Streaks in Daily Returns

2020 ◽  
Author(s):  
Alexander Klos ◽  
Alexandra Koehl ◽  
Simon Rottke
Keyword(s):  
Daily Returns

Properties of the beta coefficient of the global minimum variance portfolio

2020 ◽  
Vol 8 (1) ◽  
pp. 11-21
Author(s):  
S. M. Yaroshko ◽  
◽  
M. V. Zabolotskyy ◽  
T. M. Zabolotskyy ◽  
◽  
...  
Keyword(s):  
Confidence Interval ◽  
Asymptotic Distribution ◽  
Global Minimum ◽  
Asset Returns ◽  
Minimum Variance ◽  
Benchmark Portfolio ◽  
Beta Coefficient ◽  
Minimum Variance Portfolio ◽  
Global Minimum Variance Portfolio ◽  
Daily Returns

The paper is devoted to the investigation of statistical properties of the sample estimator of the beta coefficient in the case when the weights of benchmark portfolio are constant and for the target portfolio, the global minimum variance portfolio is taken. We provide the asymptotic distribution of the sample estimator of the beta coefficient assuming that the asset returns are multivariate normally distributed. Based on the asymptotic distribution we construct the confidence interval for the beta coefficient. We use the daily returns on the assets included in the DAX index for the period from 01.01.2018 to 30.09.2019 to compare empirical and asymptotic means, variances and densities of the standardized estimator for the beta coefficient. We obtain that the bias of the sample estimator converges to zero very slowly for a large number of assets in the portfolio. We present the adjusted estimator of the beta coefficient for which convergence of the empirical variances to the asymptotic ones is not significantly slower than for a sample estimator but the bias of the adjusted estimator is significantly smaller.

scholarly journals Volatility Modeling: An Overview of Equity Markets in the Euro Area during COVID-19 Pandemic

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1212
Author(s):  
Pierdomenico Duttilo ◽  
Stefano Antonio Gattone ◽  
Tonio Di Di Battista
Keyword(s):  
Stock Market ◽  
Euro Area ◽  
Risk Premium ◽  
Stock Markets ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Volatility Modeling ◽  
The Impact ◽  
Second Wave ◽  
Daily Returns

Volatility is the most widespread measure of risk. Volatility modeling allows investors to capture potential losses and investment opportunities. This work aims to examine the impact of the two waves of COVID-19 infections on the return and volatility of the stock market indices of the euro area countries. The study also focuses on other important aspects such as time-varying risk premium and leverage effect. This investigation employed the Threshold GARCH(1,1)-in-Mean model with exogenous dummy variables. Daily returns of the euro area stock markets indices from 4th January 2016 to 31st December 2020 has been used for the analysis. The results reveal that euro area stock markets respond differently to the COVID-19 pandemic. Specifically, the first wave of COVID-19 infections had a notable impact on stock market volatility of euro area countries with middle-large financial centres while the second wave had a significant impact only on stock market volatility of Belgium.

DAY-OF-THE-WEEK EFFECT IN US BIOTECHNOLOGY STOCKS — DO POLICY CHANGES AND ECONOMIC CYCLES MATTER?

2016 ◽  
Vol 11 (02) ◽  
pp. 1650008
Author(s):  
SWARN CHATTERJEE ◽  
AMY HUBBLE
Keyword(s):  
Stock Returns ◽  
Small Firm ◽  
Economic Cycles ◽  
Trading Decisions ◽  
Portfolio Managers ◽  
Small Investors ◽  
Garch Processes ◽  
Day Of The Week ◽  
Firm Effect ◽  
Daily Returns

This study examines the presence of the day-of-the-week effect on daily returns of biotechnology stocks over a 16-year period from January 2002 to December 2015. Using daily returns from the NASDAQ Biotechnology Index (NBI), we find that the stock returns were the lowest on Mondays, and compared to the Mondays the stock returns were significantly higher on Wednesdays, Thursdays, and Fridays. The day-of-the-week effect on returns of biotechnology stocks remained significant even after controlling for the Fama–French and Carhart factors. Moreover, the results from using the asymmetric generalized autoregressive conditional heteroskedastic (GARCH) processes reveal that momentum and small-firm effect were positively associated with the market risk-adjusted returns of the biotechnology stocks during this period. The findings of our study suggest that active portfolio managers need to consider the day of the week, momentum, and small-firm effect when making trading decisions for biotechnology stocks. Implications for portfolio managers, small investors, scholars, and policymakers are included.

scholarly journals On the volatility of sensex

2012 ◽  
Vol 1 (1) ◽  
pp. 10-22
Author(s):  
Nateson C ◽  
Suganya D
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
The Other ◽  
Stock Index ◽  
Series Data ◽  
Closing Time ◽  
Market Activity ◽  
Year 2000 ◽  
Daily Returns

The present study seeks to analyse Volatility of popular stock index SENSEX. The present study is based on the closing time series data of SENSEX covering the period from 3rd January 2000, to 30th June 2011. The year 2008 has recorded higher Volatility compared to the other years of the study. Volatility fell in the year 2009 from the high of 2008. The years after were comparatively calmer. In the year 2000, the Volatility was higher signifying enhance market activity. The overall daily Volatility for SENSEX was approximately 1.70 % while the annualized value was approximately 25%-26%. Events Reported around Daily Returns in Excess of +/-5%have also been identified.

scholarly journals Predicting the Istanbul Stock Exchange Index Return using Technical Indicators

2013 ◽  
Vol 2 (3) ◽  
pp. 111-117
Author(s):  
Senol Emir
Keyword(s):  
Stock Exchange ◽  
Absolute Error ◽  
Percentage Error ◽  
Support Vector ◽  
Model Parameters ◽  
Technical Indicators ◽  
Istanbul Stock Exchange ◽  
Vector Machines ◽  
Classical Linear Regression ◽  
Daily Returns

The aim of this study to examine the performance of Support Vector Regression (SVR) which is a novel regression method based on Support Vector Machines (SVM) approach in predicting the Istanbul Stock Exchange (ISE) National 100 Index daily returns. For bechmarking, results given by SVR were compared to those given by classical Linear Regression (LR). Dataset contains 6 technical indicators which were selected as model inputs for 2005-2011 period. Grid search and cross valiadation is used for finding optimal model parameters and evaluating the models. Comparisons were made based on Root Mean Square (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Theil Inequality Coefficient (TIC) and Mean Mixed Error (MME) metrics. Results indicate that SVR outperforms the LR for all metrics.

scholarly journals Optimal Buy-and-Hold Strategies for Financial Markets with Bounded Daily Returns

2001 ◽  
Vol 31 (2) ◽  
pp. 447-459 ◽  
Author(s):  
Gen-Huey Chen ◽  
Ming-Yang Kao ◽  
Yuh-Dauh Lyuu ◽  
Hsing-Kuo Wong
Keyword(s):  
Financial Markets ◽  
Daily Returns

scholarly journals Holiday effects in the South African futures market

1998 ◽  
Vol 29 (3) ◽  
pp. 119-133 ◽  
Author(s):  
C. F. Smit ◽  
E. V.D.M. Smit
Keyword(s):  
South African ◽  
Futures Market ◽  
Weekend Effect ◽  
Futures Contracts ◽  
The South ◽  
Futures Contract ◽  
Holiday Effect ◽  
Holiday Effects ◽  
Daily Returns ◽  
Local Research

International and local research in share markets offered evidence of a holiday effect. Pre-holiday mean returns are significantly higher than on other trading days. The holiday effect cannot be separated from the weekend effect, as holidays which fall on Fridays and Mondays also influence the weekend analysis. Both these effects exist in their own right. Research on international futures markets supports the existence of a holiday effect. The present study investigates the holiday effect on daily returns of the All Gold Near Futures contract, the All Industrial Near Futures contract and the All Share Near Futures contract in the South African futures market. A distinction is made between pre-holidays, post-holidays and non-holidays. None of the near futures contracts exhibit a significant holiday effect, although signs of a holiday effect are present. It is further shown that the month-end effect is not strongly influenced by the holiday effect. It is also concluded that the pre-holiday effects are not large enough to be exploited on an on-going basis in the South African futures market.

Market dynamics, cyclical patterns and market states

2019 ◽  
Vol 37 (4) ◽  
pp. 585-604
Author(s):  
Azza Bejaoui ◽  
Salim Ben Sassi ◽  
Jihed Majdoub
Keyword(s):  
Stock Returns ◽  
Regime Switching ◽  
Empirical Studies ◽  
Arma Model ◽  
Market Dynamics ◽  
Nonlinear Structure ◽  
Content Type ◽  
Portfolio Decisions ◽  
Short Run ◽  
Daily Returns

Purpose In this paper, the authors seek to investigate the dynamics of Bitcoin, Litecoin, Ethereum and Ripple daily returns and volatilities. Design/methodology/approach In this paper, the authors apply the MS-ARMA model on daily returns of Bitcoin (19/04/2013-13/02/2018), Ripple (05/08/2013-14/02/2018), Litcoin (29/04/2013-14/02/2018) and Ethereum (08/02/2015-14/02/2018). This model allows capture of the nonlinear structure in both the conditional mean and the conditional variance of cryptocurrency returns. Findings All the cryptocurrency markets show regime switching in the return-generating process. Market dynamics seem to be governed by two different states which differ from one cryptocurrency market to another in terms of mean return, volatility and interstate dynamics. These findings can be explained by investors’ behavior, i.e. speculative trading and herding behavior. By choosing to participate (or imitating some investors) in some cryptocurrency markets (in particular Bitcoin market), they affect the price movements and therefore the market dynamics in the short run. Practical implications Identifying the different market states provides information for investors to make more accurate portfolio decisions in the virtual market and follow the market timing strategy. Originality/value This paper attempts to analyze potential nonlinear structure in cryptocurrencies returns and analyze if there is a difference between the cryptocurrencies market cycles. So, the search for congruent and adequate specification to reproduce the stock returns dynamics in the virtual market still remains the concern of several empirical studies. This research not only examines the behavior of stock returns in the cryptocurrencies’ market but also highlights the existence of nonlinearity propriety as a stylized fact.

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