This study examines the market for green bonds, which have been in the spotlight as an eco-friendly investment product. We analyze the volatility dynamics and spillovers between the equity and green bond markets. As the return dynamics of financial products typically exhibit asymmetric volatility, we check whether green bonds also share this property. Our analyses confirm that although green bonds do exhibit the asymmetric volatility phenomenon, their volatility, unlike that of equity, is also sensitive to positive return shocks. An analysis of the association between the green bond and equity markets confirms that although the two markets have some volatility spillover effects, neither responds significantly to negative shocks in the other market.
This study examines suppliers' decision making regarding supply chain ethics and transparency using an agent‐based model with Q‐learning agents. A supplier may not accurately identify the demand for its product because the impact of supply chain ethics on the market is opaque. Thus, we define each supplier as a Q‐learning agent that learns via an iterative game process. Our simulation results show that suppliers maintain low levels of ethics and transparency, which is a collusion behavior. Several suppliers deviate from collusion, but most of the suppliers collude to manage the supply chain unethically and opaquely.
This study proposes an early warning system for risks of the housing market based on machine learning models. We adopt a signal approach to detect the housing market risk and establish the early warning system using classification methods. Considering the moment when the housing market falls into recession as a warning signal, we set the signal as the price which is more than the sum of the average and standard deviation of upcoming prices. The detected signals are consistent with empirical observations in the Korean housing market. We select the best performing function among classification models for machine learning which predicts a warning signal. As a result of an intercomparison of models including the logistic regression, the support vector machine, the random forest and the artificial neural network with the use of inputs such as housing price indices, macroeconomic variables and other housing market variables, we find that the random forest demonstrates the highest prediction performance. Our early warning system yields policy implications in terms of relevant detection of price fluctuations in the housing market.
This study analyzes the relationship between the housing and stock markets, focusing on housing market bubbles. Stock market dynamics generally have a more significant impact on housing price movements than housing market dynamics have on stock prices. However, if housing market information is provided in the form of a signal, housing price movements can predict stock market volatility. Accordingly, we build a machine learning-based early warning system for the housing market using a long short-term memory (LSTM) neural network. We apply the generalized supremum augmented Dickey-Fuller test to extract a bubble signal for the housing market. The signal significantly detects future price changes in the housing market and explains stock market volatility. Our early warning system effectively detects the bubble signal using the LSTM neural network. We confirm that the LSTM approach performs better than other benchmark models, the random forest and support vector machine models. Our empirical test results suggest that our early warning system can simultaneously detect risks in the housing and stock markets.
This study describes the structure of the capital markets for small-and medium-sized enterprises (SMEs) and startup companies in Korea, which is an emerging market that has experienced drastic changes. The overall capital market can be divided into private and public capital markets. In the private capital market, most of the demand for capital comes from non-listed private firms, including startups and SMEs. In the case of SMEs and startups, the KOSDAQ, the Korea New Exchange (KONEX), and primary collateralized bond obligations (P-CBOs) are part of the public capital market. SMEs and startups are generally incapable of raising sufficient capital owing to their low credit ratings, and they largely have limited access to primary markets to issue shares and borrow money. The Korean government has developed a systematic financial aid program to provide funds to these companies. The fund for SMEs has significantly contributed to the development of the venture capital market. Many Korean banks provide substantial lending to SMEs, but this lending is available only because of the Korean government's loan recovery guarantee. Furthermore, SMEs can issue corporate debt in the form of primary collateralized bond obligations through government guarantees, but such debt issuances have placed increasing pressure on public guarantee institutions.
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