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Computational Finance



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Autore: Stentoft Lars Visualizza persona
Titolo: Computational Finance Visualizza cluster
Pubblicazione: Basel, Switzerland, : MDPI - Multidisciplinary Digital Publishing Institute, 2020
Descrizione fisica: 1 electronic resource (259 p.)
Soggetto topico: Economics, finance, business & management
Soggetto non controllato: insurance
Solvency II
risk-neutral models
computational finance
asset pricing models
overnight price gaps
financial econometrics
mean-reversion
statistical arbitrage
high-frequency data
jump-diffusion model
instantaneous volatility
directional-change
seasonality
forex
bitcoin
S&amp
P500
risk management
drawdown
safe assets
securitisation
dealer behaviour
liquidity
bid–ask spread
least-squares Monte Carlo
put-call symmetry
regression
simulation
algorithmic trading
market quality
defined contribution plan
probability of shortfall
quadratic shortfall
dynamic asset allocation
resampled backtests
stochastic covariance
4/2 model
option pricing
risk measures
American options
exercise boundary
Monte Carlo
multiple exercise options
dynamic programming
stochastic optimal control
asset pricing
calibration
derivatives
hedging
multivariate models
volatility
Persona (resp. second.): StentoftLars
Sommario/riassunto: With the availability of new and more comprehensive financial market data, making headlines of massive public interest due to recent periods of extreme volatility and crashes, the field of computational finance is evolving ever faster thanks to significant advances made theoretically, and to the massive increase in accessible computational resources. This volume includes a wide variety of theoretical and empirical contributions that address a range of issues and topics related to computational finance. It collects contributions on the use of new and innovative techniques for modeling financial asset returns and volatility, on the use of novel computational methods for pricing, hedging, the risk management of financial instruments, and on the use of new high-dimensional or high-frequency data in multivariate applications in today’s complex world. The papers develop new multivariate models for financial returns and novel techniques for pricing derivatives in such flexible models, examine how pricing and hedging techniques can be used to assess the challenges faced by insurance companies, pension plan participants, and market participants in general, by changing the regulatory requirements. Additionally, they consider the issues related to high-frequency trading and statistical arbitrage in particular, and explore the use of such data to asses risk and volatility in financial markets.
Titolo autorizzato: Computational Finance  Visualizza cluster
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910557767003321
Lo trovi qui: Univ. Federico II
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