This study examines the relationship between the Northeast Asian and U.S. markets with particular attention placed on the global financial crisis period. For this purpose, the paper employs dynamic approaches including DCC-MGARCH, BEKK and Risk Decomposition models to ensure the robustness of empirical findings. The results are as follows. First, The Northeast Asian stock market remains relatively independent from the U.S. market movements during the sample period. Second, the regional market shows an increasing trend of joint integration with the U.S. market. Third, an increased integration is found to be only unique to the crisis period. We find no evidence to support the findings of previous empirical studies which suggest the increased level of integration since the GFC.

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Most venture investments in Korea are from venture funds supported by limited partners (LPs), and thus LPs always want to know the main determinants of the fund performance and how well their funds are operated. General partners (GPs) who want to successfully receive the investments for their venture funds from LPs are also very interested in the same issues. This study measures the performance of Korean venture funds using the public market equivalent (PME), internal rate of return (IRR), and multiple measures. The features and determinants of each performance measure are then analyzed using actual Korean venture fund data. Few studies have addressed these issues, due to limited access to and a shortage of Korean venture fund data. This study is the first to use actual venture fund data to investigate the performance and its determinants. To accurately measure the performance of venture funds, the Net Asset Value problem must be addressed. However, this study is free from that issue because the sample only contains fully liquidated venture funds. The PME measure (average 1.07) shows that on average, Korean venture funds performed slightly better than the Korea Composite Stock Price Index market from 1992 to 2010. This result may appear disappointing, but it is too early to draw conclusions, as our sample only includesthe funds formed until the early 2000s. The IRR is the most volatile ofthe three measures (its standard deviation is 10.2 times the average), because it is influenced by vintage years and market situations, such as fund liquidation periods. The average is much higher than the median for all three of the measures, suggesting that some larger funds perform considerably well. We analyze the determinants of fund performance based on three factors—fund characteristics, fund operator characteristics, and market situation characteristics—and find not only that each performance measure has different determinants, but also that the latter’s explanatory power is higher for the PME measure than for the other measures. We also find that the PME and multiple measures exhibit similar patterns, whereas the IRR is biased as a performance measure. The determinants that explain the PME measure well are fund size, fund operation period, fund cash inflow and outflow, and the number of new fund formations. The determinants that best explain multiple measures are fund size and fund cash inflow and outflow. The best determinants for the IRR measure are fund size (negative relation), fund operation period, fund cash inflow, number of liquidated funds, and vintage year. For the PME and multiple measures, fund size has a U-shaped relation with fund performance, whereas it has a negative relation with the IRR measure. The fact that the funds smaller than $3 million perform well, especially for the IRR measure, is a unique feature of Korean venture funds not found in Harris et al. (2014), who studied U.S. venture funds. This suggests that the IRR can be highly biased for smaller funds. Remarkably, the shorter the fund’s operation period, the better the fund performance in the case of the PME measure, but not in the case of the multiple measures. The performance differences among the investment fields stand out for the PME and multiple measures (especially the latter), but are not found for the IRR. For the PME and multiple measures, IT-specialized funds exhibit the best performance, and bio-specialized funds the worst. General funds without specialized investment fields only outperform specialized funds for the multiple measure. Funds with more than two cash flowsperform better than those with only one cash flow for all three of the measures. However, the IRR only shows a statistically significant result for cash inflows (liquidation), indicating that it is biased toward fund liquidation. There is no significant difference in performance between corporate venture capital (CVC)and non-CVC, between financial and non-financial venture capital (VC), and between individual and non-individual VC for all of the three measures. The number of new funds during the fund formation period has a significantly negative influence on the PME measure, and the number of liquidation funds during the fund liquidation period negatively influences theIRR measure. Because the average operating period of Korean venture funds is five-and-half years, the data used for this study only cover the venture funds mainly formed before 2005. With more fund data from the 1980s, 1990s, and 2000s, as in the U.S., it would be possible to obtain more meaningful results. It is probable that we willunderstand more about the Korean venture fund industry once we reach the mid-2010s and have access to the performance results of venture funds formed during the 2000s, which was a booming period for the Korean venture capital industry.

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Risk aversion and time preference are fundamental variables when people make financial decisions, but there are numerous opinions for how to estimate them. Researchers frequently estimate the demand of financial products with an ordinary survey, where subjects provide self-perceptive information. Given that behavioral financial questions are included in the simple questionnaire, the subjects may misunderstand such questions, or may not be able to completely understand the importance of the concepts driving them. Therefore, subjective bias could be implicitly problematic in many surveys, rendering their results untrustworthy. Although researchers can draw their conclusions based on the data collected from the survey, the economic results may be distorted or prove insignificant. This study takes the behavioral and experimental economics explored in Kahneman and Tversky (1979) and applies those theories to a financial product, such as a single payment annuity. This is a relatively new product in Korea’s aging population, thus demand for it has not been analyzed in the Korean context, despite the popularity of such products in the U.S. (Mitchell and Utkus, 2003). Following the method of eliciting risk aversion proposed by Holt and Laury (2002), we develop a new measure of risk aversion that is modified using the quantile normalization (Bolstad et al., 2003), and use the measure to check for subjective bias when choosing an annuity product. Time preference is also very critical in financial decisions. We propose a time preference measure that follows Coller and Williams (1999) and use it as an objective measure of the subject’s time preference. We design an experiment in which the subjects are guided by the instruction and payoff schemes, and follow several sessions. The participants choose a lottery from a pair of lotteries whose payoffs and probabilities are different, revealing the participants’ risk aversion preferences. In the next experiment on time preference, the participants choose a lottery from a pool of several featuring different interest rate payoffs with higher and lower payment spans, depending on the interest rates. During the experiment, the participants’ choices reveal their time preferences for our analysis. In addition to the results of the aforementioned experiments, we collect the participants’ background information, such as financial status, financial literacy, and intelligence. Given the dataset, we can then find that the subjective answers from the survey are not significantly related to the financial decision on single payment annuity. This allows us to verify that the subjective bias exists, and confirm that self-perceptive surveys are not trustworthy in choosing financial products. We also compare the self-perceptive answers and the experimental measures to test which methods are more reliable and significant. The results show that neither approach significantly determines time preferences and risk aversion in some model specifications. We construct the bias variables of risk aversion, time preference, life expectancy, financial literacy, and intelligence using the differences between the subjective answers and experimental results. Thus, when we execute econometric models with the bias variables of interest, we discover that the biased self-perceptive and experimental measures of the time preference, risk aversion, life expectancy, financial literacy, and pension literacy are significant determinants of the demand for single payment annuity. Therefore, the preferential and behavioral variables are critical in choosing financial products such as annuities, rather than the self-perceptive answers in the survey. We also run a Heckman two-step regression of the premium of the single payment annuity and find that financial status and portfolio are the most crucial determinants, regarding the moneywise incentive. Finally, the time preference and risk aversion experiments we design reveal that behavioral and preferential biases are critical in choosing financial products. Therefore, we conclude that researchers should take bias variables seriously when they estimate the demand for financial products, even after controlling for other variables.

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We investigate the effectiveness of the original PIN model (Easley, Kiefer, O’Hara, and Paperman, 1996) and five variants of the adjusted PIN model (Duarte and Young, 2009) in the Korean stock market. Throughout the series of likelihood-ratio fitness tests, we find that the unrestricted version of the adjusted PIN model fits best in the Korean stock market data.

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