000 03766nam a22005055i 4500
001 978-1-4614-8788-3
003 DE-He213
005 20140220082503.0
007 cr nn 008mamaa
008 131213s2014 xxu| s |||| 0|eng d
020 _a9781461487883
_9978-1-4614-8788-3
024 7 _a10.1007/978-1-4614-8788-3
_2doi
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aK
_2bicssc
072 7 _aBUS061000
_2bisacsh
082 0 4 _a330.015195
_223
100 1 _aCarmona, René.
_eauthor.
245 1 0 _aStatistical Analysis of Financial Data in R
_h[electronic resource] /
_cby René Carmona.
250 _a2nd ed. 2014.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2014.
300 _aXVII, 588 p. 187 illus., 37 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Texts in Statistics,
_x1431-875X
505 0 _aUnivariate Data Distributions -- Heavy Tail Distributions -- Dependence and Multivariate Data Exploration -- Parametric Regression -- Local and Nonparametric Regression -- Time Series Models -- Multivariate Time Series, Linear Systems and Kalman Filtering -- Nonlinear Time Series: Models and Simulation -- Appendices -- Indices.
520 _aAlthough there are many books on mathematical finance, few deal with the statistical aspects of modern data analysis as applied to financial problems. This book fills this gap by addressing some of the most challenging issues facing any financial engineer. It shows how sophisticated mathematics and modern statistical techniques can be used in concrete financial problems. Concerns of risk management are addressed by the control of extreme values, the fitting of distributions with heavy tails, the computation of values at risk (VaR), and other measures of risk. Data description techniques such as principal component analysis (PCA), smoothing, and regression are applied to the construction of yield and forward curve. Nonparametric estimation and nonlinear filtering are used for option pricing and earnings prediction. The book is intended for undergraduate students majoring in financial engineering, or graduate students in a Master in finance or MBA program. Because it was designed as a teaching vehicle, it is sprinkled with practical examples using market data, and each chapter ends with exercises. Practical examples are solved in the computing environment of R. They illustrate problems occurring in the commodity and energy markets, the fixed income markets as well as the equity markets, and even some new emerging markets like the weather markets. The book can help quantitative analysts by guiding them through the details of statistical model estimation and implementation. It will also be of interest to researchers wishing to manipulate financial data, implement abstract concepts, and test mathematical theories, especially by addressing practical issues that are often neglected in the presentation of the theory.
650 0 _aStatistics.
650 0 _aFinance.
650 0 _aMathematical statistics.
650 0 _aEconomics
_xStatistics.
650 1 4 _aStatistics.
650 2 4 _aStatistics for Business/Economics/Mathematical Finance/Insurance.
650 2 4 _aStatistical Theory and Methods.
650 2 4 _aQuantitative Finance.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781461487876
830 0 _aSpringer Texts in Statistics,
_x1431-875X
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4614-8788-3
912 _aZDB-2-SMA
999 _c92246
_d92246