Optimal Statistical Inference in Financial Engineering
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A01=Junichi Hirukawa
A01=Kenichiro Tamaki
A01=Masanobu Taniguchi
Arch Parameter
asymptotic inference for financial time series
Asymptotic Optimality
Author_Junichi Hirukawa
Author_Kenichiro Tamaki
Author_Masanobu Taniguchi
bond
Category=KFF
Central Limit Theorems
Concerned Time Series
Conditional Expectation
density
discount
Discount Bond
Discount Bond Price
discriminant analysis
Distribution Function
Edgeworth Expansion
eq_bestseller
eq_business-finance-law
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Fisher Information Measure
Forward Rate Curve
function
hypothesis testing
Joint Probability Density Function
MP Test
no arbitrage pricing
Optimal Statistical Inference
probability
Probability Density Function
process
random
Random Variables
Sample Autocorrelation Function
Slutsky's Lemma
spectral
Spectral Density Matrix
stochastic
stochastic modeling
Stochastic Processes
Stock Log Return
time series estimation
Ump Test
UMPU Test
UMVU Estimator
value at risk
variable
Product details
- ISBN 9781584885917
- Weight: 657g
- Dimensions: 156 x 234mm
- Publication Date: 26 Nov 2007
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Hardback
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Until now, few systematic studies of optimal statistical inference for stochastic processes had existed in the financial engineering literature, even though this idea is fundamental to the field. Balancing statistical theory with data analysis, Optimal Statistical Inference in Financial Engineering examines how stochastic models can effectively describe actual financial data and illustrates how to properly estimate the proposed models.
After explaining the elements of probability and statistical inference for independent observations, the book discusses the testing hypothesis and discriminant analysis for independent observations. It then explores stochastic processes, many famous time series models, their asymptotically optimal inference, and the problem of prediction, followed by a chapter on statistical financial engineering that addresses option pricing theory, the statistical estimation for portfolio coefficients, and value-at-risk (VaR) problems via residual empirical return processes. The final chapters present some models for interest rates and discount bonds, discuss their no-arbitrage pricing theory, investigate problems of credit rating, and illustrate the clustering of stock returns in both the New York and Tokyo Stock Exchanges.
Basing results on a modern, unified optimal inference approach for various time series models, this reference underlines the importance of stochastic models in the area of financial engineering.
Masanobu Taniguchi, Junichi Hirukawa, Kenichiro Tamaki
