In financial economics, it is assumed that individual and institutional investors behave differently in the market. While institutions are viewed as informed traders, individuals are considered as noise traders with psychological bias as in Kyle (1985) and Black (1986). In particular, Choe et al. (1999) and Kaniel et al. (2008) examine the difference of individual investor trading in stock markets and its impact on the dynamics of stock prices. Based on these works, this paper aims to show how individual investors affect volatilities as well as stock returns, and whether individuals are uninformed traders of volatility trading. Our study, however, differs from the previous studies in that we focus on options markets instead of stock markets. There are two reasons that we examine the behavior of individual investors in options market other than in the stock market. First, according to the previous studies individual investors’ behavior in options markets is inconsistent with their behavior in stock markets. Individual traders heavily depend on the changes of past prices and prefer out-of-the money options that have a severe leverage effect, which is significantly different from institutional and foreign investors. These unique characteristics of individual investors in options markets have made us interested in looking into the relationship between individual investors and options markets. Second, the structure of options markets is, in nature, optimal for analyzing the effect of each type of investors. When studying its impact using stock returns, we have to pay much attention in order to adjust for the effects by various factors such as dividends and stock-splits. However, we an easily adjust the effect of underlying asset returns on option returns using the series of options with different strike prices and different maturities, thereby simplifying our research design. As well-known, volatility is the measure of the price level which is adjusted for the effect of underlying asset. In short, if stock trading is based on the prospect about the future direction of underlying asset prices, option trading is based on that about the future direction of volatility. Therefore, it is called volatility trading. Although this paper investigates the behavior of individual investors in stock markets based on the idea of Kaniel et al. (2008), it differs from the study. While it solely focuses on the role of individual investors as liquidity providers in stock markets, our paper deals with the change of volatility by the behavior of individuals in the options market. Thus the aim of this paper is to see whether their interpretation holds valid in the volatility trading in the options market. In this paper, we analyze the autocorrelation between implied volatilities, including implied volatility of at-the-money options, and VKOSPI 200 index which results from calculating one-month model-free implied volatility using out-of-the-money KOSPI 200 index options. In addition, we examine whether volatility changes can be explained by the intensity of the buying/selling of individual investors using regression analysis, even after adjusting for the mean reverting of implied volatilities. For our second study, we sort data into ten decile groups according to the intensity of the buying/selling of individuals as a proxy for trading imbalances. Decile 1 is the most intense selling period and decile 10 is the most intense buying period. Then we examine buying/ selling imbalances and trading patterns of individuals in terms of the trend in simultaneous and cumulative volatility changes of deciles 1 and 2 and deciles 9 and 10, prior to, current, and after trading week separately, and calculate t-statistics of cumulative volatility changes for testing significance. The main results of this paper can be summarized as follows. First, we find that implied volatility is mean-reverted and that as the intense buying by individuals increases, so do current and future implied volatilities. Second, the imbalance of volatility between intense buying and selling is analyzed for each decile. According to the results, volatility significantly increases when individuals become only intensely buying options, which is likely to be consistent with the fact that individuals are net buyers in options markets. Finally, we investigate volatility change prior to, current, and after trading week and recognize that individuals are volatility-momentum traders who tend to buy options after volatility increases. Also the results of the robustness test indicate that the effect of individual investors on volatility is still significant after controlling for the total volume of options and the realized volatilities of underlying assets. Therefore, we can conclude that the information contained in the intensity of individual buying and selling is independent of information contained in the total volume and realized volatility.

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The globalization and integration of financial markets across border is an inevitable phenomenon, in line with which even emerging economies including Korea have opened their financial markets to international investors. With increasing globalization in capital markets, the cross border capital flow has become an important issue because it is directly related to vulnerability of small and open economies. In the past decades, the international financial economics literatures examine the effect in relation of the return of price and volatility transmission between stock markets (Hamao et al., 1990, Karolyi, 1995; Ng, 2000; Singh et al., 2010) or between index or commodity futures markets (Roope and Zurburegg, 2002; Xu and Fund, 2005; Liu and An, 2011). Although some studies examine the relation in currency markets, their focus is limited to price return and volatility transmission between major currencies such as Japanese Yen, Deutsch Mark, and British Pounds (Baillie and Bollerslev, 1991; Hong, 2001) or between European currencies (Baele, 2005; Bubák et al., 2011). No study so far has examined those between the currencies in the Asian region, so this paper is in fact the first of its kind to do so. As such, the purpose of this paper is to examine the dynamics of price discovery and volatility transmission between Korean Won (KRW), Chinese Yuan (CNY), and Japanese Yen (JPY). We use the US dollar, which has been used for payment and settlement in international trades, as the numeraire for evaluating the three currencies and gold. Historically, Japan has been the most influential economy for Korea next to the US. Korea and Japan have similar economic structure and geographical proximity to each other. Meanwhile, China’s economy has grown rapidly since 1990’s and has become the second largest economy in term of GDP in the world. Accordingly, its economic influence on Korea has also rapidly grown, and since 2006 it has been Korea’s biggest trade partner, replacing Japan. Hence, we conjecture that some power shift must take place from Japanese yen to Chinese yuan in terms of these currencies’ influence on Korean won. Subsequently, we conduct studies to understand how the three currencies are interrelated in term of the price discovery and volatility transmission. Our study period is from Aug. 1, 2005 to Nov. 7, 2012 since China was under the fixed exchange rate regime before Aug. 1, 2005. We also remove the period from Jul. 1, 2008 to May 31, 2010 from the above because China temporally enforced the fixed rate system during this global financial crisis (hereafter GFC) period. As a consequence, our sample period is divided into two sub-sample periods. The first one is from Aug. 1, 2005 to Jun. 30, 2008, which is before the GFC period. The other one is from Jun. 1, 2010 to Nov. 7, 2012, which is after the GFC period. To investigate the price discovery, we employ Hasbrock’s (1995) information share measurement method. Our findings are follows: Before the GFC, the contribution of JPY for the price discovery of KRW is rather high while the contribution of CNY for the discovery of KRW is low. However, after the GFC, the price discovery contribution of CNY on KRW becomes quite significant whereas that of JPY diminish. To identify the volatility transmission, we use a multivariate volatility model, BEKK. However, since we should analyze four variables, we decide to employ the rotated multivariate GARCH model (hereafter MRARCH) of Noureldin et al. (2012) for numerical stability. Using this model, we find that there is no significant volatility transmission between JPY, CNY, and KRW before the GFC. However, the influence of CNY towards KRW and JPY in volatility transmission becomes apparent after the GFC. We suggest that increased role of CNY in volatility dynamics for Korea is caused both by the increase in the trade volume between China and Korea and by the rise of China’s economic status in the world market. Although we can not get some strong evidences in terms of statistical significances, our empirical results support that there is a relative influence power shift from JPY to CNY before and after the GFC. This is the first systematic empirical work to find the linkage between the major currencies in Asia region. We hope that this study trigger further research on the multivariate currencies dynamics in the region.

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This paper develops a new credit risk model for small and medium-sized enterprises (SMEs) based on the DSW model of stochastic default intensity and the dynamics of underlying time-varying covariates. In particular, our model incorporates the default probability with the probability of initial public offerings (IPOs) in the framework of a censored stopping-time model. Default stopping time comprises of such variables as total borrowings/total assets in the stability, ROA in the profitability, account payable/sales in the activity, and financial expenses/total cost in the etc. As for the IPO stopping time, the natural log of total assets in the stability, net income/net sales in the profitability, and cash flows from operating activities/ net sales in the etc. are significant for the IPO stopping time. It is found that our model based on DSW model outperforms the multi-period logit model consistently and robustiously according to various prediction horizons and lag orders of VAR for macro-variates because the continuous stopping-time framework emphasizing on the stochastic default intensity accurately calculates the default probability superior to the discrete-time model equivalently computing the survival and default probability. In addition, it captures countercyclically monthly-frequency movement of capital requirements in compliance with the new Basel Accord. The implication is that our model as the early warning system may help the financial supervisory authority to predict the expected loss due to the sudden collapse of economic fundamentals such as rapid transfer of debtors' credit risks.

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Household debts have become an increasingly influential factor in holding financial assets (Cocco, 2005; Yao and Zhang, 2005; Chetty and Szeidl, 2009; Becker and Shabani, 2010). This paper therefore investigates the impact households’ debts have on their holding status of various financial instruments by examining the data of 10,000 households from the 2011 and 2012 Household Financial Survey conducted by Statistics Korea. In exploring the relations between household debts and household financial asset holdings, this study specifically addresses two issues. One is about the extent to which household debts across household income classes have an impact on the shareholdings of each financial instrument. The other is about the sway of household debts on the choices of three financial instruments at the moment households make asset allocation decisions. Some evidences are found on the relation of households’debt and financial asset holdings. First, while financial asset holdings as well as household debts increase in proportion to a certain age up to 60, they drop sharply afterwards. The ratio of financial asset over total asset exhibits a U-shaped distribution. This result can be explained by the fact that Korean households in general have strong tendency to demand real estate relative to financial assets, and this preference is more pronounced as age increases. In addition, both financial asset holdings and household debts increase proportionally to the education level of households, while the ratio of financial asset or financial debts over total asset shows a moderately inverted U-shaped pattern. These results seem to imply that the better educated and the older households, the more capable they become in employing debt instruments either in financial or real assets as tools to accumulate wealth. To explore the relation of household debt and financial asset holdings, dependent variables such as fin, risky assets, and cash are normalized by dividing financial wealth, risky assets, and cash by total assets. With the scaling of dependent variable being equal to or greater than zero, Tobit regressions are conducted while, for the purposes of model setting, controlling for either one or combination of categories of different levels of education, industries, and occupations. Besides, to control for the endogeneity of household debts with financial asset holdings, I use the predicted household debt ratio as an instrument variable estimator obtained from running 2SLS. The second finding is that the impact of household debt ratio on financial wealth holdings is plainly pronounced among household income classes based on the analysis of the samples of 2010 in quintiles, although the household debt ratio in the whole sample negatively dominates the holdings of three financial instruments. In particular, the household debt ratio negatively affects the holdings of financial wealth in middle income class, whereas it positively does in high income class. This result indicates that the debt ratio of high income households plays a complementary role to financial wealth holdings, but, in contrary, the debt ratio of the middle income households substitutes financial asset holdings. Further, this finding implies that high income households take advantage of debt to accumulate wealth by investing in both financial assets (Pollin, 1988, 1990) and real estate, while middle income households do not hold both together, probably owing to credit constraint. However, high income households owning real estates are more inclined to reduce the holdings of both risky assets and cash holdings. Third finding is about asset allocation across household income classes. The financial debts in middle income households shrink the holdings of financial wealth, risky assets, and cash, whereas the disposable income in middle income class raises the holdings of the three major financial vehicles. But the financial debts in high income class increase the financial wealth, but reduce the cash holdings. Additionally, when the sample is restricted to households with debts, then financial debts reduce both financial wealth and risky assets, but raise the level of cash holdings. Taken together, these results generate two possible interpretations: the extent of the impact of financial debts over asset allocation varies depending on either household income classes or the levels of household debts. As a robustness check, I carry out the Tobit models using the 2011 sample. Unfortunately, the effect of household debts on financial wealth for the high income class only is consistent with the result in the prior year sample. This research is restricted to the two survey period samples and there are, therefore, limitations to fully take in the features of households behaviors on asset holdings and asset allocation in relation to household characteristics over time. I have a good guess about the asset holdings and choices by households in 2010 and 2011, which were unduly influenced by the 2008 Subprime Mortgage disaster. One of future research is possibly the comparison with changes in households asset holdings in terms of household debts and income before and after 2008 financial crisis.

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