Expiration-Day Effects of Index Futures in a Frontier Market: The Case of Ho Chi Minh Stock Exchange

This study employs OLS, GARCH and EGARCH regression models to test the expiration-day effects of index stock futures on market returns, volatility and trading volume for the Ho Chi Minh Stock Exchange (HOSE). Data used in this study is from a daily return series of the VN30-Index for the period from 10August 2017 through 30 June 2020. The results derived from GARCH(1,1) and EGARCH(1,1) models consistently confirm that Index futures expiration-day effects on market returns exists in the HOSE. Specifically, the average market return for expiration days is significantly lower than other trading days, by 0.13% at the 5% level of significance. However, the results obtained from the regression models indicate that the expiration-day has no impact on market volatility and trading volume.

Contrasting Cryptocurrencies with Other Assets: Full Distributions and the COVID Impact

We investigate any similarity and dependence based on the full distributions of cryptocurrency assets, stock indices and industry groups. We characterize full distributions with entropies to account for higher moments and non-Gaussianity of returns. Divergence and distance between distributions are measured by metric entropies, and are rigorously tested for statistical significance. We assess the stationarity and normality of assets, as well as the basic statistics of cryptocurrencies and traditional asset indices, before and after the COVID-19 pandemic outbreak. These assessments are not subjected to possible misspecifications of conditional time series models which are also examined for their own interests. We find that the NASDAQ daily return has the most similar density and co-dependence with Bitcoin daily return, generally, but after the COVID-19 outbreak in early 2020, even S&P500 daily return distribution is statistically closely dependent on, and indifferent from Bitcoin daily return. All asset distances have declined by 75% or more after the COVID-19 outbreak. We also find that the highest similarity before the COVID-19 outbreak is between Bitcoin and Coal, Steel and Mining industries, and after the COVID-19 outbreak is between Bitcoin and Business Supplies, Utilities, Tobacco Products and Restaurants, Hotels, Motels industries, compared to several others. This study shed light on examining distribution similarity and co-dependence between cryptocurrencies and other asset classes.

Lottery-like preferences and the MAX effect in the cryptocurrency market

We investigate the significance of extreme positive returns in the cross-sectional pricing of cryptocurrencies. Through portfolio-level analyses and weekly cross-sectional regressions on all cryptocurrencies in our sample period, we provide evidence for a positive and statistically significant relationship between the maximum daily return within the previous month (MAX) and the expected returns on cryptocurrencies. In particular, the univariate portfolio analysis shows that weekly average raw and risk-adjusted return differences between portfolios of cryptocurrencies with the highest and lowest MAX deciles are 3.03% and 1.99%, respectively. The results are robust with respect to the differences in size, price, momentum, short-term reversal, liquidity, volatility, skewness, and investor sentiment.

Sector Wise Stock Market Performance during Pre and Post Covid Era

The spread of the Covid-19 pandemic has an unprecedented and immense impact on the world economy as well as the Indian economy. The stock market, treated as a barometer of the economic activity of any country is adversely affected. Not even in India, countries like Germany, France, the USA, and Spain have been strongly affected. Nationwide lockdown, restriction on the transportation system, demand-supply disequilibrium lead to slow down in the economy and create a fear factor among the participants of the capital market. Rapid fall in the share price and increased volatility are identified during this period. The present study tries to compare the stock price return volatility, no of the transaction, and delivery percentage of various listed companies listed on BSE during the pre and post COVID 19 periods to examine the effect of this pandemic on the economy as a whole. Period of Study: In this paper, we have consideredthe pre-covid period from 1st September 2019 to 15th March 2020 and post covid period from 16th March 2020 to August 2020. Sample: for this study, we have selected 50 BSE listed Companies covering 5 sectors, viz. Pharma, Automobile, Industrial Products, Banking and Finance, and Consumer Goods. Statistical Method: We have used paired sample t-test for comparing the arithmetical mean of different capital market parameters for these two sub-periods for each sector separately and standard deviation of daily return as a measure of volatility. Conclusion: From the study, we have observed that average daily share price; average daily return; daily no. of transactions and volatility is significantly different from pre and post covid period for most of the sectors. However, we have not perceived any significant difference in the delivery percentage of traded shares of these sectors between two study periods.

RISK-RETURN TRADEOFFS IN INDIAN STOCK MARKET: EVIDENCE FROM GARCH MODEL APPROACH

The objective is to examine the risk-return tradeoff in the Indian stock market. The sample period of study is from January 4, 2000 to December 31, 2020. The empirical results shows existence of risk-return tradeoff in the BSE. A positive risk-return tradeoff is found for monthly & annual return series. The market has weak risk-return relationship in daily return series. The CGARCH (1,1) captures the asymmetric volatility effect for all the different frequency based returns. The study has implications for the investors. The riskreturn relationship is stronger and significant in longer duration of investment. The market gives higher return when there is a high risk.

Return Anomalies in the Kuwaiti Stock Market

This paper intends to investigate the existence of daily return anomalies and the weekend effect within Boursa Kuwait, Kuwait’s stock exchange. Kuwait as an economy has continued to be opened up to foreign investment and as foreign funds being to flood into the market; return anomalies akin to those within international markets begin to materialize, bringing new opportunities for abnormal returns and arbitrage. The premise of this paper is the existence of the January effect and the Weekend effect, and uses econometric methods in support of their existence, bringing into question the challenges to market efficiency and the changing landscape for investors and their strategies.

The impact of COVID-19 on the standard & poor 500 index sectors: a multivariate generalized autoregressive conditional heteroscedasticity model

Purpose This paper aims to examine the impact of the daily growth rate of COVID-19 cases in the USA (COVIDg), the Federal Fund Rate (FFR) and the trade-weighted US dollar index (USDX) on S&P500 index daily returns and its 11 constituent sectors’ indices for the time period between January 22, 2020, until June 30, 2020. Design/methodology/approach The study uses the multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) model to gauge the impacts over the whole period of study, as well as over two sub-periods; first, January 22, 2020, until March 30, 2020, reflecting uncertainty in the US markets and second, from April 1, 2020, until June 30, 2020, reflecting the lockdown. Findings Results of the MGARCH model reveal a negative and significant relation between COVIDg and S&P500 index daily returns over the first sub-period and the whole study period in the following sectors, namely, communications, consumer discretionary, consumer staples, health, technology and materials. Yet, COVIDg showed a positive and significant relation with S&P500 index daily returns during the second time period in the following sectors, namely, communication, consumer discretionary, financial, industrial, information technology (IT) and utilities. Besides, USDX showed a negative significant effect on S&P500 index daily returns and on the daily return on each of its 11 constituent sectors over the second sub-period and the whole period. Further, FFR showed a significant effect only in the second sub-period, specifically, a negative effect on the daily return of the financial sector and a positive effect on the daily return of the technology sector index. Nevertheless, FFR had a positive significant effect on the daily return of the utilities sector index for the whole period under study. Research limitations/implications The impact of the crisis on the S&P500 index can be assessed only with some limitations owing to available global data and the limited time frame of the lock-down. Practical implications The study proposes supporting a smooth, functioning and resilient financial system; increasing fiscal measures by the US Government to increase liquidity on constraints; measures by The Federal Reserve to alleviate US dollar funding shortages; support market integrity; ensure continuous transparency and sharing of information; support the health sector, as well as consumer-based sectors that faced demand shocks and facilitate investments in the technology sector. Originality/value The originality of this paper lies in the examination of the impact of the novel COVID-19 pandemic on each of the 11 sectors constituting the S&P500 index separately, reflecting how the main economic sectors formulating the US economy reacted to the shock during the peak time of the pandemic to observe a full picture of the economic consequences amid the pandemic.

Risiko Pasar Saham Perbankan Syariah dengan Metode Standar Deviasi Markowitz dan Value At Risk (Var)

<p>This study describes the measurement of market risk in Islamic banking by calculating the Markowitz standard deviation and the market risk Value at Risk (VaR). The data used in this study are Islamic bank stocks in the Indonesia Stock Exchange, namely in JII or ISSI. Data obtained from reference sources and using secondary data. The observation period carried out is for 108 days from January 2 to June 11, 2020, with a daily period. The research methodology used to measure the greatest potential risk (loss) incurred in investing in the telecommunications stock index is the Markowitz standard deviation approach and the Value at Risk (VaR) using the Historical and Modeling (Analytical) methods. The results showed that the recommendations for efficient stock selection from the three banks were BRIS banks with an E (R) of 8.5% and a risk level of 1.33%. The lowest portfolio return of -0.1109% occurs on the 60th day and the highest portfolio return of 0.0769% occurs on the 47th day. Then On the daily 95% VAR, the lowest 5% return on a daily basis for BRIS is 5% from 108 days, occurs on the 85th day of -0.248% and the highest return occurs on the 47th day of 0.1444%. At the daily 95% VAR, the lowest 5% daily return of BTPN SYARIAH is 5% from 108 days, occurs on the 67th day of -0.247% and the highest return occurs on the 47th day of 0.758%. At the daily 95%, VAR with 5% lowest daily return of PANIN SYARIAH is 5% from 108 days. It does not happen, likewise, the highest return does not occur until the 108th day (the day of this research observation). If we have PANIN SYARIAH shares of Rp. 1 billion then there is no market risk (loss rate) at 95% daily. For a 5% possibility of tomorrow's portfolio loss on BRIS shares.</p>

Regime specific spillover across cryptocurrencies and the role of COVID-19

The aim of this study is to examine the daily return spillover among 18 cryptocurrencies under low and high volatility regimes, while considering three pricing factors and the effect of the COVID-19 outbreak. To do so, we apply a Markov regime-switching (MS) vector autoregressive with exogenous variables (VARX) model to a daily dataset from 25-July-2016 to 1-April-2020. The results indicate various patterns of spillover in high and low volatility regimes, especially during the COVID-19 outbreak. The total spillover index varies with time and abruptly intensifies following the outbreak of COVID-19, especially in the high volatility regime. Notably, the network analysis reveals further evidence of much higher spillovers in the high volatility regime during the COVID-19 outbreak, which is consistent with the notion of contagion during stress periods.

NEPSE in Bollinger Bands

<abstract> <p>An investor uses the graphical presentation of Bollinger Bands to get signals of the ups and downs, as well the volatility of the market from the expansion and tightening of the UBB and LBB, reflecting higher and lower volatility. The percent (%) b helps determine the opportunities during extreme periods from the market, looking at the concentration of line graph at the value "0" or "1" reflecting the bearish and bullish trend, respectively. The Bandwidth Index was able to picture out the bullish trend with a squeeze at the upper band. The positive unimodality of Q for NEPSE daily return for the period of the fiscal year 1998–1999 to the fiscal year 2019–2020 indicated normality for the market return. Nevertheless, the results for the trading signals based on the Bollinger bands are seen as useful for an investor by giving a clear signal to "buy" or "sell". At the same time, relying only on Bollinger Bands with a specific period MA, i.e. the Bollinger Bands with a shorter moving average (MA) shows higher fluctuations and vice-versa, hence, could show false signals while choosing inappropriate MA, therefore, help of other technical analysis tools should be taken while going for an investment decision.</p> </abstract>