885,820 research outputs found
Extraction of the underlying structure of systematic risk from non-Gaussian multivariate financial time series using independent component analysis: Evidence from the Mexican stock exchange
Regarding the problems related to multivariate non-Gaussianity of financial time series, i.e., unreliable results in extraction of underlying risk factors -via Principal Component Analysis or Factor Analysis-, we use Independent Component Analysis (ICA) to estimate the pervasive risk factors that explain the returns on stocks in the Mexican Stock Exchange. The extracted systematic risk factors are considered within a statistical definition of the Arbitrage Pricing Theory (APT), which is tested by means of a two-stage econometric methodology. Using the extracted factors, we find evidence of a suitable estimation via ICA and some results in favor of the APT.Peer ReviewedPostprint (published version
Finite-size effect and the components of multifractality in financial volatility
Many financial variables are found to exhibit multifractal nature, which is
usually attributed to the influence of temporal correlations and fat-tailedness
in the probability distribution (PDF). Based on the partition function approach
of multifractal analysis, we show that there is a marked finite-size effect in
the detection of multifractality, and the effective multifractality is the
apparent multifractality after removing the finite-size effect. We find that
the effective multifractality can be further decomposed into two components,
the PDF component and the nonlinearity component. Referring to the normal
distribution, we can determine the PDF component by comparing the effective
multifractality of the original time series and the surrogate data that have a
normal distribution and keep the same linear and nonlinear correlations as the
original data. We demonstrate our method by taking the daily volatility data of
Dow Jones Industrial Average from 26 May 1896 to 27 April 2007 as an example.
Extensive numerical experiments show that a time series exhibits effective
multifractality only if it possesses nonlinearity and the PDF has impact on the
effective multifractality only when the time series possesses nonlinearity. Our
method can also be applied to judge the presence of multifractality and
determine its components of multifractal time series in other complex systems.Comment: 9 RevTex pages including 9 eps figures. Comments and suggestions are
warmly welcom
What does financial volatility tell us about macroeconomic fluctuations?
This paper provides an extensive analysis of the predictive ability of financial volatility measures for economic activity. We construct monthly measures of aggregated and industry-level stock volatility, and bond market volatility from daily returns. We model log financial volatility as composed of a long-run component that is common across all series, and a short-run component. If volatility has components, volatility proxies are characterized by large measurement error, which veils analysis of their fundamental information and relationship with the economy. We find that there are substantial gains from using the long term component of the volatility measures for linearly projecting future economic activity, as well as for forecasting business cycle turning points. When we allow for asymmetry in the long-run volatility component, we find that it provides early signals of upcoming recessions. In a real-time out-of-sample analysis of the last recession, we find that these signals are concomitant with the first signs of distress in the financial markets due to problems in the housing sector around mid-2007 and the implied chronology is consistent with the crisis timeline.Realized Volatility, Business Cycles, Forecasting, Dynamic Factor Models, Markov Switching
TVICA - Time Varying Independent Component Analysis and Its Application to Financial Data
Source extraction and dimensionality reduction are important in analyzing high dimensional and complex financial time series that are neither Gaussian distributed nor stationary. Independent component analysis (ICA) method can be used to factorize the data into a linear combination of independent compo- nents, so that the high dimensional problem is converted to a set of univariate ones. However conventional ICA methods implicitly assume stationarity or stochastic homogeneity of the analyzed time series, which leads to a low accu- racy of estimation in case of a changing stochastic structure. A time varying ICA (TVICA) is proposed here. The key idea is to allow the ICA filter to change over time, and to estimate it in so-called local homogeneous intervals. The question of how to identify these intervals is solved by the LCP (local change point) method. Compared to a static ICA, the dynamic TVICA pro- vides good performance both in simulation and real data analysis. The data example is concerned with independent signal processing and deals with a portfolio of highly traded stocks.Adaptive Sequential Testing, Independent Component Analysis, Local Homogeneity, Signal Processing, Realized Volatility.
How to Identify Investor's types in real financial markets by means of agent based simulation
The paper proposes a computational adaptation of the principles underlying
principal component analysis with agent based simulation in order to produce a
novel modeling methodology for financial time series and financial markets.
Goal of the proposed methodology is to find a reduced set of investor s models
(agents) which is able to approximate or explain a target financial time
series. As computational testbed for the study, we choose the learning system L
FABS which combines simulated annealing with agent based simulation for
approximating financial time series. We will also comment on how L FABS s
architecture could exploit parallel computation to scale when dealing with
massive agent simulations. Two experimental case studies showing the efficacy
of the proposed methodology are reported.Comment: 18 pages, in pres
Growth Volatility and Financial Repression: Time Series Evidence from India
The main objective of this paper is to explore the determinants of private consumption volatility in India. While considerable effort has been expended on the examining the relationship between growth and volatility, we focus on financial repression and private consumption volatility in India. Using annual time series data, the results show that the implementation of financial repressionist policies are strongly associated with lower consumption volatility in India. The results remain robust after controlling for a wide range of macroeconomic shocks and variables. Additional analysis which involves examining each component of private consumption provides further evidence to support this finding. The presence of a threshold effect suggests that the benefits of financial openness in dampening consumption volatility can only be reaped when India becomes sufficiently liberalized.Growth volatility; financial repression; India
Time Dependent Relative Risk Aversion
Risk management and the thorough understanding of the relations between financial markets and the standard theory of macroeconomics have always been among the topics most addressed by researchers, both financial mathematicians and economists. This work aims at explaining investorsâ behavior from a macroeconomic aspect (modeled by the investorsâ pricing kernel and their relative risk aversion) using stocks and options data. Daily estimates of investorsâ pricing kernel and relative risk aversion are obtained and used to construct and analyze a three-year long time-series. The first four moments of these time-series as well as their values at the money are the starting point of a principal component analysis. The relation between changes in a major index level and implied volatility at the money and between the principal components of the changes in relative risk aversion is found to be linear. The relation of the same explanatory variables to the principal components of the changes in pricing kernels is found to be log-linear, although this relation is not significant for all of the examined maturities.risk aversion, pricing kernels, time dependent preferences
Temporal Evolution of Financial Market Correlations
We investigate financial market correlations using random matrix theory and
principal component analysis. We use random matrix theory to demonstrate that
correlation matrices of asset price changes contain structure that is
incompatible with uncorrelated random price changes. We then identify the
principal components of these correlation matrices and demonstrate that a small
number of components accounts for a large proportion of the variability of the
markets that we consider. We then characterize the time-evolving relationships
between the different assets by investigating the correlations between the
asset price time series and principal components. Using this approach, we
uncover notable changes that occurred in financial markets and identify the
assets that were significantly affected by these changes. We show in particular
that there was an increase in the strength of the relationships between several
different markets following the 2007--2008 credit and liquidity crisis.Comment: 15 pages, 10 figures, 1 table. Accepted for publication in Phys. Rev.
E. v2 includes additional section
Consistent estimation of high-dimensional factor models when the factor number is over-estimated
A high-dimensional -factor model for an -dimensional vector time series
is characterised by the presence of a large eigengap (increasing with )
between the -th and the -th largest eigenvalues of the covariance
matrix. Consequently, Principal Component (PC) analysis is the most popular
estimation method for factor models and its consistency, when is correctly
estimated, is well-established in the literature. However, popular factor
number estimators often suffer from the lack of an obvious eigengap in
empirical eigenvalues and tend to over-estimate due, for example, to the
existence of non-pervasive factors affecting only a subset of the series. We
show that the errors in the PC estimators resulting from the over-estimation of
are non-negligible, which in turn lead to the violation of the conditions
required for factor-based large covariance estimation. To remedy this, we
propose new estimators of the factor model based on scaling the entries of the
sample eigenvectors. We show both theoretically and numerically that the
proposed estimators successfully control for the over-estimation error, and
investigate their performance when applied to risk minimisation of a portfolio
of financial time series
A Method for Visualizing Multivariate Time Series Data
Visualization and exploratory analysis is an important part of any data analysis and is made more challenging when the data are voluminous and high-dimensional. One such example is environmental monitoring data, which are often collected over time and at multiple locations, resulting in a geographically indexed multivariate time series. Financial data, although not necessarily containing a geographic component, present another source of high-volume multivariate time series data. We present the mvtsplot function which provides a method for visualizing multivariate time series data. We outline the basic design concepts and provide some examples of its usage by applying it to a database of ambient air pollution measurements in the United States and to a hypothetical portfolio of stocks
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