![]() The paper adds to previous works by providing a new model that combines two strands of literature: (i) Lévy-type extensions of standard diffusion models, and (ii) switching processes which can incorporate different market regimes. Empirical results demonstrate that the newly introduced model outperforms a set of benchmark models. The key is that the parameters of a Lévy process may vary depending on the state of a Markov chain. The paper provides an empirical study of the dynamics of a range of financial variables such as equity and commodity indices by introducing the regime-switching Lévy model. Clearly, such an approach is useful when a series is thought to undergo shifts from one type of behavior to another and back again, but where the ‘forcing variable’ that causes the regime shifts is unobservable. They constitute an optimal inference on the latent state of the economy, whereby probabilities are assigned to the unobserved regimes ‘expansion’ and ‘contraction’ conditional on the available information set. Regimes constructed in this way are an important instrument for interpreting business cycles using Markov-switching models. Cai (1994) and Hamilton and Susmel (1994) introduce Markov-switching models to estimate high- and low-volatility regimes in financial data. They have also been utilized to capture volatility in financial markets. Markov-switching models have been widely used in economics and finance since Hamilton (1989a, b) introduced them to estimate regime- or state-dependent variables. In addition, given the presence of jumps in the data, pure jump models are preferred by users as they are easier to handle for practical applications such as derivatives pricing or real-life problems such as valuation of insurance contracts or real-option valuation ( Martzoukos and Trigeorgis 2002). ![]() As emphasized in Aït-Sahalia and Jacod (2009a, b), the presence of jumps and/or the absence of a continuous martingale also has important implications for portfolio choice or risk management activities. Adding a jump component to a continuous component (leading to a mixture model) or considering a jump component only allows to fit the data better than with a continuous component only. ![]() In recent years, many pure jump or jump-diffusion models have been suggested in the economic and statistical literatures to deal with (possibly large) discontinuities in price processes see the reference textbook by Cont and Tankov (2004). ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |