Journal of Insurance and Finance 2021 KCI Impact Factor : 0.67

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pISSN : 2384-3209
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2020, Vol.31, No.1

  • 1.

    Understanding the Effects of Unanticipated Future Monetary Policy Shocks

    Joonyoung Hur | 2020, 31(1) | pp.3~52 | number of Cited : 1
    Abstract PDF
    This paper studies the effects of future monetary policy shocks unanticipated by private agents using an estimated new Keynesian dynamic stochastic general equilibrium model framework. Analysis of U.S. data from 1967 Q1 to 2008 Q1 shows that the information structure on monetary policy substantially improves the model's fit to data compared to the conventional contemporaneous-shocks-only counterpart. To examine the role of agents' foresight about future monetary policy shocks, a counterfactual analysis on agents' information flows is conducted. If, throughout the sample period, agents had possessed perfect foresight about future monetary policy shocks, the business cycle fluctuations would have been milder as the volatility of key macroeconomic variables drops markedly. In addition, we find that the model-implied uncertainty about future monetary policy contains significant explanatory power for disagreement—cross-sectional dispersion of forecasts—in the Survey of Professional Forecasters.
  • 2.

    Corporate Social Responsibility and the R&D Risk Choices in a Product Differentiated Market

    Sang-Ho Lee , Cho Sumi | 2020, 31(1) | pp.53~86 | number of Cited : 1
    Abstract PDF
    This paper investigates the choices of R&D risk in a product differentiated duopoly market where the corporate social responsibility (CSR) is involved in the analysis. We compare the equilibrium choices between quantity and price competitions and find the following findings. Under quantity competition, CSR firm’s R&D risk level is higher than that of private firm, and the difference of R&D risk levels increases as the degree of product substitution or CSR level increases. Under price competition, the R&D risk level of CSR firm is higher than that of private firm with a lower substitutability, but the reverse is true with a higher substitutability. Comparing each firm’s R&D risk level under quantity and price competitions, the level of R&D risk under price competition is always higher than that under quantity competition and, as either product substitutability or CSR level increases, the difference of R&D risks is higher under price competition. We also extend the analysis into the case that the CSR firm chooses the degree of CSR strategically to increase its profit. We then show that if the degree of product substitutability is larger (smaller), the impact on CSR under quantity (price) competition is larger.
  • 3.

    Predicting Korean Stock Market Return with Financial and Macro variables - Focusing on In-sample and Out-of-sample Tests -

    Chun, Sungju | 2020, 31(1) | pp.87~113 | number of Cited : 3
    Abstract PDF
    This study evaluates the predictive power of 12 financial and macroeconomic variables for Korean stock market returns of different horizons. Both the return predictability of in-sample and out-of-sample tests are considered to examine each variable’s predictive ability more robustly. For this purpose, this article employs the MSE-F statistic developed by McCracken (2007) and the ENC-NEW statistic developed by Clark and McCracken (2001) to compare nested forecast models. In addition, the bootstrapping procedure is applied for both in-sample and out-of-sample inferences to address the finite-sample bias and the autocorrelated disturbances from overlapping observations. As a result, the book-to-market ratio variable is found to be the most consistent and significant predictor as it rejects the null of no predictability for both in-sample and out-of-sample tests.
  • 4.

    Stock Return, Volume and Volatility in the EGARCH model

    Yi Jiang | 2020, 31(1) | pp.115~136 | number of Cited : 0
    Abstract PDF
    I use EGARCH model to study the asymmetric impact of negative and positive shocks on stock return volatility. I find the asymmetric effects exist and the impact on volatility of a negative shock is greater than that of a positive shock. Furthermore, I examine the dynamic relationship between returns, volume and volatility of stock index by introducing trading volume as an exogenous variable into the EGARCH model. The results indicate that trading volume contributes some information to the returns processes of stock indexes. However, the persistence of volatility remains even after incorporating lagged volume effects, which are proxies for information flow. Granger causality tests demonstrate stronger evidence of returns causing volume than volume causing returns.