Results 31 to 40 of about 52,895 (269)

Pricing European Vulnerable Options with Jumps and Stochastic Default Obstacles Barrier under Regime Switching

open access: yesMathematics, 2023
In this paper, we propose an enhanced model for pricing vulnerable options. Specifically, our model assumes that parameters such as interest rates, jump intensity, and asset value volatility are governed by an observable continuous-time finite-state ...
Xiangdong Liu, Zanbin Zhang
doaj   +1 more source

Asian Option Pricing with Monotonous Transaction Costs under Fractional Brownian Motion

open access: yesJournal of Applied Mathematics, 2013
Geometric-average Asian option pricing model with monotonous transaction cost rate under fractional Brownian motion was established. The method of partial differential equations was used to solve this model and the analytical expressions of the Asian ...
Di Pan   +3 more
doaj   +1 more source

Mirror and synchronous couplings of geometric Brownian motions [PDF]

open access: yesStochastic Processes and their Applications, 2014
The paper studies the question of whether the classical mirror and synchronous couplings of two Brownian motions minimise and maximise, respectively, the coupling time of the corresponding geometric Brownian motions. We establish a characterisation of the optimality of the two couplings over any finite time horizon and show that, unlike in the case of ...
Saul D. Jacka   +2 more
openaire   +2 more sources

APPROXIMATION METHODS FOR INHOMOGENEOUS GEOMETRIC BROWNIAN MOTION [PDF]

open access: yesInternational Journal of Theoretical and Applied Finance, 2019
We present an accurate and easy-to-compute approximation of the transition probabilities and the associated Arrow-Debreu (AD) prices for the inhomogeneous geometric Brownian motion (IGBM) model for interest rates, default intensities or volatilities. Through this procedure, dubbed exponent expansion, transition probabilities and AD prices are obtained
LUCA CAPRIOTTI   +2 more
openaire   +2 more sources

On the integral of geometric Brownian motion [PDF]

open access: yesAdvances in Applied Probability, 2003
This paper studies the law of any real powers of the integral of geometric Brownian motion over finite time intervals. As its main results, an apparently new integral representation is derived and its interrelations with the integral representations for these laws originating by Yor and by Dufresne are established.
openaire   +4 more sources

Number of paths versus number of basis functions in American option pricing

open access: yes, 2003
An American option grants the holder the right to select the time at which to exercise the option, so pricing an American option entails solving an optimal stopping problem.
Glasserman, Paul, Yu, Bin
core   +6 more sources

Delay geometric Brownian motion in financial option valuation [PDF]

open access: yes, 2012
Motivated by influential work on complete stochastic volatility models, such as Hobson and Rogers [11], we introduce a model driven by a delay geometric Brownian motion (DGBM) which is described by the stochastic delay differential equation dSðtÞ ¼ mðSðt
Mao, Xuerong, Sabanis, Sotirios
core   +1 more source

Diffusion in a weakly random Hamiltonian flow [PDF]

open access: yes, 2004
We consider the motion of a particle governed by a weakly random Hamiltonian flow. We identify temporal and spatial scales on which the particle trajectory converges to a spatial Brownian motion.
Komorowski, T., Ryzhik, L.
core   +2 more sources

G-Brownian Motion as Rough Paths and Differential Equations Driven by G-Brownian Motion

open access: yes, 2013
The present paper is devoted to the study of sample paths of G-Brownian motion and stochastic differential equations (SDEs) driven by G-Brownian motion from the view of rough path theory.
B.M. Hambly   +24 more
core   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

open access: yesAdvanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone   +11 more
wiley   +1 more source

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