Indexing and Stock Market Serial Dependence Around the World

Baltussen, Guido; Bekkum, Sjoerd van; Da, Zhi

doi:10.2139/ssrn.2786516

Cited by 12 publications

(19 citation statements)

References 55 publications

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“…30 We see that for most of the series (17 out of 20), the considered intervals in (A.30) based on robust t−statistic approaches applied to inference on ρ R,|R| s sign(R) (1) contain zero, and for two of the series the confidence intervals lie on the right of zero. Hence, following the robust t−statistic tests (see Appendix A), we find little evidence for significantly negative (serial) dependence in the return series, in contrast to the conclusions in Baltussen et al (2019). 31 The same qualitative conclusions apply if we set s = 0.1 (Column 10 in Table 2), and also hold for inference on the MAC( 5) measure (Column 13 of the table).…”

Section: Empirical Application: Robust Tests Of Market Efficiency Non...supporting

confidence: 55%

Section: Empirical Application: Robust Tests Of Market Efficiency Non...mentioning

confidence: 99%

“…In this section we revisit a recent study by Baltussen et al (2019) on linear dependence in returns on major stock market indexes. Specifically, Baltussen et al (2019) present empirical results based on HAC inference approaches applied to the first-order autocorrelations, that is, ρ R (1), that suggest that serial dependence in daily returns on 20 major market indexes covering 15 countries in North America, Europe, and Asia was significantly positive until the end of the 1990s, and switched to being significantly negative since the 2000s. In light of Theorem 3.2 (with s = 1), asymptotic normality of the sample autocorrelation relies on finite fourth-order moments of the process, and in the case where such moment conditions are not satisfied, the limiting distribution may be a function of non-Gaussian stable variables (Remark 3.4).…”

Section: Empirical Application: Robust Tests Of Market Efficiency Non...mentioning

confidence: 99%

“…Columns 4, 7, 9 and 11 in Table 2 provide the full-sample estimates of the autocorrelations ρ R (1) and ρ R,|R| s sign(R) (1) in properties (i) and (i') and those of the multi-period autocorrelation MAC(5) (the weighted sum of the first five autocorrelation coefficients of order h = 1, 2, 3, 4, 5) used in Baltussen et al (2019). Columns 5 and 12 provide the 95% HAC confidence intervals for ρ R (1) and MAC(5).…”

Section: Empirical Application: Robust Tests Of Market Efficiency Non...mentioning

confidence: 99%

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New Approaches to Robust Inference on Market (Non-)Efficiency, Volatility Clustering and Nonlinear Dependence

Ибрагимов¹,

Pedersen²,

Skrobotov³

2020

Preprint

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Many key variables in finance, economics and risk management, including financial returns and foreign exchange rates, exhibit nonlinear dependence, heterogeneity and heavy-tailedness of some usually largely unknown type.The presence of non-linear dependence (usually modelled using GARCH-type dynamics) and heavy-tailedness may make problematic the analysis of (non-)efficiency, volatility clustering and predictive regressions in economic and financial markets using traditional approaches that appeal to asymptotic normality of sample autocorrelation functions (ACFs) of returns and their squares.The paper presents several new approaches to deal with the above problems. We provide the results that motivate the use of measures of market (non-)efficiency, volatility clustering and nonlinear dependence based on (small) powers of absolute returns and their signed versions. The paper provides asymptotic theory for sample analogues of the above measures in the case of general time series, including GARCH-type processes. It further develops new approaches to robust inference on them in the case of general GARCH-type processes exhibiting heavy-tailedness properties typical for real-world financial markets. The approaches are based on robust inference methods exploiting conservativeness properties of t−statistics Müller, 2010, 2016) and several new results on their applicability in the settings considered. In the approaches, estimates of parameters of interest (e.g., measures of nonlinear dependence given by sample autocorrelations of powers of the returns' absolute values) are computed for groups of data and the inference is based on t-statistics in resulting group estimates. This results in valid robust inference under a wide range of heterogeneity and dependence assumptions satisfied in financial and economic markets. Numerical results and empirical applications confirm advantages of the new approaches over existing ones and their wide applicability in the study of market (non-)efficiency, volatility clustering, nonlinear dependence, and other areas.

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Section: Empirical Application: Robust Tests Of Market Efficiency Non...supporting

confidence: 55%

Section: Empirical Application: Robust Tests Of Market Efficiency Non...mentioning

confidence: 99%

Section: Empirical Application: Robust Tests Of Market Efficiency Non...mentioning

confidence: 99%

Section: Empirical Application: Robust Tests Of Market Efficiency Non...mentioning

confidence: 99%

See 2 more Smart Citations

New Approaches to Robust Inference on Market (Non-)Efficiency, Volatility Clustering and Nonlinear Dependence

Ибрагимов¹,

Pedersen²,

Skrobotov³

2020

Preprint

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“…Staer (2014) finds that ETF flows are contemporaneous with index returns and that these price effects partly revert after a few days. Baltussen, Da, and van Bekkum (2016) find that the serial correlation of stock markets became more negative following indexation. They interpret this result as evidence that index products impound non-fundamental shocks (which then revert) into the underlying security prices.…”

Section: Propagation Of Demand Shocks To Underlying Securitiesmentioning

confidence: 88%

Exchange Traded Funds (ETFs)

2016

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Over nearly a quarter of a century, ETFs have become one of the most popular passive investment vehicles among retail and professional investors due to their low transaction costs and high liquidity. By the end of 2016, the market share of ETFs topped over 10% of the total market capitalization traded on US exchanges, while representing more than 30% of the overall trading volume. ETFs revolutionized the asset management industry by taking market share from traditional investment vehicles such as mutual funds and index futures. Because ETFs rely on arbitrage activity to synchronize their prices with the prices of the underlying portfolio, trading activity at the ETF level translates to trading of the underlying securities. Researchers found that while ETFs enhance price discovery, they also inject non-fundamental volatility to market prices and affect the correlation structure of returns. Furthermore, ETFs impact the liquidity of the underlying portfolios, especially during events of market stress.

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Paper profits or real money? Trading costs and stock market anomalies in country ETFs

Zaremba

Andreu

2018

International Review of Financial Analysis

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Indexing and Stock Market Serial Dependence Around the World

Cited by 12 publications

References 55 publications

New Approaches to Robust Inference on Market (Non-)Efficiency, Volatility Clustering and Nonlinear Dependence

New Approaches to Robust Inference on Market (Non-)Efficiency, Volatility Clustering and Nonlinear Dependence

Exchange Traded Funds (ETFs)

Paper profits or real money? Trading costs and stock market anomalies in country ETFs

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