Results 1  10
of
38
Misspecifying GARCHM processes
 Complex Systems
, 1995
"... We consider the relationships between ARCHtype and Stochastic Volatility models. A new class of volatility models, called Generalized Bilinear Stochastic Volatility, is described following an approach that transforms an initial GARCHM process. The focus here is on the interpretation of some simula ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
We consider the relationships between ARCHtype and Stochastic Volatility models. A new class of volatility models, called Generalized Bilinear Stochastic Volatility, is described following an approach that transforms an initial GARCHM process. The focus here is on the interpretation of some
Robustness of the Black and Scholes Formula
 Mathematical Finance
, 1998
"... Consider an option on a stock whose volatility is unknown and stochastic. An agent assumes this volatility to be a specific function of time and the stock price, knowing that this assumption may result in a misspecification of the volatility. However, if the misspecified volatility dominates the tru ..."
Abstract

Cited by 16 (1 self)
 Add to MetaCart
Consider an option on a stock whose volatility is unknown and stochastic. An agent assumes this volatility to be a specific function of time and the stock price, knowing that this assumption may result in a misspecification of the volatility. However, if the misspecified volatility dominates
a Consequense of Misspecified Models?
"... 2 Many studies have tried to explain the stock market value premium identified by Fama and French and Rosenberg, Reid and Lanstein. To the proponents of conventional asset pricing theory the value premium, measured by HmL (high booktomarket minus low booktomarket), is a bit of a dilemma. This is ..."
Abstract
 Add to MetaCart
is to examine the value premium on the Swedish stock market by applying the Sharpe Lintner CAPM as well as two additional models, LCAPM and HCPAM. The models are empirically tested in an unconditional and conditional manner where the latter uses changes in industrial production and the implied volatility
Predictive Density Construction and Accuracy Testing with Multiple Possibly Misspecified Diffusion Models
, 2009
"... This paper develops tests for comparing the accuracy of predictive densities derived from (possibly misspecified) diffusion models. In particular, we first outline a simple simulation based framework for constructing predictive densities for onefactor and stochastic volatility models. Then, we cons ..."
Abstract

Cited by 4 (4 self)
 Add to MetaCart
This paper develops tests for comparing the accuracy of predictive densities derived from (possibly misspecified) diffusion models. In particular, we first outline a simple simulation based framework for constructing predictive densities for onefactor and stochastic volatility models. Then, we
On IGARCH and convergence of the QMLE for misspecified GARCH models. Working paper
, 2008
"... Abstract: We address the IGARCH puzzle by which we understand the fact that a GARCH(1,1) model fitted by quasi maximum likelihood estimation to virtually any financial dataset exhibit the property that α ̂ + β ̂ is close to one. We prove that if data is generated by certain types of continuous time ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
stochastic volatility models, but fitted to a GARCH(1,1) model one gets that α ̂ + β ̂ tends to one in probability as the sampling frequency is increased. Hence, the paper suggests that the IGARCH effect could be caused by misspecification. The result establishes that the stochastic sequence of QMLEs do
How often to sample a continuoustime process in the presence of market microstructure noise
 Review of Financial Studies
, 2005
"... In theory, the sum of squares of log returns sampled at high frequency estimates their variance. When market microstructure noise is present but unaccounted for, however, we show that the optimal sampling frequency is finite and derives its closedform expression. But even with optimal sampling, usi ..."
Abstract

Cited by 156 (14 self)
 Add to MetaCart
, using say 5min returns when transactions are recorded every second, a vast amount of data is discarded, in contradiction to basic statistical principles. We demonstrate that modeling the noise and using all the data is a better solution, even if one misspecifies the noise distribution. So the answer is
Optimal robust meanvariance hedging in incomplete financial markets
 Journal of Mathematical Sciences
"... Abstract. Optimal Brobust estimate is constructed for multidimensional parameter in drift coefficient of diffusion type process with small noise. Optimal meanvariance robust (optimal Vrobust) trading strategy is find to hedge in meanvariance sense the contingent claim in incomplete financial mar ..."
Abstract

Cited by 3 (1 self)
 Add to MetaCart
market with arbitrary information structure and misspecified volatility of asset price, which is modelled by multidimensional continuous semimartingale. Obtained results are applied to stochastic volatility model, where the model of latent volatility process contains unknown multidimensional parameter
Optimal meanvariance robust hedging under asset price model misspecification
 Georgian Math. J
"... Abstract. The problem of constructing robust optimal in the meanvariance sense trading strategies is considered. The approach based on the notion of sensitivity of a risk functional of the problem w.r.t. small perturbation of asset price model parameters is suggested. The optimal meanvariance robu ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
variance robust trading strategies are constructed for onedimensional diffusion models with misspecified volatility. 2000 Mathematics Subject Classification: 60G22, 62F35, 91B28. Key words and phrases: Robust meanvariance hedging, misspecified asset price models. 1. Introduction and statement of the problem
Estimation of a StochasticVolatility JumpDiffusion Model
, 1999
"... Abstract: This paper makes two contributions: (1) it presents estimates of a continuoustime stochasticvolatility jumpdiffusion process (SVJD) using a simulationbased estimator, and (2) it shows that misspecified models that allow for jumps, but not stochastic volatility, can give very bad estima ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
Abstract: This paper makes two contributions: (1) it presents estimates of a continuoustime stochasticvolatility jumpdiffusion process (SVJD) using a simulationbased estimator, and (2) it shows that misspecified models that allow for jumps, but not stochastic volatility, can give very bad
Results 1  10
of
38