Time-varying Cross-correlation Research Articles

Measuring fiscal stress via fiscal vulnerability index: a case of Turkish economy

PurposeThis paper aims to develop a compound measure, which is fiscal vulnerability index, provides early warning signals of fiscal sustainability problems for Türkiye's economy.Design/methodology/approachThe index is constructed using twelve distinct fiscal indicators and applying the portfolio method, which considers the time-varying cross-correlation structure between the subindices.FindingsDynamics of the fiscal vulnerability index indicate that it accurately predicts to the well-known fiscal crisis occurring in Türkiye's recent history. As a result, such a compound measure should be used in the early identification of fiscal vulnerability in Türkiye.Originality/valueThe main contribution of this paper, relative to existing papers, is that a fiscal vulnerability index was constructed by employing the most contemporaneous method and evaluating its performance in terms of capturing historical stress periods.

Template Matching and Matrix Profile for Signal Quality Assessment of Carotid and Femoral Laser Doppler Vibrometer Signals.

Background: Laser-Doppler Vibrometry (LDV) is a laser-based technique that allows measuring the motion of moving targets with high spatial and temporal resolution. To demonstrate its use for the measurement of carotid-femoral pulse wave velocity, a prototype system was employed in a clinical feasibility study. Data were acquired for analysis without prior quality control. Real-time application, however, will require a real-time assessment of signal quality. In this study, we (1) use template matching and matrix profile for assessing the quality of these previously acquired signals; (2) analyze the nature and achievable quality of acquired signals at the carotid and femoral measuring site; (3) explore models for automated classification of signal quality.Methods: Laser-Doppler Vibrometry data were acquired in 100 subjects (50M/50F) and consisted of 4–5 sequences of 20-s recordings of skin displacement, differentiated two times to yield acceleration. Each recording consisted of data from 12 laser beams, yielding 410 carotid-femoral and 407 carotid-carotid recordings. Data quality was visually assessed on a 1–5 scale, and a subset of best quality data was used to construct an acceleration template for both measuring sites. The time-varying cross-correlation of the acceleration signals with the template was computed. A quality metric constructed on several features of this template matching was derived. Next, the matrix-profile technique was applied to identify recurring features in the measured time series and derived a similar quality metric. The statistical distribution of the metrics, and their correlates with basic clinical data were assessed. Finally, logistic-regression-based classifiers were developed and their ability to automatically classify LDV-signal quality was assessed.Results: Automated quality metrics correlated well with visual scores. Signal quality was negatively correlated with BMI for femoral recordings but not for carotid recordings. Logistic regression models based on both methods yielded an accuracy of minimally 80% for our carotid and femoral recording data, reaching 87% for the femoral data.Conclusion: Both template matching and matrix profile were found suitable methods for automated grading of LDV signal quality and were able to generate a quality metric that was on par with the signal quality assessment of the expert. The classifiers, developed with both quality metrics, showed their potential for future real-time implementation.

Time-varying cross-correlation between trading volume and returns in US stock markets

This paper used detrended cross-correlation analysis (DCCA) to study contemporaneous co-movements between trading volume and returns in US stock markets (Dow Jones, Nasdaq and Standard & Poor-500). It was found that cross-correlations are not constant, but exhibit important variations with time and scale (i.e., horizon). It was argued that the complexity of the behavior of cross-correlations is in line with the adaptive market hypothesis (AMH), which states that the behavior of the market participants evolves to adapt to changing market conditions. An interesting result is that cross-correlations are positive for early periods (from 1950 to late 2000s), and shifted to negative cross-correlations in the recent two decades, a transition that may be linked to changes in the long-term risk aversion of market participants.

Global commodity prices and inflation expectations

PurposeIn this paper, we study the effect of global energy and food inflation on household inflation expectations during the period 1988M01–2020M03 for a set of European economies.Design/methodology/approachWe use multifractal de-trended cross-correlation analysis to estimate the non-linear and time-varying cross-correlation. We provide additional robustness tests using the Autoregressive-Distributed Lag method.FindingsWe find that household inflation expectations, global energy inflation and global food inflation are all multifractal. We also find that the household inflation expectations, global energy inflation and global food inflation are positively correlated (i.e., they are persistent). However, household inflation expectations respond more when the volatility of the global energy inflation is lower than when the volatility is higher. The correlation between household inflation expectations and global food inflation does not depend on the level of volatility.Research limitations/implicationsFirst, paying attention to the global commodity inflation might help anchor inflation expectations better. It is so because Central Bank's efficacy in achieving price stability may be weakened if there is a relationship between commodity inflation and inflation expectation. This task would become even more difficult in the average inflation targeting regime than inflation targeting regime if actual inflation is persistently different from the target inflation. Second, our results also emphasize the importance of effective strategy for communicating to households about actual inflation, inflation target and keep them updated about how monetary policy functions.Originality/valueWe contribute to the literature by estimating the cross-correlation between household inflation expectations with the global commodity inflation, conditional to the volatility of the commodity inflation under consideration.

Measuring Systemic Financial Stress and its Impact on the Macroeconomy

This paper proposes a general statistical framework for systemic financial stress indexes. Several existing index designs can be represented as special cases. We introduce a daily variant of the ECB’s CISS for the euro area and the US. The CISS aggregates a representative set of stress indicators using their time-varying cross-correlations as systemic weights, like portfolio risk is computed from the risk characteristics of individual assets. A bootstrap algorithm delivers test statistics. A linear VAR shows that the Great Recession is mainly caused by CISS shocks, while their contribution to the COVID-19 crisis appears limited. A quantile VAR suggests particularly strong real effects of financial stress in the worst states of the economy.

Evaluating financial stress indicators: evidence from Indian data

Purpose The purpose of this study is to assess alternative financial stress indicators for India in terms of tracing crisis events, mapping with the business cycle and the macroeconomic effect of stress indices. Design/methodology/approach The study constructs the composite indicator of systemic stress of Hollo, Kremer and Lo Duca (2012) for India using two different methods for computing time-varying cross-correlation matrix, namely, exponentially weighted moving average (EWMA) and dynamic conditional correlation-generalized autoregressive conditional heteroscedasticity (DCC-GARCH). The derived indices are evaluated with widely used, equal variance and principal component weighting indices in terms of tracing stress events, mapping with the business cycles and the macroeconomic effect. For this purpose, the study identifies various episodes of financial stress and uses the business cycle dates in the sample covering from January 2001 to October 2018. Findings The results suggest that stress indices based on EWMA and DCC-GARCH accurately identify the well-known stress periods and capture the recession dates and show an adverse effect on economic activity. Primarily, the DCC-GARCH-based stress index emerges as a better indicator of stress because it efficiently locates all the major-minor events, traces the build-up of stress and reverts to the normal level during stable times. Practical implications The DCC-GARCH-based stress index is a very useful indicator for policymakers in regularly monitoring India’s financial conditions and providing timely identification of systemic stress to avoid adverse repercussion effects of the financial crisis. Originality/value The 2007–2008 financial crisis and subsequent recurrent instability in the financial markets highlighted the requirement for an appropriate financial stress indicator for a timely assessment of the system-wide financial stress. To the authors’ knowledge, this is the first study that incorporates the systemic nature of financial stress in the construction of stress indices for India and provides a holistic evaluation of the financial stress from an emerging country’s perspective.

MF-DCCA between molecular properties and aqueous solubility

In this paper, we propose a framework, based on Multi-Fractal De-trended Fluctuation Cross-Correlation Analysis (MF-DCCA), to investigate the cross-correlation features between the molecular properties and aqueous solubility using the data set of Delaney-processed widely used for Deep Learning (DL). The molecular properties of Minimum Degree, Number of H-Bond Donors, Number of Rings, Number of Rotatable bonds and Polar Surface Area have a weak power-law cross-correlation with aqueous solubility; the Molecular Weight has a weak long-range cross-correlation. Obvious oscillation of long-range cross-correlation captures through a sliding window approach between molecular properties and aqueous solubility based on the pseudo-time series. Experimental comparison of time-varying cross-correlation widths shows that window widths are more robust to time-varying Hurst exponent. This work can provide a reference for reducing the data dimension for neural network molecular property prediction tasks and help in high-accuracy predicting molecular aqueous solubility on DL.

A Novel 3D Non-Stationary Wireless MIMO Channel Simulator and Hardware Emulator

In this paper, a new WINNER+-based 3-D non-stationary geometry-based stochastic model (GBSM) for multiple-input multiple-output channels is proposed, as well as extended evolving algorithms of time-variant channel parameters. Meanwhile, important statistical properties of the channel model, i.e., time-variant autocorrelation function, time-variant cross-correlation function, and time-variant Doppler power spectrum density are derived and analyzed. Moreover, we propose an efficient hardware implementation method, namely, sum-of-frequency-modulation (SoFM) method, to generate non-stationary channel coefficients. By utilizing compact hardware architecture with SoFM modules, the proposed 3-D non-stationary GBSM is realized on a field-programmable gate array hardware platform. Simulations and hardware measurement results demonstrate that our proposed channel simulator and emulator can get more accurate and realistic Doppler frequency than those of the existing models. In addition, hardware measurements of statistical properties are also well consistent with the corresponding theoretical ones, which verify the correctness of both the hardware emulation scheme and theoretical derivations.

Systemic liquidity risk and portfolio theory: An application to the Italian financial markets

This paper introduces a coincident indicator of systemic liquidity risk in the Italian financial markets. In order to take account of the systemic dimension of liquidity stress, standard portfolio theory is used. Three sub-indices, that reflect liquidity stress in specific market segments, are aggregated in the systemic liquidity risk indicator in the same way as individual risks are aggregated in order to quantify overall portfolio risk. The aggregation takes account of the time-varying cross-correlations between the sub-indices, using a multivariate GARCH approach. This is able to capture abrupt changes in the correlations. We evaluate the indicator on its ability to match the results of a survey conducted among financial market experts to determine the most liquidity stressful events for the Italian financial markets. The results show that the systemic liquidity risk indicator accurately identifies events characterized by high systemic risk.

An Indicator of Systemic Liquidity Risk in the Italian Financial Markets

This paper introduces a coincident indicator of systemic liquidity risk in the Italian financial markets. In order to take account of the systemic dimension of liquidity stress, standard portfolio theory is used. Three sub-indices, that reflect liquidity stress in specific market segments, are aggregated in the systemic liquidity risk indicator in the same way as individual risks are aggregated in order to quantify overall portfolio risk. The aggregation takes account of the time-varying cross-correlations between the sub-indices, using a multivariate GARCH approach. This is able to capture abrupt changes in the correlations and makes it possible for the indicator to identify systemic liquidity events precisely. We evaluate the indicator on its ability to match the results of a survey conducted among financial market experts to determine the most liquidity stressful events for the Italian financial markets. The results show that the systemic liquidity risk indicator accurately identifies events characterized by high systemic risk, while not exaggerating the level of stress during calm periods.

Cross-Correlations between Energy and Emissions Markets: New Evidence from Fractal and Multifractal Analysis

We supply a new perspective to describe and understand the behavior of cross-correlations between energy and emissions markets. Namely, we investigate cross-correlations between oil and gas (Oil-Gas), oil and CO2(Oil-CO2), and gas andCO2(Gas-CO2) based on fractal and multifractal analysis. We focus our study on returns of the oil, gas, andCO2during the period of April 22, 2005–April 30, 2013. In the empirical analysis, by using the detrended cross-correlation analysis (DCCA) method, we find that cross-correlations for Oil-Gas, Oil-CO2, and Gas-CO2obey a power-law and are weakly persistent. Then, we adopt the method of DCCA cross-correlation coefficient to quantify cross-correlations between energy and emissions markets. The results show that their cross-correlations are diverse at different time scales. Next, based on the multifractal DCCA method, we find that cross-correlated markets have the nonlinear and multifractal nature and that the multifractality strength for three cross-correlated markets is arranged in the order of Gas-CO2 > Oil-Gas > Oil-CO2. Finally, by employing the rolling windows method, which can be used to investigate time-varying cross-correlation scaling exponents, we analyze short-term and long-term market dynamics and find that the recent global financial crisis has a notable influence on short-term and long-term market dynamics.

Cross-correlations between Renminbi and four major currencies in the Renminbi currency basket

We investigate the cross-correlations between Renminbi (CNY) and four major currencies (USD, EUR, JPY, and KRW) in the Renminbi currency basket, i.e., the cross-correlations of CNY–USD, CNY–EUR, CNY–JPY, and CNY–KRW. Qualitatively, using a statistical test in analogy to the Ljung-Box test, we find that cross-correlations significantly exist in CNY–USD, CNY–EUR, CNY–JPY, and CNY–KRW. Quantitatively, employing the detrended cross-correlation analysis (DCCA) method, we find that the cross-correlations of CNY–USD, CNY–EUR, CNY–JPY, and CNY–KRW are weakly persistent. We use the DCCA cross-correlation coefficient ρDCCA to quantify the level of cross-correlations and find the currency weight in the Renminbi currency basket is arranged in the order of USD>EUR>JPY >KRW. Using the method of rolling windows, which can capture the time-varying cross-correlation scaling exponents, we find that: (i) CNY and USD are positively cross-correlated over time, but the cross-correlations of CNY–USD are anti-persistent during the US sub-prime crisis and the European debt crisis. (ii) The cross-correlation scaling exponents of CNY-EUR have the cyclical fluctuation with a nearly two-year cycle. (iii) CNY–JPY has long-term negative cross-correlations, during the European debt crisis, but CNY and KRW are positively cross-correlated.

Cyclic Feature Detection With Sub-Nyquist Sampling for Wideband Spectrum Sensing

For cognitive radio networks, efficient and robust spectrum sensing is a crucial enabling step for dynamic spectrum access. Cognitive radios need to not only rapidly identify spectrum opportunities over very wide bandwidth, but also make reliable decisions in noise-uncertain environments. Cyclic spectrum sensing techniques work well under noise uncertainty, but require high-rate sampling which is very costly in the wideband regime. This paper develops robust and compressive wideband spectrum sensing techniques by exploiting the unique sparsity property of the two-dimensional cyclic spectra of communications signals. To do so, a new compressed sensing framework is proposed for extracting useful second-order statistics of wideband random signals from digital samples taken at sub-Nyquist rates. The time-varying cross-correlation functions of these compressive samples are formulated to reveal the cyclic spectrum, which is then used to simultaneously detect multiple signal sources over the entire wide band. Because the proposed wideband cyclic spectrum estimator utilizes all the cross-correlation terms of compressive samples to extract second-order statistics, it is also able to recover the power spectra of stationary signals as a special case, permitting lossless rate compression even for non-sparse signals. Simulation results demonstrate the robustness of the proposed spectrum sensing algorithms against both sampling rate reduction and noise uncertainty in wireless networks.

CISS - A Composite Indicator of Systemic Stress in the Financial System

This paper introduces a new indicator of contemporaneous stress in the financial system named Composite Indicator of Systemic Stress (CISS). Its specific statistical design is shaped according to standard definitions of systemic risk. The main methodological innovation of the CISS is the application of basic portfolio theory to the aggregation of five market-specific subindices created from a total of 15 individual financial stress measures. The aggregation accordingly takes into account the time-varying cross-correlations between the subindices. As a result, the CISS puts relatively more weight on situations in which stress prevails in several market segments at the same time, capturing the idea that financial stress is more systemic and thus more dangerous for the economy as a whole if financial instability spreads more widely across the whole financial system. Applied to euro area data, we determine within a threshold VAR model a systemic crisis-level of the CISS at which financial stress tends to depress real economic activity. Weekly updates of the CISS dataset at: sdw.ecb.europa.eu/browseSelection.do?node=9551138 JEL Classification: G01, G10, G20, E44

CISS - A Composite Indicator of Systemic Stress in the Financial System

This paper introduces a new indicator of contemporaneous stress in the financial system named Composite Indicator of Systemic Stress (CISS). Its specific statistical design is shaped according to standard definitions of systemic risk. The main methodological innovation of the CISS is the application of basic portfolio theory to the aggregation of five market-specific subindices created from a total of 15 individual financial stress measures. The aggregation accordingly takes into account the time-varying cross-correlations between the subindices. As a result, the CISS puts relatively more weight on situations in which stress prevails in several market segments at the same time, capturing the idea that financial stress is more systemic and thus more dangerous for the economy as a whole if financial instability spreads more widely across the whole financial system. Applied to euro area data, we determine within a * We thank Philipp Hartmann for inspiring and supporting this project throughout all stages. Philipp also invented the indicator’s name and its abbreviation CISS (pronounced like “kiss”). We thank Tommy Kostka for excellent research assistance and for several good ideas which helped improving the CISS. Very helpful comments from Geert Bekaert, Wolfgang Lemke, Simone Manganelli and an anonymous referee are gratefully acknowledged. We finally thank participants at the Euro Area Business Cycle Network conference “Econometric Modelling of Macro-Financial Linkages” in Florence and the 5th CSDA International Conference on Computational and Financial Econometrics in London for fruitful discussions and comments. However, the views expressed in this paper are those of the authors and do not necessarily reflect those of the European Central Bank, the Eurosystem or the Magyar Nemzeti Bank.  Corresponding author: Tel. +49 69 1344 7065.

The geometry of time-varying cross-correlation random fields

The goal of this paper is to assess the P -value of local maxima of time-varying cross-correlation random fields. The motivation for this comes from an electroencephalography (EEG) experiment, where one seeks connectivity between all pairs of voxels inside the brain at each time point of the recording window. In this way, we extend the results of [Cao, J., Worsley, K.J., 1999b. The geometry of correlation fields with an application to functional connectivity of the brain. The Annals of Applied Probability 9 (4), 1021–1057] by searching for high correlations not only over all pairs of voxels, but over all time points as well. We apply our results to an EEG data set of a face recognition paradigm. Our analysis determines those time instants for which there are significantly correlated regions involved in face recognition.

The fast householder Bi-SVD subspace tracking algorithm

A new bi-iteration type subspace tracker for updating a rank- r SVD approximant of a time-varying cross-correlation matrix of dimension N × M is introduced. The algorithm is based on updated orthonormal-square ( QS) decompositions with row-Householder reflections and attains a dominant complexity of 3 Nr + 3 Mr operations per time update, which is the lower bound of dominant complexity for an algorithm of this kind. A closed-form quasicode listing of the algorithm is provided. Computer experiments validate the theoretical results.

A general correlation model for shadow fading in mobile radio systems

We derive an explicit model for the cross-correlation function of the shadowing components affecting the links between a mobile station and two base stations. This model includes both the autocorrelation model proposed in earlier work by Gudmenson in 1991 for the single component, and the cross-correlation early evidenced in the work by Graziano in 1978. The model is useful in analytical computations, e.g., of second order statistics of signal-to-interference ratio. The model can include the behavior of a time-variant cross-correlation at zero-time shift and of a non-homogeneous environment.