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Intraday stochastic volatility in discrete price changes : the dynamic Skellam model

Koopman, Siem Jan; Lit, Rutger; Lucas, André - 2015

We introduce a dynamic Skellam model that measures stochastic volatility from high-frequency tick-by-tick discrete stock price changes. The likelihood function for our model is analytically intractable and requires Monte Carlo integration methods for its numerical evaluation. The proposed...

Persistent link: https://www.econbiz.de/10011295740

Observation-driven filters for timeseries with stochastic trends and mixed causal non-causal dynamics

Blasques, Francisco; Koopman, Siem Jan; Mingoli, Gabriele - 2023

This paper proposes a novel time-series model with a non-stationary stochastic trend, locally explosive mixed causal non-causal dynamics and fat-tailed innovations. The model allows for a description of financial time-series that is consistent with financial theory, for a decomposition of the...

Persistent link: https://www.econbiz.de/10014380706

Modeling common bubbles : a mixed causal non-causal dynamic factor model

Mingoli, Gabriele - 2024

This paper introduces a novel dynamic factor model designed to capture common locally explosive episodes, also known as common bubbles, within large-dimensional, potentially non-stationary time series. The model leverages a lower-dimensional set of factors exhibiting locally explosive behavior...

Persistent link: https://www.econbiz.de/10015133628

A score-driven filter for causal regression models with time-varying parameters and endogenous regressors

Blasques, Francisco; Stegehuis, Noah - 2024

This paper proposes a score-driven model for filtering time-varying causal parameters through the use of instrumental variables. In the presence of suitable instruments, we show that we can uncover dynamic causal relations between variables, even in the presence of regressor endogeneity which...

Persistent link: https://www.econbiz.de/10014496538

Improving score-driven density forecasts with an application to implied volatility surface dynamics

Zou, Xia; Lin, Yicong; Lucas, André - 2025

Point forecasts of score-driven models have been shown to behave at par with those of state-space models under a variety of circumstances. We show, however, that density rather than point forecasts of plain-vanilla score-driven models substantially underperform their state-space counterparts in...

Persistent link: https://www.econbiz.de/10015408437

A simple test for causality in volatility

Chang, Chia-Lin; McAleer, Michael - 2016

An early development in testing for causality (technically, Granger non-causality) in the conditional variance (or … volatility) associated with financial returns, was the portmanteau statistic for non-causality in variance of Cheng and Ng (1996 …). A subsequent development was the Lagrange Multiplier (LM) test of non-causality in the conditional variance by Hafner …

Persistent link: https://www.econbiz.de/10011556246

Option prices and model-free measurement of implied herd behavior in stock markets

Linders, Daniël; Dhaene, Jan; Schoutens, Wim - 2015 - This version: December 20, 2014

In this paper, we introduce two classes of indices which can be used to measure the market perception concerning the degree of dependency that exists between a set of random variables, representing different stock prices at a fixed future date. The construction of these measures is based on the...

Persistent link: https://www.econbiz.de/10010464790

Abnormal returns, risk, and options in large data sets

Caserta, Silvia; Daníelsson, Jón; Vries, Casper G. de - 1998

Large data sets in finance with millions of observations have becomewidely available. Such data sets enable the construction of reliablesemi-parametric estimates of the risk associated with extreme pricemovements. Our approach is based on semi-parametric statisticalextreme value analysis, and...

Persistent link: https://www.econbiz.de/10011299966

Detecting serial dependence in tail events : a test dual to the BDS test

Diks, Cees G. H. - 2002

Persistent link: https://www.econbiz.de/10001689451

Modeling and forecasting S&P 500 volatility : long memory, structural breaks and nonlinearity

Martens, Martin; Dijk, Dick van; Pooter, Michiel de - 2004

Persistent link: https://www.econbiz.de/10002128301