The approach of value at risk (VaR): Measure risk stock returns of pharmaceutical sector

Value at Risk (VaR) method. The development of financial market conditions during the past year as a result of the Covid-19 pandemic is very uncertain, which must be watched out by investors. So, this study takes an observation period of one year with a daily period of shares. Case studies in this research using secondary data on pharmaceutical sector on the IDX. Three of the eleven stocks pharmaceutical sector that showed fluctuating growth were Kalbe Farma Tbk (KLBF), Sido Herbal Medicine and Pharmaceutical Industry (SIDO), Kimia Farma Tbk (KAEF). The VaR value of stock securities portfolios is calculated using the historical simulation approach, Monte Carlo simulation, and correlation method. The Correlation method was used because of the calculation of the simplest parameter in measuring VaR. The Historical Simulation method was used because the data follow nonparametric condition. Meanwhile, the Monte Carlo simulation is a better method because the value of the simulation that is always convergent to the real situation. In this research to compared the best method determined by the MAD value. The results show that VaR values through stock simulations Variance Covariance for the four issuers where KLBF is 3.09%, SIDO has a VaR value of 3.34%, KAEF 3.42% and PRDA 3.07%. Using Historical simulations for the four issuers where KLBF and PRDA have the same VaR value of 2.93% while for SIDO it is 3.17% and KAEF is 3.33%. by using Monte Carlo simulation for the four issuers where KLBF is 6.29%, SIDO has VaR value of 7.06%, KAEF of 7.29% and PRDA of 5.39%. with 95% confidence level. © 2023 Author(s).

Open Innovation in Organization Through Stakeholder Capability During Pandemic (COVID-19): Indonesian SMEs Perspective

This study aims to analyze the role of stakeholders who support the open innovation transition in SMEs. This is important because the innovation process has shifted from closed innovation to open innovation which requires good management of organizational capabilities, especially in managing stakeholders and resources owned by the organization. However, in the current pandemic situation, SMEs have challenges in adopting and implementing these open innovations. In this study, a conceptual research model was compiled and produced which elaborated several previous references, and then tested empirically on respondents, namely in SMEs in Indonesia. The responses involved in this study were 218 SMEs, but the complete response are 206 respondents. Data testing was carried out using the Partial Least Square-Structural Equation Modeling (PLS-SEM) statistical method. The results of hypothesis testing indicate that there are four variables that significantly strengthen the effect of closed innovation on open innovation, namely financial capability, network, knowledge management system, and organizational culture (p<0.01). The moderating variable with the most dominant influence is financial ability (beta=0.915 , p<0.01). Based on the results of this study, SMEs that have high financial capabilities or have advantages in terms of funding and financial management can make the transition from closed innovation to open innovation better or independently. There is one moderating variable that is not proven to be significant, namely technology. These findings can then be used to formulate appropriate policies to support the adoption of open innovation in the context of developing the ability of SMEs to survive during the pandemic.