scholarly journals Extreme Value Volatility Estimators and Realized Volatility of Istanbul Stock Exchange: Evidence from Emerging Market

2016 ◽  
Vol 8 (8) ◽  
pp. 71 ◽  
Author(s):  
Hakki Ozturk ◽  
Umit Erol ◽  
Asli Yuksel
Keyword(s):  
Stock Exchange ◽  
Emerging Market ◽  
Realized Volatility ◽  
Extreme Value ◽  
Short Term ◽  
Forecasting Models ◽  
Major Index ◽  
Istanbul Stock Exchange ◽  
Forecasting Performance ◽  
The One

<p>This paper evaluates the forecasting performance of alternative models for the one-day ahead forecasts of BIST-30 index (Istanbul Stock Exchange- Borsa Istanbul major index that contains 30 blue-chip stocks) volatility. Realized volatility is used as the relevant benchmark for the evaluation of forecasts. We document evidence, which shows that realized volatility is a less noisy estimator than the daily square benchmark explaining more of the variation in the volatility. In addition; the benefit of using extreme value estimators as volatility proxies are discussed. It is empirically demonstrated that the extreme value estimators are 5 to 8 times more efficient than historical volatility measures. The use of extreme value estimators with simple forecasting models provide better short-term forecasts than the GARCH based volatility forecasts due to higher efficiency of extreme value estimators.</p>

scholarly journals Extreme Value Volatility Estimators and Realized Volatility of Istanbul Stock Exchange: Evidence from Emerging Market

2016 ◽  
Vol 8 (8) ◽  
pp. 73
Author(s):  
Hakki Ozturk ◽  
Umit Erol ◽  
Asli Yuksel
Keyword(s):  
Stock Exchange ◽  
Emerging Market ◽  
Realized Volatility ◽  
Extreme Value ◽  
Short Term ◽  
Forecasting Models ◽  
Major Index ◽  
Istanbul Stock Exchange ◽  
Forecasting Performance ◽  
The One

<p>This paper evaluates the forecasting performance of alternative models for the one-day ahead forecasts of BIST-30 index (Istanbul Stock Exchange- Borsa Istanbul major index that contains 30 blue-chip stocks) volatility. Realized volatility is used as the relevant benchmark for the evaluation of forecasts. We document evidence, which shows that realized volatility is a less noisy estimator than the daily square benchmark explaining more of the variation in the volatility. In addition; the benefit of using extreme value estimators as volatility proxies are discussed. It is empirically demonstrated that the extreme value estimators are 5 to 8 times more efficient than historical volatility measures. The use of extreme value estimators with simple forecasting models provide better short-term forecasts than the GARCH based volatility forecasts due to higher efficiency of extreme value estimators.</p>

scholarly journals Short-term earthquake forecasting experiment before and during the L’Aquila (central Italy) seismic sequence of April 2009

2015 ◽  
Vol 57 (6) ◽  
Author(s):  
Maura Murru ◽  
Jiancang Zhuang ◽  
Rodolfo Console ◽  
Giuseppe Falcone
Keyword(s):  
Central Italy ◽  
Aftershock Sequence ◽  
Earthquake Forecasting ◽  
Model Parameters ◽  
Short Term ◽  
Productivity Function ◽  
Epidemic Type Aftershock Sequence ◽  
Forecasting Performance ◽  
The One ◽  
Lower Magnitude

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p>In this paper, we compare the forecasting performance of several statistical models, which are used to describe the occurrence process of earthquakes in forecasting the short-term earthquake probabilities during the L’Aquila earthquake sequence in central Italy in 2009. These models include the Proximity to Past Earthquakes (PPE) model and two versions of the Epidemic Type Aftershock Sequence (ETAS) model. We used the information gains corresponding to the Poisson and binomial scores to evaluate the performance of these models. It is shown that both ETAS models work better than the PPE model. However, in comparing the two types of ETAS models, the one with the same fixed exponent coefficient (<span>alpha)</span> = 2.3 for both the productivity function and the scaling factor in the spatial response function (ETAS I), performs better in forecasting the active aftershock sequence than the model with different exponent coefficients (ETAS II), when the Poisson score is adopted. ETAS II performs better when a lower magnitude threshold of 2.0 and the binomial score are used. The reason is found to be that the catalog does not have an event of similar magnitude to the L’Aquila mainshock (M<sub>w</sub> 6.3) in the training period (April 16, 2005 to March 15, 2009), and the (<span>alpha)</span>-value is underestimated, thus the forecast seismicity is underestimated when the productivity function is extrapolated to high magnitudes. We also investigate the effect of the inclusion of small events in forecasting larger events. These results suggest that the training catalog used for estimating the model parameters should include earthquakes of magnitudes similar to the mainshock when forecasting seismicity during an aftershock sequence.</p></div></div></div>

Structural breaks in unbiased volatility estimator: Modeling and forecasting

2017 ◽  
Vol 11 (1) ◽  
pp. 27-50 ◽  
Author(s):  
Dilip Kumar
Keyword(s):  
Long Memory ◽  
Structural Breaks ◽  
Realized Volatility ◽  
Extreme Value ◽  
Trading Strategy ◽  
Risk Averse ◽  
Memory Property ◽  
Economic Significance ◽  
Forecasting Performance ◽  
Modeling And Forecasting

The study provides a framework to model the unbiased extreme value volatility estimator (The AddRS estimator) in presence of structural breaks. We observe that the structural breaks in the volatility based on the AddRS estimator can partly explain its long memory property. We evaluate the forecasting performance of the proposed framework and compare the results with the corresponding results of the models from the GARCH family. The forecasts evaluation exercises consider the cases when future breaks are known as well as unknown. Our findings indicate that the proposed framework outperform the sophisticated GARCH class of models in forecasting realized volatility. Moreover, we devise a trading strategy based on the forecasts of the variance to highlight the economic significance of the proposed framework. We find that a risk averse investor can make substantial gain using the volatility forecasts based on the proposed frameworks in comparison to the GARCH family of models.

scholarly journals Impact of managerial ownership on capital structure: a survey of Turkish firms

2008 ◽  
Vol 5 (2) ◽  
pp. 8-14
Author(s):  
Özgür Arslan
Keyword(s):  
Capital Structure ◽  
Stock Exchange ◽  
Emerging Market ◽  
Debt Maturity ◽  
Managerial Ownership ◽  
Insider Ownership ◽  
Istanbul Stock Exchange ◽  
Dividend Smoothing ◽  
The Relationship ◽  
Considerable Support

This paper investigates the relationship between insider ownership and capital structure decisions made by managers for an emerging market. Therefore, we survey managers of 103 firms listed in the Istanbul Stock Exchange (ISE). Our findings lend considerable support to our expectation that leverage, debt maturity and dividend issues reduce ability of managers to divert resources from value maximisation. However the same monitoring and disciplining tax is not observed for stock issues. Also, our findings document that managers of firms listed in the ISE do not opt to dividend smoothing policy. Finally, the results are in line with our expectation that, the more willing are the managers to reduce asymmetric information between them and shareholders, the higher their ownership level in firms.

Does underconfidence matter in short-term and long-term investment decisions? Evidence from an emerging market

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Maqsood Ahmad
Keyword(s):  
Decision Making ◽  
Stock Exchange ◽  
Emerging Market ◽  
Investment Decision ◽  
Investment Decisions ◽  
Investment Strategies ◽  
Short Term ◽  
Content Type ◽  
Investment Decision Making

PurposeThe purpose of this article is to clarify the mechanism by which underconfidence heuristic-driven bias influences the short-term and long-term investment decisions of individual investors, actively trading on the Pakistan Stock Exchange.Design/methodology/approachInvestors' underconfidence has been measured using a questionnaire, comprising numerous items, including indicators of short-term and long-term investment decision. In order to establish the influence of underconfidence on the investment decisions in both the short and long run, a 5-point Likert scale questionnaire has been used to collect data from the sample of 203 investors. The collected data were analyzed using SPSS and AMOS graphics software. Hypotheses were tested using structural equation modeling technique.FindingsThis article provides further empirical insights into the relationship between heuristic-driven biases and investment decision-making in the short and long run. The results suggest that underconfidence bias has a markedly negative influence on the short-term and long-term decisions made by investors in developing markets. It means that heuristic-driven biases can impair the quality of both short-term and long-term investment decisions.Practical implicationsThis article encourages investors to avoid relying on cognitive heuristics, namely, underconfidence or their feelings when making short-term and long-term investment strategies. It provides awareness and understanding of heuristic-driven biases in investment management, which could be very useful for finance practitioners' such as investor who plays at the stock exchange, a portfolio manager, a financial strategist/advisor in an investment firm, a financial planner, an investment banker, a trader/broker at the stock exchange or a financial analyst. But most importantly, the term also includes all those persons who manage corporate entities and are responsible for making its financial management strategies. They can improve the quality of their decision-making by recognizing their behavioral biases and errors of judgment, to which we are all prone, resulting in more appropriate investment strategies.Originality/valueThe current study is the first to focus on links between underconfidence bias and short-term and long-term investment decision-making. This article enhanced the understanding of the role that heuristic-driven bias plays in the investment management and more importantly, it went some way toward enhancing understanding of behavioral aspects and their influence on the investment decision-making in an emerging market. It also adds to the literature in the area of behavioral finance specifically the role of heuristics in investment strategies; this field is in its initial stage, even in developed countries, while, in developing countries, little work has been done.

scholarly journals Stock mechanics: Predicting recession in S&P500, DJIA and NASDAQ

Open Physics ◽  
2006 ◽  
Vol 4 (1) ◽  
Author(s):  
Çağlar Tuncay
Keyword(s):  
Power Law ◽  
Stock Exchange ◽  
Emerging Market ◽  
Original Method ◽  
Istanbul Stock Exchange

AbstractProposed in this paper is an original method assuming potential and kinetic energies for prices and for the conservation of their sum that has been developed for forecasting exchanges. Connections with a power law are shown. Semiempirical applications on the S&P500, DJIA, and NASDAQ predict a forthcoming recession in them. An emerging market, the Istanbul Stock Exchange index ISE-100 is found harboring a potential to continue to rise.

scholarly journals Pattern Of Short-Term Volatility Accentuation Within The Trading Day: An Investigation Of The U.S. And European Equity Markets

2011 ◽  
Vol 5 (2) ◽  
Author(s):  
Deniz Ozenbas
Keyword(s):  
New York ◽  
Stock Price ◽  
Price Discovery ◽  
Stock Exchange ◽  
Equity Markets ◽  
Price Volatility ◽  
Short Term ◽  
Major Index ◽  
Short Period ◽  
The Individual

Trading friction leads into accentuated stock price volatility over the short term. As such, short-term accentuated volatility can be viewed as symptomatic of a market with increased inefficiencies in the price discovery process. If price discovery is marked by price swings, runs and reversals, then short period (intra-day) volatility is heightened in that market. In this study, we use return series with various differencing intervals that are as short as half-hour and as long as two weeks to investigate the short-term volatility accentuation in five different equity markets: the Nasdaq Stock Market and the New York Stock Exchange in the US, and the London Stock Exchange, Deutsche Boerse and Euronext Paris in Europe. In all these markets, we investigate the individual stocks that make up a major index during the calendar year 2000. Variance-ratio statistics are employed to investigate the quality of these five markets. Results confirm an intra-day reverse J-shaped pattern of half-hour volatility in these markets. The evidence also suggests an accentuation of volatility during longer periods, such as 24-hour intervals. This accentuation appears to subside when we extend our differencing interval to longer periods such as one-week or two-week returns.

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