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³¯Â¥ 13.02.22 Á¶È¸ 448
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º» ¿¬±¸´Â ±¹³» ½Ç¼ÕÀǷẸÇè Áö±Þº¸Çè±ÝÀ» ±Ø´Ü°ª È¥ÇÕºÐÆ÷·Î ¸ðÇüÈÇÏ¿© À§ÇèÁ¶Á¤ (risk-adjusted) º¸Çè·á¸¦ »êÃâÇÏ´Â ¹æ¹ýÀ» ¸ð»öÇÏ°í ÀÖ´Ù. ÀÇ·áºñ Áö±Þº¸Çè±ÝÀÇ µÎÅÍ¿î ²¿¸®ºÐÆ÷ Ư¼ºÀ» ¹Ý¿µÇϱâ À§ÇØ ¼ÕÇغÐÆ÷ÇÔ¼ö¸¦ È¥ÇÕ¸ðÇüÀ¸·Î ¼³Á¤ÇÏ°í ±Ø´Ü°ª ºÐÆ÷ÇÔ¼ö¸¦ È¥ÇÕºÐÆ÷ÀÇ ±¸¼ºÇÔ¼ö·Î »ç¿ëÇÏ¿´À¸¸ç, µÎÅÍ¿î ²¿¸®ÀÇ Æ¯Â¡ÀÌ ³ªÅ¸³ª´Â ÀÓ°èÁ¡(threshold)À» °æÇèÀû ¼±ÅÃÀÌ ¾Æ´Ñ º£ÀÌÁö¾ð ¹æ¹ýÀ» ÅëÇØ ÃßÁ¤ÇÏ¿´´Ù. ¿¬±¸°á°úÀÇ ½ÇÁõºÐ¼®À¸·Î½á ¹Î¿µ°Ç°º¸Çèȸ»ç ÃÖ±Ù 2°³³â Àå±â¼ÕÇغ¸ÇèÀÇ ½Ç¼ÕÀǷẸÇè Áö±Þº¸Çè±Ý ÀڷḦ È°¿ëÇÏ¿© È¥ÇÕºÐÆ÷¸ðÇüÀÇ ¸ð¼ö¸¦ ¸ÞÆ®·ÎÆú¸®½º-ÇìÀ̽ºÆÃÁî ¾Ë°í¸®µëÀ» ÀÌ¿ëÇÏ¿© ÃßÁ¤ÇÏ¿´À¸¸ç, ÃßÁ¤°á°ú¸¦ ÀÌ¿ëÇÑ ½Ç¼ÕÀǷẸÇè À§ÇèÁ¶Á¤ ¼øº¸Çè·á »êÃâ¹æ¹ýÀ» ¿¹½ÃÇÏ¿´´Ù. ºÐ¼® °á°ú Áö±Þº¸Çè±Ý ½Éµµ ºÐÆ÷ÀÇ »óÀÌÇÔ¿¡ µû¸¥ À§ÇèÀ» °í·ÁÇÑ º¸Çè·á´Â ±âÁ¸ÀÇ ´ë¼öÀÇ ¹ýÄ¢¿¡ Åä´ë·Î µÐ ±â´ë°ªÀ¸·Î »êÃâÇÑ º¸Çè·á º¸´Ù 10.5% Á¤µµÀÇ À§ÇèÇÒÁõÀÌ ÇÊ¿äÇÑ °ÍÀ¸·Î ³ªÅ¸³ª°í ÀÖ´Ù.
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In this paper, we introduce a practical method of estimating the threshold over which a heavy-tailed distribution is approximated asymptotically for the underlying distribution of extreme events. We introduce a mixture model of a loss distribution of a certain parametric form below a threshold and a heavy-tailed distribution above the threshold. The number of exceedances over a threshold are considered a random variable for a prior distribution in the Bayesian framework in order to estimate the threshold and corresponding extreme value index. A numerical example is given to illustrate the Bayesian estimation of the parameters by applying the mixture model to losses in medical insurance policies in Korea. About a 10.5% extra charge over traditionally calculated premiums seems necessary to hedge the risk embedded in the heavy-tailed loss distribution.
Å°¿öµå : ±Ø´Ü°ª ºÐÆ÷, ±Ø´Ü°ª ÀÌ·Ð, ¸ÞÆ®·ÎÆú¸®½º-ÇìÀ̽ºÆÃÁî, º£ÀÌÁö¾ð ÃßÁ¤, ½Ç¼ÕÀǷẸÇè, ÀϹÝÈÆÄ·¹ÅäºÐÆ÷, ÀÓ°èÁ¡, È¥ÇÕºÐÆ÷
°ÔÁ¦Áö : º¸Çè±ÝÀ¶¿¬±¸ 71±Ç pp.71-98
Ãâó : https://www.kiri.or.kr
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