D:\sbornik\...\tpel.DVI Mathematical Problems of Computer Science 36, 70{78, 2012. N ew Repr esentation for N on Ruin P r obability of I nsur ance M odel with Rent Contr acts and I ts Application for Assessment of Cr itical Risks A r t a k R . Ma r t ir o s ya n Institute for Informatics and Automation Problems of NAS of RA e-mail: artakm81@inbox.ru Abstract The paper considers the particular solution of one integro-di®erential equation of insurance risk theory. New representation for that solution is found, which is used for assessment of critical risks of the insurance companies that conduct purely rent operations. The critical risks are found in the case of a heavy tra±c and regular tail variation of the insurance premiums distribution function. Keywords: Insurance, rent, regular variation, heavy tra±c, critical risks, integro- di®erential equation. Refer ences [1 ] L . Ta ka c h , Combinatorial methods in the random process theory ( Mir , Mo s c o w, 1 9 7 1 ) . [2 ] T. S a xe n , On the probability of ruin in the collective risk theory for insurance enterprises with only negative risk sums, ( S ka n d . A kt ., 3 1 , p p . 1 9 9 - 2 2 8 , 1 9 4 8 ) . [3 ] T. S a xe n , Sur les mouvements aleatoires et le probleme de ruine de la theorie du risque collective, ( S o c . S c i. Fe n n . Co m m . P h ys . Ma t h ., 1 6 , p p . 1 - 5 5 , 1 9 5 1 ) . [4 ] A . R . Ma r t ir o s ya n , " Cr it ic a l r is ks in m o d e ls o f c o lle c t ive in s u r a n c e " , Actuary, N 3 , 2 0 0 9 ( in R u s s ia n , s e e www:actuaries:ru=magazine=?SECT IONI D = 4 1 1 ) . [5 ] A . G. S h o lo m ic kiy, " R is k t h e o r y: c h o ic e a t t h e a n c e r t a in t y a n d r is k m o d e lin g " , M .:, 2 0 0 5 . [6 ] E . S e n e t a , R egularly varying functions ( N a u ka , Mo s c o w, 1 9 8 5 ) . [7 ] A . M. S e d le t s kii, Analytic fourier transforms and exponential approximations, S e r ie s : Co n t e m p o r a r y Ma t h e m a t ic s , Fu n d a m e n t a l D ir e c t io n s 5 ( Mo s c o w, 2 0 0 3 ) . [8 ] V . Fe lle r , Introduction to probability theory and its applications 2 ( Mir , Mo s c o w, 1 9 8 4 ) . [9 ] A . R . Ma r t ir o s ya n , \ Th e a s ym p t o t ic a n a lys is o f t h e c h a r a c t e r is t ic s o f t h e in s u r a n c e m o d e ls in c r it ic a l s it u a t io n s " , D is s e r t a t io n o f c a n d id a t e o f p h ys ic a l a n d m a t h e m a t - ic a l s c ie n c e s , Y e r e va n , 2 0 0 8 ( s e e E le c t r o n ic jo u r n a l d i®e r e n t ia l e qu a t io n s a n d c o n - t r o l p r o c e s s e s , h t t p :/ / www.m a t h .s p b u .r u / d i®jo u r n a l/ R U / n u m b e r s / 2 0 0 8 .3 / is s u e .h t m l, o r h t t p :/ / www.p r o r e c t o r .o r g / a vt o r s / m a r t ir o s ya n / d is s .p d f ) . [1 0 ] E . A . D a n ie lia n , " L im it t h e o r e m s fo r wa it in g t im e in s in g le -c h a n n e l s is t e m s " , R eports of Academy of Sciences of Armenia, vo l. L X X I, N 3 , p p . 1 2 9 -1 3 5 , 1 9 8 0 . 7 0 A. Martirosyan 7 1 [1 1 ] V . G. S a a kya n , " R a n d o m c h o ic e d is c ip lin e in m o d e ls ¡! Mrj ¡! Grj 1 j1 " , M .: D issertation for physical and mathematical sciences, 1 9 8 5 . [1 2 ] E . A . D a n ie lia n , R . N . Ch it c h ya n , " Mu lt id im e n s io n a l lim it t h e o r e m s fo r t h e wa it in g t im e in p r io r it y qu e u e s o f ¡! Mrj ¡! Grj1 j1 t yp e " , Acta Cybernetica, vo l. 5 , Fa s c 3 , p p . 3 2 5 -3 4 3 , S z e g e d , 1 9 8 1 . è»Ýï³Ý»ñÇ å³Ûٳݳ·ñ»ñáí ³å³Ñáí³·ñ³Ï³Ý Ùá¹»ÉÇ ãëݳÝϳóÙ³Ý Ñ³í³Ý³Ï³ÝáõÃÛ³Ý Ýáñ Ý»ñϳ۳óáõÙ ¨ Ýñ³ ÏÇñ³éáõÃÛáõÝÁ ÏñÇïÇÏ³Ï³Ý éÇëÏ»ñÇ ·Ý³Ñ³ïÙ³Ý Ýå³ï³Ïáí ². سñïÇñáëÛ³Ý ²Ù÷á÷áõÙ Ðá¹í³ÍáõÙ ¹Çï³ñÏíáõÙ ¿ ³å³Ñáí³·ñ³Ï³Ý éÇëÏ»ñÇ ï»ëáõÃÛ³Ý Ù»Ï ÇÝï»- ·ñá¹Çý»ñ»ÝóÇ³É Ñ³í³ë³ñÙ³Ý Ù³ëݳÏÇ ÉáõÍáõÙÁ: ²Û¹ ÉáõÍÙ³Ý Ñ³Ù³ñ ·ïÝí³Í ¿ Ýáñ Ý»ñϳ۳óáõÙ, áñÝ û·ï³·áñÍí»É ¿ ÙdzÛÝ é»Ýï³Ý»ñÇ ·áñͳñùÝ»ñáí ½µ³ÕíáÕ ³å³Ñáí³·ñ³Ï³Ý ÁÝÏ»ñáõÃÛ³Ý ÏñÇïÇÏ³Ï³Ý éÇëÏ»ñÇ ·Ý³Ñ³ïÙ³Ý Ñ³Ù³ñ: ÎñÇïÇ- Ï³Ï³Ý éÇëÏ»ñÁ ·ïÝí³Í »Ý ͳÝñ³µ»éÝí³ÍáõÃÛ³Ý ¨ ϳÝáݳí³ñ ÷á÷áËíáÕ åáã»ñáí ³å³Ñáí³·ñ³Ï³Ý í׳ñÝ»ñÇ µ³ßËÙ³Ý ¹»åùáõÙ: Íîâîå ïðåäñòàâëåíèå äëÿ âåðîÿòíîñòè íåðàçîðåíèÿ ñòðàõîâîé ìîäåëè ñ äîãîâîðàìè ñâÿçàííûìè ñ ðåíòàìè è åå ïðèìåíåíèåì ïðè îöåíêå êðèòè÷åñêèõ ðèñêîâ À. Ìàðòèðîñÿí Àííîòàöèÿ  ðàáîòå ðàññìîòðåíî ÷àñòíîå ðåøåíèå îäíîãî èíòåãðîäèôôåðåíöèàëíîãî óðàâíåíèÿ òåîðèè ñòðàõîâîãî ðèñêà. Äëÿ ýòîãî ðåøåíèÿ íàéäåíî íîâîå ïðåäñòàâëåíèå, êîòîðîå ïðèíèìàåòñÿ ïðè îöåíêå êðèòè÷åñêèõ ðèñêîâ ñòðàõîâûõ êîìïàíèé, çàíèìàþøèõñÿ îïåðàöèÿìè, ñâÿçàííûìè ñ îáû÷íîé ðåíòîé. Êðèòè÷åñêèå ðèñêè íàéäåíû â ñëó÷àÿõ êðèòè÷åñêîé çàãðóçêè è ïðè ïðàâèëüíîì èçìåíåíèè õâîñòîâ ðàñïðåäåëåíèé ñòðàõîâûõ âûïëàò.