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In any stock market, volatility is a significant factor in strengthening their asset pricing. The upsurge in volatility in the stock market can activate and bring changes in the financial risk. According to financial conventional theory, the stakeholders (investors) are selected to be balanced and variations in pertinent risk are also to be anticipated due to the outcome of the drive-in basic factors in Indian stock markets. The hypothesis shows that there are actions in systematic and unsystematic risks that are determined by volatility. It is allied to sentiment-driven in the trader movement.

The paper used the methodology of generalized autoregressive conditional heteroskedasticity-in mean GARCH-M and exponential GARCH-M (E-GARCH-M) methods on the Indian stock market. The data have been covered from 2000 to 2019.

Finally, the study suggests that due to the unfitness of the capital asset pricing model (CAPM), the selection has enhanced with sentiment is an important risk factor.

The investor sentiment and stock return volatility statement are established by using the investor sentiment amalgamated stock market index built.

The outcome of the study shows that there is an important association between stakeholder (investor) sentiment and stock return, in case of volatility behavioural finance can significantly explain the behaviour of stock returns on the Indian Stock Exchange.

Extensive studies have investigated the relation between risk and return in the stock and major asset markets, whereas little studies have been done for housing, particularly the Australian housing market. This study aims to determine the relationship between housing risk and housing return in Australia.

The analysis of this study involves two stages. The first stage is to estimate the presence of volatility clustering effects. Thereafter, the relation between risk and return in the Australian housing market is assessed by using a component generalised autoregressive conditional heteroscedasticity-in-mean (C-CARCH-M) model.

The empirical results show that there is a strong positive risk-return relationship in all Australian housing markets. Specifically, comparable results are also evident in all housing markets in various Australian capital cities, reflecting that Australian home buyers, in general, are risk reverse and require a premium for higher risk level. This could be attributed the unique characteristics of the Australian housing market. In addition, there is evidence to suggest that a stronger volatility clustering effect than previously documented in the daily case.

The findings enable more informed and practical investment decision-making regarding the relation between housing return and housing risk.

This paper is the first study to offer empirical evidence of the risk-return relationship in the Australian housing market. Besides, this is the first housing price volatility study that utilizes daily data.

Study conditions of empirical publications on time series modeling and forecasting of electricity prices vary widely, making it difficult to generalize results. The key purpose of the present study is to offer a comparison of different model types and modeling conditions regarding their forecasting performance.

The authors analyze the forecasting performance of AR (autoregressive), MA (moving average), ARMA (autoregressive moving average) and GARCH (generalized autoregressive moving average) models with and without the explanatory variables, that is, power consumption and power generation from wind and solar. Additionally, the authors vary the detailed model specifications (choice of lag-terms) and transformations (using differenced time series or log-prices) of data and, thereby, obtain individual results from various perspectives. All analyses are conducted on rolling calibrating and testing time horizons between 2010 and 2014 on the German/Austrian electricity spot market.

The main result is that the best forecasts are generated by ARMAX models after spike preprocessing and differencing the data.

The present study extends the existing literature on electricity price forecasting by conducting a comprehensive analysis of the forecasting performance of different time series models under varying market conditions. The results of this study, in general, support the decision-making of electricity spot price modelers or forecasting tools regarding the choice of data transformation, segmentation and the specific model selection.

This paper aims to empirically test, from a regulatory portfolio management standpoint, the application of liquidity-adjusted risk techniques in the process of getting optimum and investable economic-capital structures in the Gulf Cooperation Council financial markets, subject to applying various operational and financial optimization restrictions under crisis outlooks.

The author implements a robust methodology to assess regulatory economic-capital allocation in a liquidity-adjusted value at risk (LVaR) context, mostly from the standpoint of investable portfolios analytics that have long- and short-sales asset allocation or for those portfolios that contain long-only asset allocation. The optimization route is accomplished by controlling the nonlinear quadratic objective risk function with certain regulatory constraints along with LVaR-GARCH-M (1,1) procedure to forecast conditional risk parameters and expected returns for multiple asset classes.

The author’s conclusions emphasize that the attained investable economic-capital portfolios lie-off the efficient frontier, yet those long-only portfolios seem to lie near the efficient frontier than portfolios with long- and short-sales assets allocation. In effect, the newly observed market microstructures forms and derived deductions were not apparent in prior research studies (Al Janabi, 2013).

The attained empirical results are quite interesting for practical portfolio optimization, within the environments of big data analytics, reinforcement machine learning, expert systems and smart financial applications. Furthermore, it is quite promising for multiple-asset portfolio management techniques, performance measurement and improvement analytics, reinforcement machine learning and operations research algorithms in financial institutions operations, above all after the consequences of the 2007–2009 financial crisis.

While this paper builds on Al Janabi’s (2013) optimization algorithms and modeling techniques, it varies in the sense that it covers the outcomes of a multi-asset portfolio optimization method under severe event market scenarios and by allowing for both long-only and combinations of long-/short-sales multiple asset. The achieved empirical results, optimization parameters and efficient and investable economic-capital figures were not apparent in Al Janabi’s (2013) paper because the prior evaluation were performed under normal market circumstances and without bearing in mind the impacts of the 2007–2009 global financial crunch.

This study analyzes the impact of exchange rate volatility (ERV) on inbound tourist arrivals from four ASEAN countries namely Indonesia, the Philippines, Singapore, and Thailand during 1970–2017. Volatility in the exchange rates between the tourist currency and ringgit Malaysia is measured using the Generalized Autoregressive Conditional Heteroskedasticity model. The results from Autoregressive Distributed Lagged models indicate that ERV has no significant impact on tourist arrivals from ASEAN to Malaysia. This implies that tourists from these countries may not be sensitive to ERV when choosing Malaysia as their travel destination. There are two possible explanations for the results. First, Malaysian ringgit has been depreciating against major currencies and regional currencies in recent years, which makes ringgit relatively cheaper than other ASEAN currencies. Second, the empirical results of the study support the argument that ERV has a more serious impact on tourist spending compared to tourist arrivals.

The purpose of this paper is to empirically examine the impact of capital investments on new capabilities development during competence‐destroying change. The moderating role of uncertainty is also explored.

This paper utilizes two distinct but related research streams; the literature on organizational capabilities and real options, to build the theory and hypotheses.

Data from a sample of 767 alliances between incumbent pharmaceutical firms and new biotechnology firms reveal that incumbent firms who increase capital investments in emerging technological domains despite the uncertainty present in them, are more likely to develop new products based on emerging technology.

The results encourage future research on the nexus of managerial cognition, capital investments, uncertainty and the adaptation process.

Extant literature implicitly suggests that capital investments are critical for developing new capabilities; yet no prior study has addressed the relationship between capital investments and new capabilities development during competence‐destroying change. This paper addresses this gap in the literature.