Stochastic calculus for finance

By Steven E. Shreve. 2004

511 citations recorded by the source corpus.

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Citations in the corpus

Risk analysis and valuation of life insurance contracts: Combining actuarial and financial approaches (2011, unknown): “1 3 ** * ),(2, 2 1 )(,,)( )()()()()(ln σσρσσ σλσ σμβ which implies 13 For Itô processes )(),( 21 tXtX and a sufficiently smooth function ( )21,, xxtf one obtains ( ) jji jxxj jxt dXdXfdXfdtfXXtdf jjj ∑∑ == ++= 2 1, , 2 121 2 1,, , cf. e.g., Shreve (2004).”
Pricing Volatility Swaps Under Heston's Stochastic Volatility Model with Regime Switching (2007, unknown): “gif"/> are consistent with those in Shreve ( 2004 ).”
The Worst Case for Real Options (2010, unknown): “15 or [32], Chap.”
Random Choice and Learning (2019, background): “Since the continuous random process accumulates quadratic variation, the decision maker can perfectly estimate the parameters jij after an arbitrarily small time interval (Shreve 2004).”
Portfolio sensitivity to changes in the maximum and the maximum drawdown (2010, background): “Its value at time t can be written as Vt ¼ e rðT tÞE½DT j F t : ð2Þ The Markov property of process S allows us to express Vt as a function of St, Mt and Dt (Shreve 2004, pp. 107 and 109): Vt ¼ vðt,St,Mt,DtÞ ¼ e rðT tÞE½DT j St,Mt,Dt : ð3Þ The sensitivities of the forward to the running maximum and…”
War Signals: A Theory of Trade, Trust and Conflict (2011, unknown): “Applying methods from stopping-time theory (see, e.g., Shreve 2004), we establish the following Proposition.”
Waiting for News in the Market for Lemons (2011, unknown): “Thus, the density of Mt for all y > 0 is fMt(y) = 2 √ 2πφ2t e − 1 2φ2t (y− 1 2 φ2t)2 − eyΦ ( −y − 12φ2t φ √ t ) (59) which can be obtained from (Shreve, 2004, p. 114), where Φ(·) denotes the standard normal CDF.”
A review of numerical methods for nonlinear partial differential equations (2012, background): “Instead, there is a growing role for nonlinear stochastic PDEs [112, 146, 42] with a wide range of applications, from financial models and data assimilation in atmospheric sciences to material sciences and biological models; see [118, 185, 116, 121, 154, 169, 166] and the references therein.”