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Emese Lazar

Publications and source records attributed to Emese Lazar.

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Measures of Model Risk in Continuous-time Finance Models

Measuring model risk is required by regulators on financial and insurance markets. We separate model risk into parameter estimation risk and model specification risk, and we propose expected shortfall type model risk measures applied to Levy jump models and affine jump-diffusion models. We investigate the impact of parameter estimation risk and model specification risk on the models' ability to capture the joint dynamics of stock and option prices. We estimate the parameters using Markov chain Monte Carlo techniques, under the risk-neutral probability measure and the real-world probability measure jointly. We find strong evidence supporting modeling of price jumps.

econ.EM

Analytic Moments for GARCH Processes

For a GJR-GARCH specification with a generic innovation distribution we derive analytic expressions for the first four conditional moments of the forward and aggregated returns and variances. Moment for the most commonly used GARCH models are stated as special cases. We also the limits of these moments as the time horizon increases, establishing regularity conditions for the moments of aggregated returns to converge to normal moments. Our empirical study yields excellent approximate predictive distributions from these analytic moments, thus precluding the need for time-consuming simulations.

q-fin.ST

On the Continuous Limit of Weak GARCH

We prove that the symmetric weak GARCH limit is a geometric mean-reverting stochastic volatility process with diffusion determined by kurtosis of physical log returns; this provides an improved fit to implied volatility surfaces. When log returns are normal the limit coincides with Nelson's limit. The limit is unique, unlike strong GARCH limits, because assumptions about convergence of model parameters is unnecessary -- parameter convergence is uniquely determined by time-aggregation of the weak GARCH process.

stat.ME