19,868 research outputs found
Estimating the Counterparty Risk Exposure by using the Brownian Motion Local Time
In recent years, the counterparty credit risk measure, namely the default
risk in \emph{Over The Counter} (OTC) derivatives contracts, has received great
attention by banking regulators, specifically within the frameworks of
\emph{Basel II} and \emph{Basel III.} More explicitly, to obtain the related
risk figures, one has first obliged to compute intermediate output functionals
related to the \emph{Mark-to-Market} (MtM) position at a given time T being a positive, and finite, time horizon. The latter implies an
enormous amount of computational effort is needed, with related highly time
consuming procedures to be carried out, turning out into significant costs. To
overcome latter issue, we propose a smart exploitation of the properties of the
(local) time spent by the Brownian motion close to a given value
Memristors for the Curious Outsiders
We present both an overview and a perspective of recent experimental advances
and proposed new approaches to performing computation using memristors. A
memristor is a 2-terminal passive component with a dynamic resistance depending
on an internal parameter. We provide an brief historical introduction, as well
as an overview over the physical mechanism that lead to memristive behavior.
This review is meant to guide nonpractitioners in the field of memristive
circuits and their connection to machine learning and neural computation.Comment: Perpective paper for MDPI Technologies; 43 page
Sticky prices and monetary policy : evidence from disaggregated U.S. data
This paper uses factor-augmented vector autoregressions (FAVAR) estimated using a large data set to disentangle fluctuations in disaggregated consumer and producer prices which are due to macroeconomic factors from those due to sectorial conditions. This allows us to provide consistent estimates of the effects of US monetary policy on disaggregated prices. While sectorial prices respond quickly to sector-specific shocks, we find that for a large number of price series, there is a significant delay in the response of prices to monetary policy shocks. In addition, price responses display little evidence of a “price puzzle,” contrary to existing studies based on traditional VARs. The observed dispersion in the reaction of producer prices is relatively well explained by the degree of market power, as predicted by models with monopolistic competition. JEL Classification: E32, E5
Inferring short-term volatility indicators from Bitcoin blockchain
In this paper, we study the possibility of inferring early warning indicators
(EWIs) for periods of extreme bitcoin price volatility using features obtained
from Bitcoin daily transaction graphs. We infer the low-dimensional
representations of transaction graphs in the time period from 2012 to 2017
using Bitcoin blockchain, and demonstrate how these representations can be used
to predict extreme price volatility events. Our EWI, which is obtained with a
non-negative decomposition, contains more predictive information than those
obtained with singular value decomposition or scalar value of the total Bitcoin
transaction volume
On the interplay between multiscaling and stocks dependence
We find a nonlinear dependence between an indicator of the degree of
multiscaling of log-price time series of a stock and the average correlation of
the stock with respect to the other stocks traded in the same market. This
result is a robust stylized fact holding for different financial markets. We
investigate this result conditional on the stocks' capitalization and on the
kurtosis of stocks' log-returns in order to search for possible confounding
effects. We show that a linear dependence with the logarithm of the
capitalization and the logarithm of kurtosis does not explain the observed
stylized fact, which we interpret as being originated from a deeper
relationship.Comment: 19 pages, 8 figures, 9 table
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