The co-movement and causality between housing and stock markets in the time and frequency domains considering inflation

2018 ◽  
Vol 8 (1) ◽  
pp. 92-108 ◽  
Author(s):  
Jiaojiao Fan ◽  
Xin Li ◽  
Qinghua Shi ◽  
Chi-Wei Su
Keyword(s):  
Wavelet Analysis ◽  
Frequency Domain ◽  
Causal Relationship ◽  
Time Domain ◽  
Stock Prices ◽  
Stock Markets ◽  
Housing Prices ◽  
Price Effect ◽  
Content Type ◽  
Positive Effect

Purpose The purpose of this paper is to examine the causal relationship between Chinese housing and stock markets. The authors discuss the three transmission mechanisms between the two markets: wealth effect, modern portfolio theory and credit-price effect. Moreover, the authors focus on the effects of inflation on the relationship between the two markets. Design/methodology/approach This paper uses wavelet analysis to test the housing and stock markets relationship both in the frequency domain and time domain. Findings The empirical results indicate that housing prices have a positive effect on stock prices, and these have the same effect on housing prices. Moreover, this positive effect means that stock prices have a wealth effect on housing prices and these have a credit-price effect on stock prices. Research limitations/implications These results provide information to financial institutions and individual investors in China to assist them in constructing investment portfolios within these two asset markets. Originality/value The authors first use wavelet analysis to analyze Chinese housing and stock markets and to provide information both on the frequency domain and time domain. Moreover, the authors take the inflation factor as a control variable in the causal relationship between the housing and stock markets.

The causality between house prices and stock prices: evidence from seven European countries

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Manuchehr Irandoust
Keyword(s):  
Stock Prices ◽  
Stock Markets ◽  
Financial Stability ◽  
Structural Breaks ◽  
Causal Structure ◽  
Housing Prices ◽  
Control Variable ◽  
European Countries ◽  
Cross Sectional ◽  
Content Type

Purpose This paper aims to examine whether there exists a long-run causal relationship between the prices of households’ two major assets: stocks and houses over the period 1975Q1–2017Q1 for seven major European countries. Design/methodology/approach The paper uses the bootstrap panel Granger causality approach to determine the causal structure, focusing on cross-sectional dependence, slope heterogeneity and structural breaks. Findings The findings show that, in most cases, there is a unidirectional causality running from stock price to house price but the converse is not true. This confirms a strong wealth effect in housing markets. The findings are important for not only households but also policymakers concerned with financial stability and housing prices. Originality/value First, the methodology used here devotes full attention to dynamic co-movement between housing and stock markets. Second, this study uses a rather long quarterly data, which implies that the findings could be robust. Third, the study uses real personal disposable income as a control variable to remove the effects of economic growth. Fourth, most of the previous studies do not consider the presence of structural breaks and this makes the result of causality invalid and biased. Fifth, most of the previous studies on housing and stock markets concentrated on the US and non-European countries such as China, Korea and Singapore.

Spectrum analysis of moving average operator and construction of time-frequency hybrid sequence operator

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Changhai Lin ◽  
Sifeng Liu ◽  
Zhigeng Fang ◽  
Yingjie Yang
Keyword(s):  
Frequency Domain ◽  
Time Domain ◽  
Moving Average ◽  
Spectral Characteristics ◽  
Complex Data ◽  
Time Data ◽  
Average Operator ◽  
Content Type ◽  
Time Frequency ◽  
Hybrid Sequence

PurposeThe purpose of this paper is to analyze the spectral characteristics of moving average operator and to propose a novel time-frequency hybrid sequence operator.Design/methodology/approachFirstly, the complex data is converted into frequency domain data by Fourier transform. An appropriate frequency domain operator is constructed to eliminate the impact of disturbance. Then, the inverse Fourier transform transforms the frequency domain data in which the disturbance is removed, into time domain data. Finally, an appropriate moving average operator of N items is selected based on spectral characteristics to eliminate the influence of periodic factors and noise.FindingsThrough the spectrum analysis of the real-time data sensed and recorded by microwave sensors, the spectral characteristics and the ranges of information, noise and shock disturbance factors in the data can be clarified.Practical implicationsThe real-time data analysis results for a drug component monitoring show that the hybrid sequence operator has a good effect on suppressing disturbances, periodic factors and noise implied in the data.Originality/valueFirstly, the spectral analysis of moving average operator and the novel time-frequency hybrid sequence operator were presented in this paper. For complex data, the ideal effect is difficult to achieve by applying the frequency domain operator or time domain operator alone. The more satisfactory results can be obtained by time-frequency hybrid sequence operator.

Economic policy uncertainty and stock prices in BRIC countries: evidence from asymmetric frequency domain causality approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mucahit Aydin ◽  
Ugur Korkut Pata ◽  
Veysel Inal
Keyword(s):  
Economic Policy ◽  
Frequency Domain ◽  
Stock Prices ◽  
Policy Uncertainty ◽  
Causality Test ◽  
Content Type ◽  
Economic Policy Uncertainty ◽  
Bric Countries ◽  
Causality Tests ◽  
Asymmetric Causality

Purpose The aim of this study is to investigate the relationship between economic policy uncertainty (EPU) and stock prices during the period from March 2003 to March 2021. Design/methodology/approach The study uses asymmetric and symmetric frequency domain causality tests and focuses on BRIC countries, namely, Brazil, Russia, India and China. Findings The findings of the symmetric causality test confirm unidirectional permanent causality from EPU to stock prices for Brazil and India and bidirectional causality for China. However, according to the asymmetric causality test, the findings for China show that there is no causality between the variables. The results for Brazil and India indicate that there is unidirectional permanent causality from positive components of EPU to positive components of stock prices. Moreover, for Brazil, there is unidirectional temporary causality from the negative components of EPU to the negative components of stock prices. For India, there is temporary causality in the opposite direction. Originality/value The reactions of financial markets to positive and negative shocks differ. In this context, to the best of the authors’ knowledge, this study is the first attempt to examine the causal relationships between stock prices and uncertainty using an asymmetric frequency domain approach. Thus, the study enables the analysis of the effects of positive and negative shocks in the stock market separately.

Stock prices' long memory in China and the United States

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhengxun Tan ◽  
Yao Fu ◽  
Hong Cheng ◽  
Juan Liu
Keyword(s):  
Stock Market ◽  
Stock Prices ◽  
Long Memory ◽  
Stock Markets ◽  
Information Disclosure ◽  
Structural Breaks ◽  
Efficient Market Hypothesis ◽  
The United States ◽  
Chinese Stock Market ◽  
Content Type

PurposeThis study aims to examine the long memory as well as the effect of structural breaks in the US and the Chinese stock markets. More importantly, it further explores possible causes of the differences in long memory between these two stock markets.Design/methodology/approachThe authors employ various methods to estimate the memory parameters, including the modified R/S, averaged periodogram, Lagrange multiplier, local Whittle and exact local Whittle estimations.FindingsChina's two stock markets exhibit long memory, whereas the two US markets do not. Furthermore, long memory is robust in Chinese markets even when we test break-adjusted data. The Chinese stock market does not meet the efficient market hypothesis (EMHs), including the efficiency of information disclosure, regulations and supervision, investors' behavior, and trading mechanisms. Therefore, its stock prices' sluggish response to information leads to momentum effects and long memory.Originality/valueThe authors elaborately illustrate how long memory develops by analyzing not only stock market indices but also typical individual stocks in both the emerging China and the developed US, which diversifies the EMH with wider international stylized facts and findings when compared with previous literature. A couple of tests conducted to analyze structural break effects and spurious long memory demonstrate the reliability of the results. The authors’ findings have significant implications for investors and policymakers worldwide.

Long-range dependence in Indian stock market: a study of Indian sectoral indices

2014 ◽  
Vol 9 (4) ◽  
pp. 505-519 ◽  
Author(s):  
Dilip Kumar
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Frequency Domain ◽  
Long Range ◽  
Time Domain ◽  
Long Range Dependence ◽  
Fluctuation Analysis ◽  
Scaling Exponent ◽  
Simulation Experiments ◽  
Content Type

Purpose – The purpose of this paper is to test the efficient market hypothesis for major Indian sectoral indices by means of long memory approach in both time domain and frequency domain. This paper also tests the accuracy of the detrended fluctuation analysis (DFA) approach and the local Whittle (LW) approach by means of Monte Carlo simulation experiments. Design/methodology/approach – The author applies the DFA approach for the computation of the scaling exponent in the time domain. The robustness of the results is tested by the computation of the scaling exponent in the frequency domain by means of the LW estimator. The author applies moving sub-sample approach on DFA to study the evolution of market efficiency in Indian sectoral indices. Findings – The Monte Carlo simulation experiments indicate that the DFA approach and the LW approach provides good estimates of the scaling exponent as the sample size increases. The author also finds that the efficiency characteristics of Indian sectoral indices and their stages of development are dynamic in nature. Originality/value – This paper has both methodological and empirical originality. On the methodological side, the author tests the small sample properties of the DFA and the LW approaches by using simulated series of fractional Gaussian noise and find that both the approach possesses superior properties in terms of capturing the scaling behavior of asset prices. On the empirical side, the author studies the evolution of long-range dependence characteristics in Indian sectoral indices.

Global financial crisis and cyclical co-movements of Asian financial markets

2016 ◽  
Vol 34 (5) ◽  
pp. 465-495 ◽  
Author(s):  
KimHiang Liow
Keyword(s):  
Financial Crisis ◽  
Real Estate ◽  
Financial Markets ◽  
Frequency Domain ◽  
Stock Markets ◽  
Global Financial Crisis ◽  
Short Term ◽  
Content Type ◽  
Real Estate Markets ◽  
Asset Classes

Purpose – The purpose of this paper is to investigate the cross-spectra of stock, real estate and bond of ten selected Asian economies in the pre- and post-global financial crisis periods to detect whether there is greater cyclical co-movement post-financial crisis, and whether any observed increased co-movement measures the outcomes of contagion or integration. Design/methodology/approach – Co-spectral approach is the proper econometric tool to deliver economic insight for this research. Findings – Results indicate that Asian stock markets, and to a lesser degree, bond and real estate markets are more correlated post-financial crisis. Similarly, Asian financial markets have experienced increased co-movements with the US financial markets post-financial crisis. Moreover, these observed increased co-movements measure the outcomes of contagion in some cases of within-asset and cross-asset classes, as well as for some cross-US-Asian asset factor relationships along the high-frequency components of between two and four weeks. The stock markets are the most contagious, followed by the real estate markets and bond markets. Research limitations/implications – The results provide short-term investors with additional co-movement information at higher frequencies in order to identify short-term fluctuations of different asset classes. The empirical study also underscores the role of Asian real estate in investment portfolios in a mixed real estate, stock and bond context from a frequency domain perspective. Practical implications – The practical implication of this research is that benefits to investors from international diversification may not be as great during the present time compared to previous periods because financial/asset market movements have become more correlated. However, it does not imply the complete absence of diversification benefits. This is because although cyclical correlations increase in the short run, many of the values are still between low and moderate range, indicating that some diversification benefits may still be realized. Originality/value – In advancing the body of knowledge in international financial markets, this research is probably the first study to consider a multi-asset class portfolio context that includes stock, real estate and bond across the ten Asian economies and the USA in a single study. The frequency domain analysis conducted in this paper adds to the understanding of real estate, stock and bond market co-movement, integration and contagion dynamics, as well as the Asian cross-asset factor and US-Asian asset factor relationships in global mixed-investing environment.

Current spectrum estimation using Prony's estimator and coherent resampling

2014 ◽  
Vol 33 (3) ◽  
pp. 989-997 ◽  
Author(s):  
Michał Lewandowski ◽  
Janusz Walczak
Keyword(s):  
Fourier Transform ◽  
Frequency Domain ◽  
Time Domain ◽  
Domain Model ◽  
Spectrum Estimation ◽  
Nonlinear Load ◽  
Frequency Model ◽  
Content Type ◽  
Base Frequency ◽  
Current Spectrum

Purpose – A highly accurate method of current spectrum estimation of a nonlinear load is presented in this paper. Using the method makes it possible to evaluate the current injection frequency domain model of a nonlinear load from previously recorded time domain voltage and current waveforms. The paper aims to discuss these issues. Design/methodology/approach – The method incorporates the idea of coherent resampling (resampling synchronously with the base frequency of the signal) followed by the discrete Fourier transform (DFT) to obtain the frequency spectrum. When DFT is applied to a synchronously resampled signal, the spectrum is free of negative DFT effects (the spectrum leakage, for example). However, to resample the signal correctly it is necessary to know its base frequency with high accuracy. To estimate the base frequency, the first-order Prony's frequency estimator was used. Findings – It has been shown that the presented method may lead to superior results in comparison with window interpolated Fourier transform and time-domain quasi-synchronous sampling algorithms. Research limitations/implications – The method was designed for steady-state analysis in the frequency domain. The voltage and current waveforms across load terminals should be recorded simultaneously to allow correct voltage/current phase shift estimation. Practical implications – The proposed method can be used in case when the frequency domain model of a nonlinear load is desired and the voltage and current waveforms recorded across load terminals are available. The method leads to correct results even when the voltage/current sampling frequency has not been synchronized with the base frequency of the signal. It can be used for off-line frequency model estimation as well as in real-time DSP systems to restore coherent sampling of the analysed signals. Originality/value – The method proposed in the paper allows to estimate a nonlinear load frequency domain model from current and voltage waveforms with higher accuracy than other competitive methods, while at the same time its simplicity and computational efficiency is retained.

Financial Index Prediction Based on Wavelet Analysis and SVM

2013 ◽  
Vol 303-306 ◽  
pp. 1114-1118
Author(s):  
Xian Tan
Keyword(s):  
Wavelet Analysis ◽  
Frequency Domain ◽  
Time Domain ◽  
Financial Time Series ◽  
Support Vector ◽  
Frequency Domain Method ◽  
Signal Characteristics ◽  
Vector Machines ◽  
Financial Index ◽  
The Time Domain

The analysis of the time sequence can be two ways in the time domain and frequency domain. But many financial time series exhibit strong non-stationary and long memory, which makes many traditional individually focused on the research and analysis of the time domain or frequency domain method is no longer applicable. In this paper, wavelet analysis and support vector machines for use in the time domain and frequency domain have the ability to characterize the local signal characteristics, location and mutation of the singular points and irregular mutation analysis, these mutations detected the degree of significance.

Mixed frequency domain spillover effect of international economic policy uncertainty on stock market

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shuzhen Zhu ◽  
Xiaofei Wu ◽  
Zhen He ◽  
Yining He
Keyword(s):  
International Economic ◽  
Economic Policy ◽  
Stock Market ◽  
Frequency Domain ◽  
Time Domain ◽  
Spillover Effect ◽  
Spillover Effects ◽  
Policy Uncertainty ◽  
Content Type ◽  
Economic Policy Uncertainty

Purpose The purpose of this paper is to construct a frequency-domain framework to study the asymmetric spillover effects of international economic policy uncertainty on China’s stock market industry indexes. Design/methodology/approach This paper follows the time domain spillover model, asymmetric spillover model and frequency domain spillover model, which not only studies the degree of spillover in time domain but also studies the persistence of spillover effect in frequency domain. Findings It is found that China’s economic policy uncertainty plays a dominant role in the spillover effect on the stock market, while the global and US economic policy uncertainty is relatively weak. By decomposing realized volatility into quantified asymmetric risks of “good” volatility and “bad” volatility, it is concluded that economic policy uncertainty has a greater impact on stock downside risk than upside risk. For different time periods, the sensitivity of long-term and short-term spillover economic policy impact is different. Among them, asymmetric high-frequency spillover in the stock market is more easily observed, which provides certain reference significance for the stability of the financial market. Originality/value The originality aims at extending the traditional research paradigm of “time domain” to the research perspective of “frequency domain.” This study uses the more advanced models to analyze various factors from the static and dynamic levels, with a view to obtain reliable and robust research conclusions.

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