Modelling Extremal Events: for Insurance and FinanceBoth in insurance and in finance applications, questions involving extremal events (such as large insurance claims, large fluctuations in financial data, stock market shocks, risk management, ...) play an increasingly important role. This book sets out to bridge the gap between the existing theory and practical applications both from a probabilistic as well as from a statistical point of view. Whatever new theory is presented is always motivated by relevant real-life examples. The numerous illustrations and examples, and the extensive bibliography make this book an ideal reference text for students, teachers and users in the industry of extremal event methodology. |
Contents
I | 1 |
II | 21 |
III | 22 |
IV | 28 |
V | 36 |
VI | 37 |
VII | 39 |
VIII | 44 |
LXI | 323 |
LXII | 325 |
LXIII | 327 |
LXIV | 345 |
LXV | 348 |
LXVI | 352 |
LXVII | 358 |
LXVIII | 371 |
IX | 49 |
XI | 53 |
XII | 59 |
XIII | 60 |
XIV | 70 |
XV | 82 |
XVI | 88 |
XVII | 96 |
XVIII | 103 |
XIX | 106 |
XX | 113 |
XXI | 114 |
XXII | 120 |
XXIII | 128 |
XXIV | 130 |
XXV | 134 |
XXVI | 138 |
XXVII | 152 |
XXVIII | 168 |
XXIX | 181 |
XXX | 182 |
XXXI | 196 |
XXXII | 204 |
XXXIII | 209 |
XXXIV | 219 |
XXXV | 220 |
XXXVI | 225 |
XXXVII | 226 |
XXXVIII | 232 |
XXXIX | 237 |
XL | 238 |
XLI | 242 |
XLII | 247 |
XLIII | 248 |
XLIV | 250 |
XLV | 254 |
XLVI | 260 |
XLVII | 263 |
XLVIII | 264 |
XLIX | 277 |
L | 283 |
LI | 290 |
LIII | 294 |
LIV | 303 |
LV | 305 |
LVI | 307 |
LVII | 309 |
LVIII | 316 |
LIX | 317 |
LX | 321 |
LXIX | 372 |
LXX | 378 |
LXXI | 381 |
LXXII | 386 |
LXXIII | 393 |
LXXIV | 403 |
LXXV | 413 |
LXXVI | 418 |
LXXVII | 424 |
LXXVIII | 430 |
LXXIX | 431 |
LXXX | 433 |
LXXXI | 436 |
LXXXII | 439 |
LXXXIII | 444 |
LXXXIV | 449 |
LXXXV | 454 |
LXXXVI | 455 |
LXXXVII | 461 |
LXXXVIII | 473 |
LXXXIX | 481 |
XC | 483 |
XCI | 486 |
XCII | 493 |
XCIII | 498 |
XCIV | 503 |
XCV | 507 |
XCVI | 521 |
XCVII | 522 |
XCVIII | 526 |
XCIX | 527 |
C | 532 |
CI | 541 |
CII | 551 |
CV | 552 |
CVI | 553 |
CVIII | 554 |
CX | 555 |
CXII | 557 |
CXIII | 559 |
CXV | 561 |
CXVI | 562 |
CXVII | 564 |
CXVIII | 571 |
CXIX | 583 |
CXX | 587 |
CXXI | 591 |
627 | |
CXXIII | 643 |
Other editions - View all
Modelling Extremal Events: for Insurance and Finance Paul Embrechts,Claudia Klüppelberg,Thomas Mikosch No preview available - 2011 |
Common terms and phrases
a-stable applications assume asymptotic autocorrelations behaviour Borel sets Brownian motion condition constants cn Cramér-Lundberg defined Definition denotes density df F domain of attraction Embrechts Example exponential extreme value distribution extreme value theory Figure finance finite finite-dimensional distributions Gaussian generalised geometric Brownian motion Gumbel Gumbel distribution Haan heavy-tailed Hence Hill estimator homogeneous Poisson process iid rvs iid sequence instance integral Klüppelberg large deviations Lemma lim sup limit distribution linear process maxima maximum domain MDA(a mean excess function method normalised norming constants Notes and Comments P(Mn parameter Pareto Pareto distribution periodogram point process proof Proposition quantile random variables random walk regular variation regularly varying relation result sample paths Section SLLN stationary process stationary sequence stochastic processes subexponential sums Suppose tail threshold upper order statistics varying function vector weak convergence