Simulated Likelihood Estimation of Diffusions with an Application to Exchange Rate Dynamics in Incomplete Markets

By Michael W. Brandt, Pedro Santa-clara. 2002

289 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/0238ddc8465ca5adaa06a114a2cc2b6bc9bfe561

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Tim Bollerslev
2. Torben G. Andersen
3. Garland B. Durham
4. V. Todorov
5. N. Shephard
6. Yacine Aït-Sahalia
7. Liuren Wu
8. Kenneth J. Singleton

In Finance

1. Tim Bollerslev
2. V. Todorov
3. Torben G. Andersen
4. N. Shephard
5. Liuren Wu
6. Garland B. Durham
7. Yacine Aït-Sahalia
8. George Tauchen

Citations in the corpus

• Maximum-likelihood estimation for diffusion processes via closed-form density expansions (2013, methodology): “In particular, among various numerical methods, Lo [46] proposed to employ a numerical solution of Kolmogorov equation for transition density; Pedersen [55], Brandt and SantaClara [21], Durham and Gallant [25], Stramer and Yan [60], Beskos and Roberts [17], Beskos et al.”
• Discrete-Time Dynamic Term Structure Models with Generalized Market Prices of Risk (2010, unknown): “…Simonato (1999), Ait-Sahalia (1999, 2002), and Duffie, Pedersen, and Singleton (2003); the Monte Carlo based approximations of Pedersen (1995) and Brandt and Santa-Clara (2001)); and the simulation-based method-ofmoments estimators proposed by Duffie and Singleton (1993) and Gallant and Tauchen…”
• Simulation-Based Estimation of Contingent-Claims Prices (2009, unknown): “this area of research includes Lo (1988) and Brandt and Santa-Clara (2002). Recently,”
• A Bayesian approach to bandwidth selection for multivariate kernel regression with an application to state-price density estimation (2009, background): “Address: Monash University, Victoria 3800, Australia.”
• Estimating the Parameters of Stochastic Volatility Models Using Option Price Data (2015, methodology): “The density f (Sk | Vk, Vk−1) is approximated using the method suggested by Pedersen (1995) and Brandt and Santa-Clara (2002).”
• Estimation of Dynamic Term Structure Models (2012, methodology): “For example, Campbell and Shiller (1991) ̄nd that when the slope of the term structure is steep, expected excess returns to bonds are high.”
• The Impact of Jumps in Volatility and Returns (2003, unknown): “Other methods that have been used to estimate models with stochastic volatility and jumps include EMM, simulated maximum likelihood (Brandt and Santa-Clara (2002), Durham and Gallant (2001), Piazessi (2001)), calibration (Bates (1996, 2000), Bakshi, Cao, and Chen (1997)), and the implied-state GMM…”
• The jump-risk premia implicit in options: evidence from an integrated time-series study $ (2002, background): “See Brandt and Santa-Clara (2001) for an application to over-the-counter derivatives.”
• Nonlinear Mean Reversion in the Short-Term Interest Rate (2003, methodology): “Figure 2 illustrates the comparison of highfrequency data augmentation with two classical methods, the simulated method of moments [e.g., Duffie and Singleton (1993)] and simulated maximum likelihood [e.g., Brandt and Santa-Clara (2002) ].”
• Estimation for discretely observed diffusions using transform functions (2003, methodology): “Pedersen (1995) and Brandt & Santa-Clara (2001) independently derived a simulation based method for approximating the likelihood function.”
• Seeing the Wood for the Trees: A Critical Evaluation of Methods to Estimate the Parameters of Stochastic Differential Equations (2006, methodology): “The method of “simulated” maximum likelihood (SML) is developed independently9 in Pedersen (1995) and Brandt and Santa-Clara (2002) with an alternative algorithm given in Hurn, Lindsay and Martin (2003). In each of the methods to be described, SML aims to obtain an estimate of f(x |Xk; θ) by simulat”
• Applied Stochastic Differential Equations (2019, unknown): “7 Simulated Likelihood Methods The basic idea in simulated likelihood methods (Pedersen, 1995; Brandt and Santa-Clara, 2002; Iacus, 2008) is to use a simple simulation to approximate the transition density p.”
• Stochastic Volatility: Origins and Overview (2008, methodology): “Later refinements and applications for SV diffusion models include Elerian, Chib, and Shephard (2001), Brandt and Santa-Clara (2002), Durham and Gallant (2002), and Durham (2003).”
• Estimation of Continuous-Time Models with an Application to Equity Volatility Dynamics (2006, unknown): “Citing this reason, Durham (2003), Durham and Gallant (2002), and Brandt and Santa-Clara (2002) have argued in favor of simulation-based methods.”