An ultra-high-frequency data model on financial asset price movements is considered. This model allows us to relate the changes in price volatility and trading activity to news or information arrivals. The underlying event arrivals process is assumed to be unobserved by the market agents. Then, the study of the risk-minimizing hedging-strategies for derivatives under partial information bring us to a nonlinear filtering problem. Taking into account the weak form of market efficiency, under some Markovianity assumptions, classical filtering techniques are used to evaluate the risk-minimizing hedging-strategies.

A Partially Observed Ultra-High-Frequency Data Model: Risk Minimizing-Hedging

TARDELLI, PAOLA
2007

Abstract

An ultra-high-frequency data model on financial asset price movements is considered. This model allows us to relate the changes in price volatility and trading activity to news or information arrivals. The underlying event arrivals process is assumed to be unobserved by the market agents. Then, the study of the risk-minimizing hedging-strategies for derivatives under partial information bring us to a nonlinear filtering problem. Taking into account the weak form of market efficiency, under some Markovianity assumptions, classical filtering techniques are used to evaluate the risk-minimizing hedging-strategies.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11697/13054
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 2
social impact