upward trending for most of the pairs examined. However, the tail co-movements show significant spikes in response to financial stress in the global economy, which implies that there could be joint crashes in the regional banking system during extreme negative events. The fact that the region has not been the epicenter for most of the crisis periods covered in this study, yet responds significantly, makes it necessary for adoption of policies that maximize resilience to shocks. The study concludes that time-varying copulas are best suited for modeling the dependence structure of the Asian banking sector indices compared with static copula.

This study examines the value relevance of intellectual capital (IC) to investors. In this respect, associations of IC with contemporaneous market valuation, stock returns, excess returns and share prices are initially tested. In addition, associations for current IC and future data of the above stock performance indicators are estimated to investigate whether the selected performance indicators have delayed response to IC. Pulic’s VAIC TM method is selected to measure IC and fixed effects panel regressions are used to analyse data of 191 NYSE listed banking sector firms over 2000-2011. Results of contemporaneous analysis fail to establish conclusive associations between IC and selected stock performance indicators. Analyses to test the delayed reactions evident that contemporaneous IC relates significantly negatively with market valuation and stock returns, and significantly positively with excess returns and stock prices. This suggests that investors perceive IC as firm-specific risk related investments which account for uncertain future benefits. Evidence for investor relevance based on adopted stock performance indicators in this study, and delayed reaction analysis add new attributes to extant IC performance literature.

Several recent articles report evidence of predictability in the skewness of equity returns, raising hopes that predictability in third moments will be useful for forecasting the probability of tail events. The evidence is unfortunately difficult to interpret, partly because they were obtained mainly from parametric models of time-varying conditional skewness, and because little is known about the behavior of such models, for instance, when there are outliers. We investigate a non-parametric approach to testing for predictability in skewness. Specifically, we explore the size and power of a Runs tests, and compare this approach with other tests. A re-examination of daily market returns reveals mild evidence of predictability in skewness. Incorporating this conditional heteroskewness into standard volatility models hardly improves out-of-sample forecasts of tail probabilities.

The standard root-b1 test is widely used for testing skewness. However, several studies have demonstrated that this test is not reliable for discriminating between symmetric and asymmetric distributions in the presence of excess kurtosis. The main reason for the failure of the standard root-b1 test is that its variance formula is derived under the assumption of no excess kurtosis. In this paper we theoretically derive adjustment to the root-b1 test under the framework of Roa's Score (or the Lagrange multiplier) test principle. Our adjusted test automatically corrects the variance formula and does not lead to over- or under-rejection of the correct null hypothesis. In a similar way, we also suggest an adjusted test for kurtosis in the presence of asymmetry. These tests are then applied to both simulated and real data. The finite sample performances of the adjusted tests are far superior compared to those of their unadjusted counterparts.

Most of the tests for asymmetry are developed under the null hypothesis of normal distribution. As is well known, many financial data exhibits fat tail, and commonly used tests (such as the standard square root test based on sample skewness) are not valid for leptokurtic financial data. Also, the square root test uses the third moment, which may not be robust in the presence of gross outliers. In this paper, we propose a simple parametric test for symmetry based on the Pearson type IV family of distributions, which take account of leptokurtosis explicitly. Our test is based on a function that bounded over the real line, and we expect it to be more well behaved than the test based on sample skewness (third moment). Results from our Monte Carlo study reveal that the suggested test performs quite well in finite samples, and it is robust to excess kurtosis. We also apply the test to stock return data to illustrate its usefulness.

This paper develops a flexible parametric approach to capture asymmetry and excess kurtosis along with conditional heteroskedasticity with a general family of distributions for analyzing stock returns data. Engle's (1982) autoregressive conditional heteroskedastic (ARCH) model and its various generalizations can account for many of the stylized facts, such as fat tails and volatility clustering. However, in many applications, it has been found that the conditional normal or Student's t ARCH process is not sufficiently heavy-tailed to account for the excess kurtosis in the data. Moreover, asymmetry in financial data is rarely modeled systematically. Therefore, there is a real need to find an asymmetric density that can be easily estimated and whose tails are heavier than the Student's t-distribution. Pearson type IV density is such a distribution, and it is much easier to handle than those that have been used in the literature, such as non-central t and Gram-Charlier distributions, to account for skewness and excess kurtosis simultaneously. Pearson type IV distribution has three parameters that can be interpreted as variance, skewness and kurtosis; and they can also be considered as different components of the risk premium. Modeling simultaneously time-varying behavior of mean, variance, skewness and kurtosis produces a better explanation of risk than mean-variance analysis only. These methodologies can also be used to analyze other financial data such as exchange rates, interest rates and spot and future prices.