Are Different National Stock Markets Driven by the Same Stochastic Hidden Variable?

The following contribution analyzes linkages between preselected national stock markets by a multivariate application of Markov-Switching models. This study shows evidence that the US-stock market and the German and Swedish stock markets are driven by the same unobservable stochastic variable. The latent variable causes these stock markets to switch between highly persistent Bull- and Bear-market regimes which offer strategic market timing opportunities. An out-of-sample experiment where stock market regimes are simultaneously forecasted on a monthly frequency (January 2008 – December 2010) shows that an actively managed equity funds being restricted to hold stocks permanently, dominates all passive trading strategies that account for internationally diversified equity portfolios.

Do Business Cycles Exhibit Beneficial Information for Portfolio Management? An Empirical Application of Statistical Arbitrage

An advantageous statistical arbitrage strategy should exhibit a zero-cost trading strategy for which the expected payoff should be positive. In practical applications, however, the abnormal returns often are out-of-sample not significant. The statistical model being suggested here results in an estimated portfolio exhibiting in-sample a cointegration relationship with the artificial stock index. The portfolio returns exhibited out-of-sample a mean of 10.44% p.a., whereas the volatility was one third lower in comparison to the benchmark's volatility. Accounting for trading costs of 2.94% p.a. on average, the annual returns of the estimated portfolio are out-of-sample still 6.83% higher than the market returns. As a result, the model involves implicitly advantageous market timing.

Have volatility spillover effects of cointegrated European stock markets increased over time?

In this study volatility spillover effects in preselected cointegrated European stock markets are investigated. The data generating processes are estimated by applying Vector-Auto Regression (VAR) models. Thereby, the impacts of volatility spillovers are measured by a new concept being denoted here as Volatility Impulse Response Density Functions (VIRDF) being an advancement of the Volatility Impulse Response Functions (VIRF) methodology. A sample-split analysis covering daily data from 26.11.1990-05.10.2000 and 06.10.2000-28.05.2010 reveals that the volatility spillover impact from the German stock market to the Swedish and British stock markets have increased by 73.87%, respectively, 15.52%.

On Survivor Stocks in the S&P 500 Stock Index

This paper investigates the performance and characteristics of survivor stocks in the S&P 500 index. Using both in-sample and out-of-sample comparisons, survivor stocks outperformed this market index by a considerable margin. Relative to other S&P 500 index companies, survivor stocks tend to be small-value stocks that exhibit high profitability and invest conservatively. Surprisingly, survivor stocks tend to be loser stocks with negative exposure to the momentum factor. Further analyses show that the volatility of the survivor stocks portfolio is less exposed to tail risks and responds less to shocks in the innovation process.© 2022 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

When the blockchain does not block : on hackings and uncertainty in the cryptocurrency market

A total of 1.1 million bitcoins were stolen in the 2013–2017 period. Noting that the average price for a Bitcoin in 2018 was $7572 the corresponding monetary equivalent of losses is$8.9 billion highlighting the societal impact of this criminal activity. Investigating the response of the uncertainty of Bitcoin returns when hacking incidents occur, the results of this study point toward two different responses. After experiencing a contemporaneous effect at day t=0, the volatility increases significantly again at day t+5. Hacking incidents that occur in the Bitcoin market also affect the uncertainty in the Ethereum market with a time delay of five days. Notably, neither Bitcoin nor Ethereum appear to exhibit asymmetric responses to negative innovations.© 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.fi=vertaisarvioitu|en=peerReviewed

No reward—no effort : Will Bitcoin collapse near to the year 2140?

This paper explores whether the overall evolution of Bitcoin log-prices would manifest a log-period power-law singularity (LPPLS) signature, eventually resulting in the arrival of a finite-time singularity. Calibrating the LPPLS model using daily data on Bitcoin covering the 2011—2023 period, this study indeed finds evidence for a strong LPPLS signature suggesting the arrival of a spontaneous singularity in the year 2129. Further striking evidence suggests that Bitcoin will experience what we term a close-to-singularity-condition near to the year 2050—a remarkable coincidence with the recently documented arrival of a finite-time singularity in U.S. equities.© 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Correlation versus co-fractality : Evidence from foreign-exchange-rate variances

The concept of correlation appears to be the cornerstone of modern finance as it is applied in almost all finance-related research studies. However, Fama (1963) argued that “if the [population] variance is infinite, other statistical tools (e.g., least-squares regression) which are based on the assumption of finite variance will, at best, be considerably weakened and may in fact give very misleading answers” (p. 421). This study shows variances of foreign exchange rates to be governed by power laws with a tail exponent of α < 3, suggesting infinite second moments. We derive a new concept to measure dependencies between power-law processes with this tail exponent, which we term co-fractality. We show that risk diversification based on the concept of correlation indeed gives misleading results. Notably, foreign-exchange-rate variances lacking co-fractality in our earlier subsample do not show evidence for co-fractality in our later subsample. We argue that co-fractality, as opposed to correlation, should be used to measure the dependency between processes governed by power laws.© 2023 The Author. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

A multifractal model of asset (in)variances

This study extends Mandelbrot’s (2008) multifractal model of asset returns to model realized variances across different time frequencies. In a comparative manner, various degrees of time deformations are explored for implementation of the multiplicative cascade. In doing so, this study focuses on two effects: discontinuity measured by the specific power-law exponent and dependency measured by the Hurst exponent. This study shows that the benchmark model, for which Mandelbrot’s (2008) “cartoon” is the foundation, has some remarkable properties as it is capable of explaining the realized variances for the GBP/USD exchange rate and Bitcoin. Notably, the realized variances for crude oil and the S&P 500 require a more extreme time deformation. The invariance hypothesis is confirmed for all realized variances because the power-law exponents for weekly and monthly data coincide with predictions of the multifractal model. Overall, the novel results derived from the proposed multifractal models suggest that some realized variances of otherwise unrelated asset markets are driven by the same underlying “driving force”—a common multifractal cascade.© 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Essays on empirical asset pricing

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What do we know about the second moment of financial markets?

Recent research shows that the vast majority of scientific studies published in leading finance journals fails scientific replication (Hou, Xue, and Zhang, 2020; Harvey, Liu, and Zhu; 2016). This study argues that p-hacking, publication pressure and the selection bias from leading finance journals are perhaps not the underlying root cause for this issue. This study shows that standard methodologies often used in finance research are inevitably sample-specific due to the very nature of financial markets. While the consensus of earlier research postulates a rejection of the time-honored Levy hypothesis, the results of this study strongly indicate that the variance of variance does not exist in any of the financial key markets that are considered. An unexpected finding of this study is that the variance process governing the U.S. dollar foreign exchange rate market is generating more extreme events than the Bitcoin market. The results cast doubts on the validity of methodologies currently used in finance research.© 2021 The Author. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed