Abstract: The current study investigates the behaviour of time-varying parameters that are based on the score function of the predictive model density at time t. The mechanism to update the parameters over time is the scaled score of the likelihood function. The results revealed that there is high persistence of time-varying, as the location parameter is higher and the skewness parameter implied the departure of scale parameter from the normality with the unconditional parameter as 1.5. The results also revealed that there is a perseverance of the leptokurtic behaviour in stock returns which implies the returns are heavily tailed. Prior to model estimation, the White Neural Network test exposed that the stock price can be modelled by a GAS model. Finally, we proposed further researches specifically to model the existence of time-varying parameters with a more detailed model that encounters the heavy tail distribution of the series and computes the risk measure associated with the returns.
Abstract: In this talk, we introduce a newly developed quantile
function model that can be used for estimating conditional
distributions of financial returns and for obtaining multi-step ahead
out-of-sample predictive distributions of financial returns. Since we
forecast the whole conditional distributions, any predictive quantity
of interest about the future financial returns can be obtained simply
as a by-product of the method. We also show an application of the
model to the daily closing prices of Dow Jones Industrial Average
(DJIA) series over the period from 2 January 2004 - 8 October 2010.
We obtained the predictive distributions up to 15 days ahead for
the DJIA returns, which were further compared with the actually
observed returns and those predicted from an AR-GARCH model.
The results show that the new model can capture the main features
of financial returns and provide a better fitted model together with
improved mean forecasts compared with conventional methods. We
hope this talk will help audience to see that this new model has the
potential to be very useful in practice.
Abstract: In this paper it was examined the influence of margin
regulation on stock market volatility in EU 1993 – 2014. Regulating
margin requirements or haircuts for securities financing transactions
has for a long time been considered as a potential tool to limit the
build-up of leverage and dampen volatility in financial markets. The
margin requirement dictates how much investors can borrow against
these securities. Margin can be an important part of investment.
Using daily and monthly stock returns and there is no convincing
evidence that EU Regulation margin requirements have served to
dampen stock market volatility. In this paper was detected the
expected negative relation between margin requirements and the
amount of margin credit outstanding. Also, it confirmed that changes
in margin requirements by the EU regulation have tended to follow
than lead changes in market volatility. For the analysis have been
used the modified Levene statistics to test whether the standard
deviation of stock returns in the 25, 50 and 100 days preceding
margin changes is the same as that in the succeeding 25, 50 and 100
days. The analysis started in May 1993 when it was first empowered
to set the initial margin requirement and the last sample was in May
2014. To test whether margin requirements influence stock market
volatility over the long term, the sample of stock returns was divided
into 14 periods, according to the 14 changes in margin requirements.
Abstract: According to the existing literature, companies manage analysts’ expectations of their future earnings by issuing pessimistic
earnings guidance to meet the expectations. Consequently, one could expect that markets price this pessimistic bias in advance and penalize companies more for lowering the guidance than reward for beating the guidance. In this paper we confirm this empirically. In addition we show that although guidance revisions have a statistically significant relation to stock returns, that is not the case with the actual earnings surprise. Reason for this could be that, after the annual earnings report also information on future earnings power is given at the same time.
Abstract: This study investigates the relationship between 10
year bond value, Yen/U.S dollar exchange rate, non-farm payrolls (all
employs) and crude oil to U.S. Dow Jones Sustainability Index. A
GARCH model is used to test these relationships for the period
January 1st 1999 to January 31st 2008 using monthly data. Results
show that an increase of the 10 year bond and non farm payrolls (all
employs) lead to an increase of the D.J.S.I returns. On the contrary
the volatility of the Yen/U.S dollar exchange rates as well as the
increase of crude oil returns has negative effects on the U.S D.J.S.I
returns. This study aims at assisting investors to understand the
influences certain macroeconomic indicators have on the companies-
stock returns as reported by the D.J.S.I.
Abstract: In this paper, we apply the FM methodology to the
cross-section of Romanian-listed common stocks and investigate the
explanatory power of market beta on the cross-section of commons
stock returns from Bucharest Stock Exchange. Various assumptions
are empirically tested, such us linearity, market efficiency, the “no
systematic effect of non-beta risk" hypothesis or the positive
expected risk-return trade-off hypothesis. We find that the Romanian
stock market shows the same properties as the other emerging
markets in terms of efficiency and significance of the linear riskreturn
models. Our analysis included weekly returns from January
2002 until May 2010 and the portfolio formation, estimation and
testing was performed in a rolling manner using 51 observations (one
year) for each stage of the analysis.