Modelling Security Market Events in Continuous Time: Intensity Based, Multivariate Point Process Models

By Clive G. Bowsher. 2007

424 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/128f884c82ff7badf3bfef767771db8401b8cf4d

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In Applied Mathematics

Citations in the corpus

Limit theorems for nearly unstable Hawkes processes (2013, methodology): “, see [14], in the context of value at risk estimation and to Bowsher, see [12], who jointly studied transaction times and midquote changes, using the Hawkes framework.”
Rough fractional diffusions as scaling limits of nearly unstable heavy tailed Hawkes processes (2015, methodology): “In the last years, the probabilistic and statistical analysis of Hawkes processes has known several interesting developments, driven by the recent use of Hawkes processes in various applied fields such as neurosciences [13, 33, 34, 36], sociology [9, 28, 41], criminology [30, 31], genome analysis [3”
Hawkes Processes in Finance (2015, methodology): “Fitted Hawkes D Nt Contracts T dt Φ(t) Comments Section 2 [15] Bowsher 1-2 Trade Stocks 2 months 1s 2-Exp + seasonality [18] Chavez et al. 1 Extreme ∆P Stocks 1 year 1ms 2-Exp Marked [42] Jaisson et al. 8 Level-I Futures 1 year 1µs NP Section 3 [2] Bacry et al. 1 Trade Futures 3.5 months 1ms NP 2 Pr”
Hawkes processes and their applications to finance: a review (2018, background): “Some people claim that Bowsher (2007) was the first (his original working paper appeared on the Internet in 2002).”
Affine Point Processes and Portfolio Credit Risk (2010, neutral): “For example, Bowsher [7] fits different Hawkes models to security trade data.”
Modeling financial contagion using mutually exciting jump processes (2015, positive): “In market microstructure, Bowsher (2007) employs them to jointly model transaction times and price changes at high frequency. Salmon and Tham (2007) study how trading activity at one maturity of the yield curve affects and is affected by trading at other maturities. Self-exciting models have recentl”
The role of volume in order book dynamics: a multivariate Hawkes process analysis (2016, background): “A proper account for inhibition in a Hawkes processes framework requires to abandon the linear specification (5) in favour of nonlinear formulations that ensure the positiveness of the intensity, while at the same time allowing the presence of negative valued kernels and thus inhibitory effects (Bré”
Properties of Realized Variance Under Alternative Sampling Schemes (2006, background): “…returns (e.g., Ball and Torous 1983; Carr, Geman, Madan, and Yor 2002; Maheu and McCurdy 2004) and high-frequency transaction data (e.g., Bowsher 2002; Large 2005; Rogers and Zane 1998; Rydberg and Shephard 2003) to the pricing of derivatives (e.g., Carr and Wu 2004; Cox and Ross 1976;…”
Variation, jumps, market frictions and high frequency data in financial econometrics (2005, neutral): “This is attractive as there is a great deal of literature which moves econometricians towards modelling prices as point processes (e.g. Engle and Russell (1998), Engle (2000) and Bowsher (2003)).”
Harnessing jump component for crude oil volatility forecasting in the presence of extreme shocks (2019, methodology): “Ait-Sahalia et al. (2015), Bowsher (2007), and Large (2007) all used Hawkes processes to model events in financial markets, such as trading activity, liquidity supply, and contagion across markets.”
Portfolio Choice in Markets with Contagion (2012, neutral): “, Brillinger (1988) and Ogata and Akaike (1982)), genome analysis (see Reynaud-Bouret and Schbath (2010)), credit derivatives (Errais et al. (2010)), to model transaction times and price changes at high frequency (Bowsher (2007)), trading activity at different maturities of the yield curve (Salmon a”
Non-parametric kernel estimation for symmetric Hawkes processes. Application to high frequency financial data (2011, background): “Applications can be found in the field of order arrival rate modeling [16,7,29], noise microstructure dynamics [2], volatility clustering [11], extreme values and VaR estimation [8] and credit modeling [12].”
Modelling microstructure noise with mutually exciting point processes (2011, background): “…and addressing directly the price dynamics at the tick level (see, for instance, Engle and Russell (1998) and Engle and Lunde (2001), who introduce the ACMACD model, or Bauwens and Hautsch (2006) and Bowsher (2007), who construct intensity-based point processes for modelling high-frequency data).”