
12ÔÂ8ÈÕ--9ÈÕ£¬µÚ¶þ½ìÖйú±´Ò¶Ë¹¼ÆÁ¿¾¼ÃѧÂÛ̳£¨2023£©ÔÚб¦GGÈçÆÚ½øÐС£±¾½ìÂÛ̳ÓÉÖйú±´Ò¶Ë¹¼ÆÁ¿¾¼ÃѧÂÛ̳Óëб¦GG¾¼ÃѧԺ½áºÏÖ÷°ì£¬Ð±¦GG¾¼ÃѧԺ³Ð°ì£¬À´×Ôº£ÄÚ±íµÄËÄÊ®Óàλר¼ÒѧÕß±ðÀëͨ¹ýÏßÏ»òÕßÏßÉÏ·½Ê½²Î¼Ó×êÑС£Õâ´ÎÂÛ̳ΪѧÕßÃÇÌṩÁËÉî¿Ì»¥»»µÄƽ̨£¬¶ÔÍÆ¶¯ÎÒ¹ú±´Ò¶Ë¹¼ÆÁ¿¾¼ÃѧµÄ×êÑÐÓгÁÒªÒâ˼£¬»ñµÃÁË¿í·º¹Ø×¢ºÍÖ§³Ö¡£Í¨¹ýÕâÒ»»î¶¯£¬²»½öÍØÕ¹ÁËÓë¹ú¼ÊÒ»Á÷ѧÕßµÄѧÊõºÏ×÷£¬Ò²ÓÐÁ¦ÍƽøÁ˶Ա´Ò¶Ë¹¼ÆÁ¿¾¼ÃѧÀíÂÛÓëʵ¼ÊµÄÉî¿Ì̽Çó£¬ÎªÎÒ¹úÔÚÕâÒ»ÁìÓòµÄ×êÑÐÓë·¢Õ¹×¢ÈëÁËеĻîÁ¦¡£

ÂÛ̳¿ªÄ»Ê½ÓÉб¦GG¾¼ÃѧԺÄßÖÐнÌÊÚÖ÷³Ö¡£Ð±¦GG¾¼ÃѧԺµ³Î¯¸±Êé¼Ç¡¢³£Îñ¸±Ôº³¤Òó·ï½ÌÊÚÊ×ÏȶԲÎÓëÕâ´ÎÂÛ̳µÄ¸÷λ¼Î±ö·îÉÏÁËÖܵ½ÑóÒçµÄÓ½Ó´Ç¡£ÒóÔº³¤°µÊ¾£¬±¾´ÎÂÛ̳»ã¼¯ÁËÀ´×Ô¸÷µØµÄ¶¥¼âר¼ÒºÍѧÕߣ¬ËûÃǽ«·ÖÏí¸÷×ÔÓɱ´Ò¶Ë¹¼ÆÁ¿¾¼ÃѧÁìÓòµÄ×îÐÂ×êÑгɾͺͼû½â¡£ÎÒÃÇÓÐÐÅÄͨ¹ý»¥»»ºÍºÏ×÷£¬Ñ§ÕßÃǽ«¿ÉÄÜÉîÈë¶Ô±´Ò¶Ë¹²½ÖèÔÚ¾¼ÃѧÖÐÀûÓõÄÒâʶ£¬ÎªÑ§Êõ½çºÍʵ¼ÊÁìÓò´øÀ´¸ü¶àµÄÆô·¢ºÍ´´Ð£¬ÎªÎÒ¹ú¼ÆÁ¿¾¼ÃѧµÄ·¢Õ¹¹±Ï×б¦GGÖǻۺÍÁ¦Á¿¡£

Ëæºó£¬ÖйúÈËÃñ´óѧ¾¼ÃѧԺ¸±Ôº³¤¼æµÚ¶þ½ìÖйú±´Ò¶Ë¹¼ÆÁ¿¾¼ÃѧÂÛ̳Ö÷ϯÀîÓ½ÌÊÚÒ²·îÉÏÁËÈÈÁÒÕæÖ¿µÄÖ´ǡ£ÀîÔº³¤ÔÚÖ´ÇÖаµÊ¾£¬±¾´ÎÂÛּ̳Ôڴһ¸ö¿í·º»¥»»ºÍºÏ×÷µÄƽ̨£¬Í¨¹ý¶ÈÏí¾ÑéºÍ֪ʶ£¬ÎÒÃÇÄܹ»¹²Í¬Ë÷Çó±´Ò¶Ë¹²½ÖèÔÚ¾¼Ãѧ×êÑкÍʵ¼ÊÖеÄDZÁ¦£¬Õ⽫ΪÎÒÃÇ´ò¿ª¸üÁÉÀ«µÄ×êÑÐÁìÓò£¬²¢ÇÒÔÚÃæ¶ÔÌôսʱÌṩ¸ü¾ß¶´²ìÁ¦µÄ½â¾ö¹æ»®¡£Ëæºó£¬ÏßϲÎÓëÂÛ̳µÄ¼Î±öºÏÓ°ÁôÏë¡£

ÉÏÎçµÄ×ÚÖ¼»ã±¨»·½ÚÓÉÀîÓ½ÌÊÚÖ÷³Ö¡£Õâ´ÎÂÛ̳ԼÇëÁËÈýλÔÚ±´Ò¶Ë¹¼ÆÁ¿¾¼Ã×êÑÐÁìÓòÓÐןÜÉîѧÊõÔìÒèµÄ³ÛÃûѧÕߣ¬±ðÀëÊÇÈÕ±¾¹úÁ¢Õþ²ß×êÑдóѧԺ´óѧ£¨GRIPS£©µÄRoberto Leon-Gonzalez½ÌÊÚ¡¢Î÷Äϲƾ´óѧµÄ³£½úÔ´½ÌÊÚÓëÖйúÈËÃñ´óѧµÄÀîÓ½ÌÊÚ¡£




Roberto Leon-Gonzalez½ÌÊڵĻ㱨Ö÷ÌâÊÇ¡°Approximate Factor Models with a Common Multiplicative Factor for Stochastic Volatility¡±¡£Leon-Gonzalez½ÌÊÚÉî¿Ì×êÑй«¹²³Ë·¨Òò×ÓËæ»úµßô¤ÂÊÄ£ÐÍ£¬Ìá³öÄæGamma¹ý³ÌµÄCSVÄ£ÐÍ£¬Í¨¹ý¶Ô±ÈÈÕ±¾¡¢°ÍÎ÷¡¢ÃÀ¹úºÍÓ¢¹ú4¸ö¹ú¶ÈµÄʵ֤Á˾ֲû·¢£¬¸ÃÄ£ÐÍÓëÆäËûCSVÄ£ÐÍÏà±ÈÓµÓиüºÃµÄÔ¤²â¾«¶È£¬ÏÔʾ³öÆäÔÚºê¹Û¾¼ÃºÍ½ðÈÚÁìÓòµÄ׿Խ»úÄÜ£¬Îª±´Ò¶Ë¹¼ÆÁ¿¾¼Ãѧ·¢Õ¹¹±Ï×ÁËÐÂÊӽǡ£
³£½úÔ´½ÌÊڵĻ㱨Ö÷ÌâÊÇ¡°Exploring Excellence: Bayesian Penalized Empirical Likelihood and MCMC Sampling¡±£¬Ö¼ÔÚ̽Çó±´Ò¶Ë¹³ÍÖξÑéËÆÈ»µÄв½ÖèÂÛ¿ò¼Ü¡£³£½ÌÊÚÔڻ㱨ÖÐÌá³öÁËÁ½Öֹ滮£¬µÚÒ»ÖÖÊÇͨ¹ýµ÷½ÚÀ¸ñÀÊÈÕ³Ë×ӵIJ½ÖèÀ´ÓÐЧµØÑ¡ÔñÄ£ÐÍǰÌᣬµÚ¶þÖÖÊÇͨ¹ýÓÐЧ½µÎ¬µÄ²½ÖèÀ´¿Ë·þ±´Ò¶Ë¹ÀûÓÃÉè¼Æ²ÉÑù¹æ»®ÖйÌÓеÄÄÑÌâ¡£¸Ã×êÑÐÌṩÁËÒ»Öֽýݶø¸ßЧµÄ²½Ö裬¼ÓÇ¿Á˾ÑéËÆÈ»²½ÖèÔÚͳ¼Æ´§¶ÈÖеĺÏÓÃÐÔ£¬Îª×êÑÐÈËÔ±ºÍÆäËûѧÕßÌṩÏàʶ¾ö¸´ÔÓÎÊÌâµÄв½Öè¡£

ÀîÓ½ÌÊڵĻ㱨Ö÷ÌâÊÇ¡°Risk of Predictive Distributions and Model Comparison on Misspeci?ed Model¡±¡£Àî½ÌÊÚÖ¸³ö£¬¶ÔÓÚ¿ÉÄÜ´æÔÚÃýÎóÉ趨µÄÄ£ÐÍ£¬´ÓÔ¤²âµÄ½Ç¶ÈÀ´¿´£¬Í¨³£ÓÐÈýÖÖ·ÖÆçµÄÔ¤²âÉ¢²¼¿É¹©ºòѡʹÓ㬼´²åÈëʽԤ²âÉ¢²¼¡¢Í¨ÀýµÄ±´Ò¶Ë¹Ô¤²âÉ¢²¼ÒÔ¼°Muller£¨2013£©Ìá³öµÄ¼Ð²ã±´Ò¶Ë¹ºóÑéÔ¤²âÉ¢²¼¡£ÔÚK-LËðʧº¯ÊýÏ£¬Àî½ÌÊںͺÏ×÷ÕßÖ¤ÁËÈ»¼Ð²ã±´Ò¶Ë¹Ô¤²âÉ¢²¼±ÈͨÀý±´Ò¶Ë¹Ô¤²âÉ¢²¼ÓµÓиüµÍµÄ½¥½ü·çÏÕ£¬Ìá³öÁË´æÔÚÃýÎóÉ趨Ç龰ϵÄÐÅÏ¢×¼Ôò£¬²¢Í¨¹ýÔÚ¾¼ÃѧºÍ½ðÈÚÁìÓòµÄʵ֤·ÖÎöչʾÁËÆäÏÖʵÀûÓá£


ÏÂÎçµÄÁ½³¡·ÖÂÛ̳ͬʱ½øÐУ¬¹²ÓÐÊ®¶þλר¼ÒѧÕß±ðÀëÐû½²ÁËËûÃǵÄ×îÐÂ×êÑгɾ͡£·ÖÂÛ̳һÓÉб¦GGÄßÖÐнÌÊÚÖ÷³Ö¡£»ª¶«Ê¦·¶´óѧµÄÌÀÒø²Å½ÌÊÚ½éÉÜÁË»ùÓÚ´ó¹æÄ£Ç°Õ°ÐÔ¶ÓÁи´ÔÓ×ÝÏòÊý¾ÝµÄÏø´·çÏÕÆÀ¹ÀÓëÔ¤²âÄ£Ð͵ijÉÁ¢£¬ÔÚ±´Ò¶Ë¹µÄ¿ò¼ÜÏÂʹÓò´ËɻعéÀ´Ô¤²âÒßÇ鹿ģµÄ±ä¶¯Ç÷Ïò¡£ºþÄÏ´óѧµÄºò³É嫽ÌÊÚ½éÉÜÁËËûÃÇÍŶӹØÓÚ´øÓÐÏßÐÔÔ¼ÊøµÄ´óÐͽṹÏòÁ¿×ԻعéÄ£Ð͵ı´Ò¶Ë¹¹À¼Æ²½ÖèµÄ×îнøÕ¹¡£Õã½´óѧµÄÎéÖÞ²©Ê¿½éÉÜÁËËûÃǹØÓÚºóÑéÔ¤²âPÖµµÄ½¥½ü±Æ½üºÍ»ùÓÚºóÑéµÄWaldÐͲî¾àµÄ×êÑгɾ͡£Ä«¶û±¾´óѧµÄËÎÓ½ÌÊڻ㱨ÁËËûÔÚÓµÓнṹ±äµãµÄ±´Ò¶Ë¹·Ç²ÎÊýÄ£Ð͵ıä·Ö´§¶È×êÑÐÁìÓòµÄнøÕ¹¡£ÖйúÈËÃñ´óѧµÄÕÅÔ´²©Ê¿½éÉÜÁË»ùÓÚ±´Ò¶Ë¹Ä£ÐÍÑ¡ÔñµÄ×ۺϽÚÔì²½Öè·ÖÎö£¬²¢½«ÆäÀûÓÃÓÚ×êÑÐÖйú·¿µØ²ú˰¶Ôס·¿×âÁÞ¼ÛÖµµÄÓ°Ï졣б¦GGµÄ´÷ε²©Ê¿½éÉÜÁ˹ØÓÚKelly×¼ÔòÔÚÂ½Ðø¹¦·òÄÚµÄ×îÓŽâÎÊÌ⣬֤ÁËÈ»ÔÚ±´Ò¶Ë¹ÊÓ½ÇÏÂKelly×¼ÔòÔÚÂ½Ðø¹¦·òÓµÓÐ×îÓŽ⣬²¢½áºÏÏÖʵÊý¾Ý½²ÁËÈ»Bayesian-Kelly¶¯Ì¬×ʲúÅäÖõÄÓźñÐÔÔÚ²ÖλѡÔñÉϽøÐÐÓÅ»¯¡£


·ÖÂÛ̳¶þÓÉÑïÖÝ´óѧÍõ±ó½ÌÊÚÖ÷³Ö¡£Ìì½ò²Æ¾´óѧÁõÀÔì½½ÌÊÚ½éÉÜÁËÆäÍŶӻùÓÚ±´Ò¶Ë¹Éî¶È½ø½¨µÄÊý×ÔìջݽðÈÚÐÅ´û·çÏÕ¼ø±ðÓëÔ¤¾¯µÄ×êÑгɾͣ¬ÒÔÌáÉýÆÕ»Ý½ðÈÚÐÅ´û·çÏÕʶ´ËÍâÕýÈ·ÐÔºÍʵʱÐÔ£¬ÓÅ»¯ÐÅ´û·çÏÕÔ¤¾¯»úÔìÌá³öÓ¦¶ÔÕ½Êõ¡£ºþÄÏ´óѧÀÐǽÌÊÚ×êÑÐÁ˸ôÒ¹Êг¡ÒÑʵÏÖµßô¤ÐÔ£¬Í¨¹ý¶ÔÈÕÄں͸ôÒ¹ÒÑʵÏÖµßô¤ÂʽøÐнáºÏ½¨Ä££¬Åú×¢¸ôÒ¹µßô¤ÂʺÍËæºóµÄÈÕÄÚµßô¤ÂÊÖ®¼ä´æÔÚºÜÇ¿µÄÓÆ¾ÃÐÔ¡£½ËÕʦ·¶´óѧÁõÅô·É½ÌÊÚÌá³öÁË»ùÓÚ¾ùÖµÖÐÖµºÍ±´Ò¶Ë¹×ԾٵĸĽøBaggingËã·¨ÒÔ¿Ë·þ´«Í³Bagging¼¯³É»Ø¹éËã·¨µÄ¾ÛºÏģʽ¶ÔÒì³£Öµ½ÏΪÃô¸ÐµÄȱµã¡£Ê×¶¼¾¼ÃÒµÎñ´óѧÍôÏëÁá½ÌÔ±³ÁмìÑéÁËIV»Ø¹éÄ£ÐÍ£¬²¢¹Ø×¢²»ÆëÈ«¹¤¾ß±äÁ¿¡£Í¨¹ýÔÚÅÅËûÐÔÏÞ¶ÈǰÌáÉÏÊ©¼ÓÏÈÑ飬³ÉÁ¢ÁËÓÃÓÚ²ÎÊý¼ø±ð¡¢²»È·¶¨ÐÔÁ¿»¯ºÍIV±ÈÁ¦µÄͳһ±´Ò¶Ë¹¿ò¼Ü¡£Õã½´óѧ³ÂÒÝ·²²©Ê¿ÔÚËæ»úÌùÏÖÒò×Ó¿ò¼ÜÏÂ×êÑÐÁËÖйú¹«Ë¾Õ®È¯Êг¡ºá½ØÃæÊÕÒæ±ä¶¯µÄ¾ö¶¨³É·Ö¡£Í¨¹ý¸ßάÊý¾ÝµÄ±´Ò¶Ë¹²½Ö裬½èÖú¾¼ÃÇý¶¯µÄ¼â·åºÍ°å×´ÏÈÑéÀ´¼ø±ðSDFµÄ¹Ø¼ü×é³É²¿ÃÅ¡£Ð±¦GGÓÚ¶÷ƽ²©Ê¿½éÉÜÁËÒ»ÖÖеĽðÈÚ¼«²îÐòÁзÇÏßÐÔ·ÖÎöÄ£ÐÍ¡ª¡ªº¯ÊýϵÊýǰÌá×Իع鼫²îÄ£ÐÍ£¬À©´óÁËÖ÷Á÷µÄ·Ç¶Ô³Æ¼«²îµßô¤ÂÊÄ£ÐÍ£¬ÌṩÁËÒ»¸ö¸ü¾ßÊÊÓ¦ÐԵĿò¼ÜÀ´¿Ì»·ÇÏßÐÔÌØµã¡£
±¾´ÎÂÛ̳µÄ»ã±¨ÖÊÁ¿ºÜ¸ß£¬Ñ§ÕßÃÇ´ÓѧÊõ×êÑÓעʵ¼Ê¼ÛÖµµÈ½Ç¶È¶Ô±´Ò¶Ë¹¼ÆÁ¿¾¼ÃѧµÄ·¢Õ¹ºÍÀûÓýøÐÐÁË̽Çó£¬Îª²Î»áÈËÔ±ºÍÌý¶à´øÀ´ÁËÒ»³¡¸ßÖÊÁ¿Ñ§ÊõÊ¢Ñ磬Óë»áÈËÔ±ÊÕ³ÉÂúÂú£¡