The impact of futures trading on underlying stock index volatility: the case of the FTSE Mid 250 contract

2000 ◽  
Vol 7 (7) ◽  
pp. 439-442 ◽  
Author(s):  
Darren Butterworth
Keyword(s):  
Stock Index ◽  
Futures Trading ◽  
The Impact

scholarly journals Enhancing Portfolio Performance and VIX Futures Trading Timing with Markov-Switching GARCH Models

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 185
Author(s):  
Oscar V. De la Torre-Torres ◽  
Francisco Venegas-Martínez ◽  
Mᵃ Isabel Martínez-Torre-Enciso
Keyword(s):  
Markov Switching ◽  
Stock Index ◽  
Garch Models ◽  
Portfolio Performance ◽  
Futures Trading ◽  
Volatility Index ◽  
Investment Level ◽  
Vix Futures ◽  
High Volatility ◽  
The Impact

In the present paper, we test the use of Markov-Switching (MS) models with time-fixed or Generalized Autoregressive Conditional Heteroskedasticity (GARCH) variances. This, to enhance the performance of a U.S. dollar-based portfolio that invest in the S&P 500 (SP500) stock index, the 3-month U.S. Treasury-bill (T-BILL) or the 1-month volatility index (VIX) futures. For the investment algorithm, we propose the use of two and three-regime, Gaussian and t-Student, MS and MS-GARCH models. This is done to forecast the probability of high volatility episodes in the SP500 and to determine the investment level in each asset. To test the algorithm, we simulated 8 portfolios that invested in these three assets, in a weekly basis from 23 December 2005 to 14 August 2020. Our results suggest that the use of MS and MS-GARCH models and VIX futures leads the simulated portfolio to outperform a buy and hold strategy in the SP500. Also, we found that this result holds only in high and extreme volatility periods. As a recommendation for practitioners, we found that our investment algorithm must be used only by institutional investors, given the impact of stock trading fees.

Do Indian stock market sentiments impact contemporaneous returns?

2018 ◽  
Vol 7 (3) ◽  
pp. 332-346
Author(s):  
Divya Aggarwal ◽  
Pitabas Mohanty
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Temporal Dynamics ◽  
Stock Exchange ◽  
Principal Component ◽  
Stock Index ◽  
Content Type ◽  
Investor Sentiments ◽  
Indian Stock Market ◽  
The Impact

Purpose The purpose of this paper is to analyse the impact of Indian investor sentiments on contemporaneous stock returns of Bombay Stock Exchange, National Stock Exchange and various sectoral indices in India by developing a sentiment index. Design/methodology/approach The study uses principal component analysis to develop a sentiment index as a proxy for Indian stock market sentiments over a time frame from April 1996 to January 2017. It uses an exploratory approach to identify relevant proxies in building a sentiment index using indirect market measures and macro variables of Indian and US markets. Findings The study finds that there is a significant positive correlation between the sentiment index and stock index returns. Sectors which are more dependent on institutional fund flows show a significant impact of the change in sentiments on their respective sectoral indices. Research limitations/implications The study has used data at a monthly frequency. Analysing higher frequency data can explain short-term temporal dynamics between sentiments and returns better. Further studies can be done to explore whether sentiments can be used to predict stock returns. Practical implications The results imply that one can develop profitable trading strategies by investing in sectors like metals and capital goods, which are more susceptible to generate positive returns when the sentiment index is high. Originality/value The study supplements the existing literature on the impact of investor sentiments on contemporaneous stock returns in the context of a developing market. It identifies relevant proxies of investor sentiments for the Indian stock market.

scholarly journals HOW DID COVID-19 PANDEMIC AFFECT THE TOURISM INDEX IN BORSA ISTANBUL?

2021 ◽  
Author(s):  
Ercan Özen ◽  
Letife Özdemir
Keyword(s):  
Regression Model ◽  
Regression Method ◽  
Stock Index ◽  
Daily Data ◽  
Tourism Sector ◽  
The Impact ◽  
Bound Test ◽  
Cointegration Relationship ◽  
Ardl Bound Test

This study aims to investigate the impact of the Covid-19 pandemic on Turkey's tourism sector. In the study, for the period 12 March 2020 - 31 August 2020 the daily data of the BIST tourism stock index and Covid-19 case and death counts in Turkey were used. The cointegration relationship between the Covid-19 pandemic and the BIST tourism index was investigated with the ARDL bound test. In addition, the effect of the Covid-19 pandemic on the BIST tourism index was tested with the FMOLS regression method. As a result of the ARDL bound test, it was determined that there is a long-term cointegration relationship between the Covid-19 case and death numbers and the BIST tourism index. According to the FMOLS regression model results, it is seen that the deaths of Covid 19 significantly affect the tourism index. A 1% increase in the number of deaths causes the BIST tourism index to decrease by 0.08%. The coefficient of the number of Covid-19 cases is not significant, showing that the number of cases does not have a sufficient effect on the tourism index.

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