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Catastrophe (CAT) events associated with natural catastrophes and man-made disasters cause profound impacts on the insurance industry. This research thus reviews the impact of CAT risk on the insurance industry and how traditional reinsurance and securitized risk-transfer instruments are used for managing CAT risk.

This research reviews the impact of CAT risk on the insurance industry and how traditional reinsurance and securitized risk-transfer instruments are used for managing CAT risk. Apart from many negative influences, CAT events can increase the net revenue of the insurance industry around CAT events and improve insurance demand over the post-CAT periods. The underwriting cycle of reinsurance causes inefficiencies in transferring CAT risks. Securitized risk-transfer instruments resolve some inefficiencies of the reinsurance market, but are subject to moral hazard, basis risk, credit risk, regulatory uncertainty, etc. The authors introduce some popular securitized solutions and use Merton's structural framework to demonstrate how to value these CAT-linked securities. The hybrid solutions by combining reinsurance with securitized CAT instruments are expected to offer promising applications for CAT risk management.

The authors introduce some popular securitized solutions and use Merton's structural framework to demonstrate how to value these CAT-linked securities. The hybrid solutions by combining reinsurance with securitized CAT instruments are expected to offer promising applications for CAT risk management.

This research reviews a broad array of impacts of CAT risks on the (re)insurance industry. CAT events challenge (re)insurance capacity and influence insurers' supply decisions and reconstruction costs in the aftermath of catastrophes. While losses from natural catastrophes are the primary threat to property–casualty insurers, the mortality risk posed by influenza pandemics is a leading CAT risk for life insurers. At the same time, natural catastrophes and man-made disasters cause distinct impacts on (re)insures. Man-made disasters can increase the correlation between insurance stocks and the overall market, and natural catastrophes reduce the above correlation. It should be noted that huge CAT losses can also improve (re)insurance demand during the postevent period and thus bring long-term effects to the (re)insurance industry.

This chapter derives sentiment indicators (implied volatility and implied skewness) from the option pricing models of Corrado and Su (1996), Bakshi, Kapadia, and Madan (2003), and Zhang, Zhen, Sun, and Zhao (2017), and then integrates these sentiment indicators with artificial intelligence deep neural network (AIDNN) for developing the behavioral finance AIDNN (BFAIDNN) algorithms. We apply the BFAIDNN algorithms to daily derivatives data of Taiwan Futures and Options markets from 2015 to 2017. Our results demonstrate that the trading strategies established by the BFAIDNN algorithms can generate positive rewards.

We employ three systemic risk measures of banks, including the systemic risk index (SRISK) and marginal expected shortfall (MES) of Brownlees and Engle (2017) and the conditional Value-at-Risk (ΔCoVaR) of Adrian and Brunnermeier (2016), to analyze bank's exposure and contribution to systemic risk in the banking system when a financial crisis occurs. We find evidence that time-varying systemic risk exists, and systemic risk exposures escalate with the interconnectedness of banks. We also find revenue diversification is another significant factor that reduces a bank's exposure to systemic risk but not for banks in Taiwan and Singapore.

This chapter, by virtue of a generalized specification, examines the equilibrium growth paths under two distinct scenarios, namely, a small open economy and a small semiopen economy in a two-sector, endogenous growth model of money. We show that these two scenarios end up with very different characteristics of equilibrium and the steady-state effects of inflation targeting (IT). In a small open economy, there is a nonbalanced-growth path equilibrium (hence, great ratios are nonstationary), while in a small semiopen economy there is a balanced-growth path equilibrium (great ratios are stationary). This provides a convincing reconciliation of the discrepancy in the empirical literature on great ratios. In addition, our steady-state analysis implicitly suggests that a lower inflation target gives rise to a positive GDP growth effect only for those IT countries which are more open to international trade. This enables us to explain why IT countries are relatively open to the international market and why some IT countries with a high degree of trade openness continuously lowered their inflation targets in the 1990s.