Showing 1 - 10 of 174
We develop a new method that detects jumps nonparametrically in financial time series and significantly outperforms the … generated by a process that experiences both jumps and volatility bursts. As a result, the network learns how to disentangle the …: we obtain fewer spurious detection and identify a larger number of true jumps. When applied to real data, our approach …
Persistent link: https://www.econbiz.de/10012181300
trader identifiers at a tick transaction level. Jumps are frequent events and they cluster in time. The order flow imbalance … and the preponderance of aggressive traders, as well as a widening of the bid-ask spread predict them. Jumps have short …
Persistent link: https://www.econbiz.de/10011762219
distributional properties of the realized Amihud, including jumps, clustering, and leverage effects …
Persistent link: https://www.econbiz.de/10014238265
We develop a methodology for detecting asset bubbles using a neural network. We rely on the theory of local martingales in continuous-time and use a deep network to estimate the diffusion coefficient of the price process more accurately than the current estimator, obtaining an improved detection...
Persistent link: https://www.econbiz.de/10012181227
We present a careful analysis of possible issues of the application of the self-excited Hawkes process to high-frequency financial data and carefully analyze a set of effects that lead to significant biases in the estimation of the "criticality index'' n that quantifies the degree of endogeneity...
Persistent link: https://www.econbiz.de/10010257507
The paper proposes a framework for large-scale portfolio optimization which accounts for all the major stylized facts of multivariate financial returns, including volatility clustering, dynamics in the dependency structure, asymmetry, heavy tails, and nonellipticity. It introduces a so-called...
Persistent link: https://www.econbiz.de/10011410659
This paper proposes a machine learning approach to estimate physical forward default intensities. Default probabilities are computed using artificial neural networks to estimate the intensities of the inhomogeneous Poisson processes governing default process. The major contribution to previous...
Persistent link: https://www.econbiz.de/10012419329
A non-Gaussian multivariate regime switching dynamic correlation model for fi nancial asset returns is proposed. It incorporates the multivariate generalized hyperbolic law for the conditional distribution of returns. All model parameters are estimated consistently using a new two-stage...
Persistent link: https://www.econbiz.de/10012051878
A common method of valuing the equity in highly leveraged transactions is the flows-to-equity method. When applying this method various formulas can be used to calculate the time-varying cost of equity. In this paper we show that some commonly used formulas are inconsistent with the assumptions...
Persistent link: https://www.econbiz.de/10008797682
We develop a dynamic model of banking to assess the effects of liquidity and leverage requirements on banks' insolvency risk. In this model, banks face taxation, flotation costs of securities, and default costs and maximize shareholder value by making their financing, liquid asset holdings, and...
Persistent link: https://www.econbiz.de/10011293576