scholarly journals Forecasting Volatility: Evidence from the Saudi Stock Market

2018 ◽  
Vol 11 (4) ◽  
pp. 84 ◽  
Author(s):  
Naseem Al Rahahleh ◽  
Robert Kao
Keyword(s):  
Stock Market ◽  
Policy Makers ◽  
Forecasting Accuracy ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
Asymmetric Power ◽  
International Investors ◽  
Forecasting Volatility ◽  
Class Models ◽  
Petrochemical Industries

The purpose of this paper is to evaluate the forecasting performance of linear and non-linear generalized autoregressive conditional heteroskedasticity (GARCH)–class models in terms of their in-sample and out-of-sample forecasting accuracy for the Tadawul All Share Index (TASI) and the Tadawul Industrial Petrochemical Industries Share Index (TIPISI) for petrochemical industries. We use the daily price data of the TASI and the TIPISI for the period of 10 September 2007 to 26 February 2015. The results suggest that the Asymmetric Power of ARCH (APARCH) model is the most accurate model in the GARCH class for forecasting the volatility of both the TASI and the TIPISI in the context of petrochemical industries, as this model outperforms the other models in model estimation and daily out-of-sample volatility forecasting of the two indices. This study is useful for the dataset examined, because the results provide a basis for traders, policy-makers, and international investors to make decisions using this model to forecast the risks associated with investing in the Saudi stock market, within certain limitations.

scholarly journals Measuring and Forecasting Volatility in Chinese Stock Market Using HAR-CJ-M Model

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Chuangxia Huang ◽  
Xu Gong ◽  
Xiaohong Chen ◽  
Fenghua Wen
Keyword(s):  
Stock Market ◽  
Sample Path ◽  
Realized Volatility ◽  
The Other ◽  
Chinese Stock Market ◽  
Momentum Effect ◽  
Forecasting Performance ◽  
The Future ◽  
Forecasting Volatility ◽  
Continuous Volatility

Basing on the Heterogeneous Autoregressive with Continuous volatility and Jumps model (HAR-CJ), converting the realized Volatility (RV) into the adjusted realized volatility (ARV), and making use of the influence of momentum effect on the volatility, a new model called HAR-CJ-M is developed in this paper. At the same time, we also address, in great detail, another two models (HAR-ARV, HAR-CJ). The applications of these models to Chinese stock market show that each of the continuous sample path variation, momentum effect, and ARV has a good forecasting performance on the future ARV, while the discontinuous jump variation has a poor forecasting performance. Moreover, the HAR-CJ-M model shows obviously better forecasting performance than the other two models in forecasting the future volatility in Chinese stock market.

APPLING GREY FORECASTING METHOD TO FORECAST THE PORTFOLIO’S RATE OF RETURN IN STOCK MARKET OF IRAN

2012 ◽  
Vol 01 (07) ◽  
pp. 01-16
Author(s):  
Ali Mohammadi ◽  
Sara Zeinodin Zade
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Predictive Ability ◽  
Grey Model ◽  
Rate Of Return ◽  
Forecasting Accuracy ◽  
Grey Forecasting ◽  
Best Value ◽  
Forecasting Method ◽  
Time Series Method

Stock market is one of the most important investment market, which influenced by many factors, therefore it needs a robust and accurate forecasting. In this study ,grey model used as a forecasting method and examined if it is the most reliable forecasting method in comparison of time series method. The information of portfolio’s rate of return is gathered from 50 accepted companies in Tehran stock market, which were announced as the best companies last year. Mean Square of the errors (MSE) is computed by different value of α in grey model which could be varied between .1 to .9 ,to examined if α=.5 is the best value that our model could take .Then the predictive ability of the model is compared with different type of time series based forecasting methods Experimental results confirm forecasting accuracy of grey model. Tracking signal is computed for grey model to see whether grey model forecasting is in control or not. At the last portfolio’s rate of return is forecasted for next periods.

Αποτίμηση κινδύνων των ευρωπαϊκών τραπεζών και συστήματα έγκαιρης προειδοποίησης

2018 ◽  
Author(s):  
Παντελής Σταυρούλιας
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Cross Validation ◽  
Multinomial Logistic Regression ◽  
Classification Tree ◽  
Probit Regression ◽  
Macroprudential Policy ◽  
Policy Makers ◽  
Out Of Sample ◽  
Sample Testing

Οι έγκυρες προβλέψεις χρηματοοικονομικών κρίσεων διασφάλιζαν ανέκαθεν την σταθερότητα τόσο ολόκληρου του χρηματοοικονομικού οικοδομήματος γενικότερα, όσο και του τραπεζικού τομέα ειδικότερα. Με την παρούσα διατριβή επιτυγχάνεται η πρόβλεψη συστημικών τραπεζικών κρίσεων για χώρες της EE-14 αρκετά τρίμηνα προτού αυτές γίνουν αντιληπτές με την χρησιμοποίηση των πιο διαδεδομένων μεταβλητών (μακροοικονομικών, τραπεζικών και αγοράς) μέσω δύο προσεγγίσεων, της δυαδικής και της πολυεπίπεδης. Ακολουθώντας τη δυαδική προσέγγιση, εξάγονται μοντέλα ταξινόμησης με την εφαρμογή της Διακριτής Ανάλυσης (Discriminant Analysis), της Γραμμικής Παλινδρόμησης (Linear Regression), της Λογιστικής Παλινδρόμησης (Logistic Regression) και της Παλινδρόμησης Πιθανοομάδας (Probit Regression), για την έγκαιρη πρόβλεψη των κρίσεων -12 έως -7 τρίμηνα πριν την εμφάνισή τους. Επιπροσθέτως, συγκρίνεται η απόδοση της ανωτέρω ανάλυσης χρησιμοποιώντας τις νεότερες και πλέον υποσχόμενες μεθόδους του Δέντρου Ταξινόμησης (Classification Tree), του Τυχαίου Δάσους (Random Forest) και της C5. Ταυτόχρονα προτείνεται ένα νέο μέτρο επιλογής κατωφλίων και απόδοσης προσαρμογής (GoF) των μοντέλων πρόβλεψης και μια νέα συνδυαστική (combined) μέθοδος ταξινόμησης. Προκειμένου να διερευνηθεί η απόδοση της ανωτέρω ανάλυσης, χρησιμοποιείται ο εκτός του δείγματος έλεγχος (out-of-sample testing) με τη μέθοδο της ανά χώρα σταυρωτής επικύρωσης (country-blocked cross validation). Σύμφωνα με τη μέθοδο αυτή, πραγματοποιείται η ανάλυση και εξάγονται τα μοντέλα πρόβλεψης με τη χρήση των δεκατριών από τις δεκατέσσερις χώρες του δείγματος (in-sample), εφαρμόζονται τα εξαγόμενα μοντέλα για την δέκατη τέταρτη χώρα που είχε εξαιρεθεί από το αρχικό δείγμα (out-of-sample) και ελέγχονται τα αποτελέσματα πρόβλεψης με τα πραγματικά δεδομένα της χώρας αυτής. Η παραπάνω διαδικασία επαναλαμβάνεται δεκατέσσερις φορές, αφήνοντας δηλαδή κάθε φορά μια χώρα εκτός δείγματος και τελικά εξάγεται ο μέσος όρος των επαναλήψεων. Στην παρούσα διατριβή, και χρησιμοποιώντας τον εκτός του δείγματος έλεγχο, επιτυγχάνεται η κατά 82.4% σωστή ταξινόμηση (Ακρίβεια – Accuracy), 78.4% ποσοστό Αληθινών Θετικών (Τrue Ρositive Rate - TPR) και 80.6% ποσοστό Θετικής Τιμής Πρόβλεψης (Positive Predictive Value - PPV). Σύμφωνα με την πολυεπίπεδη προσέγγιση, διακρίνονται δύο επίπεδα-περίοδοι πρόβλεψης των Συστημικών Τραπεζικών Κρίσεων. Το πρώτο επίπεδο ονομάζεται έγκαιρη πρόβλεψη (early warning) και αφορά περίοδο -12 έως -7 τρίμηνα πριν την έλευση της κρίσης ενώ το δεύτερο επίπεδο ονομάζεται καθυστερημένη πρόβλεψη (late warning) και αφορά περίοδο -6 έως -1 τρίμηνα πριν την έλευση της κρίσης. Για την πολυεπίπεδη αυτή ταξινόμηση, γίνεται χρήση των Νευρωνικών Δικτύων (Neural Networks), της Πολυωνυμικής Λογιστικής Παλινδρόμησης (Multinomial Logistic Regression) και της Πολυεπίπεδης Γραμμικής Διακριτής Ανάλυσης (Multinomial Discriminant Analysis). Εφαρμόζοντας τον ίδιο εκτός του δείγματος έλεγχο με την πρώτη προσέγγιση επιτυγχάνεται η κατά 85.7% σωστή ταξινόμηση με την βέλτιστη μέθοδο που αποδεικνύεται ότι είναι η Πολυεπίπεδη Γραμμική Διακριτή Ανάλυση. Εφαρμόζοντας την ανωτέρω ανάλυση, οι ενδιαφερόμενοι φορείς άσκησης πολιτικής (policy makers) μπορούν να ανιχνεύσουν την ύπαρξης κρίσης σε βάθος χρόνου έως τριών ετών με τα προτεινόμενα μοντέλα, χρησιμοποιώντας μόνο δεδομένα που υπάρχουν ελεύθερα προσβάσιμα στο κοινό, ασκώντας με τον τρόπο αυτό την κατάλληλη ανά περίπτωση μακροπροληπτική πολιτική (macroprudential policy).

scholarly journals Is Abnormally Large Volume a Clue?

2016 ◽  
Vol 8 (9) ◽  
pp. 226
Author(s):  
Tsung-Hsun Lu ◽  
Jun-De Lee
Keyword(s):  
Stock Market ◽  
Large Volume ◽  
Stock Prices ◽  
Stock Price ◽  
Trading Volume ◽  
Sensitive Analysis ◽  
Support Level ◽  
Out Of Sample ◽  
Sample Test ◽  
Abnormal Trading Volume

This paper investigates whether abnormal trading volume provides information about future movements in stock prices. Utilizing data from the Taiwan 50 Index from October 29, 2002 to December 31, 2013, the researchers employ trading volume rather than stock price to test the principles of resistance and support level employed by technical analysis. The empirical results suggest that abnormal trading volume provides profitable information for investors in the Taiwan stock market. An out-of-sample test and a sensitive analysis are conducted for the robustness of the results.

scholarly journals Tail-Heaviness, Asymmetry, and Profitability Forecasting by Quantile Regression

2020 ◽  
Author(s):  
Hui Tian ◽  
Andrew Yim ◽  
David P. Newton
Keyword(s):  
Quantile Regression ◽  
Forecast Error ◽  
Simulated Data ◽  
Ordinary Least Squares ◽  
Shape Effect ◽  
Forecasting Accuracy ◽  
Forecasting Performance ◽  
Cash Flow Forecasting ◽  
Positive Effect ◽  
Better Than

We show that quantile regression is better than ordinary-least-squares (OLS) regression in forecasting profitability for a range of profitability measures following the conventional setup of the accounting literature, including the mean absolute forecast error (MAFE) evaluation criterion. Moreover, we perform both a simulated-data and an archival-data analysis to examine how the forecasting performance of quantile regression against OLS changes with the shape of the profitability distribution. Considering the MAFE and mean squared forecast error (MSFE) criteria together, we see that the quantile regression is more accurate relative to OLS when the profitability to be forecast has a heavier-tailed distribution. In addition, the asymmetry of the profitability distribution has either a U-shape or an inverted-U-shape effect on the forecasting accuracy of quantile regression. An application of the distributional shape analysis framework to cash flow forecasting demonstrates the usefulness of the framework beyond profitability forecasting, providing additional empirical evidence on the positive effect of tail-heaviness and supporting the notion of an inverted-U-shape effect of asymmetry. This paper was accepted by Shiva Rajgopal, accounting.

scholarly journals The predictive capacity of GARCH-type models in measuring the volatility of crypto and world currencies

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245904
Author(s):  
Viviane Naimy ◽  
Omar Haddad ◽  
Gema Fernández-Avilés ◽  
Rim El Khoury
Keyword(s):  
Value At Risk ◽  
Risk Measure ◽  
Canadian Dollar ◽  
Swiss Franc ◽  
Garch Models ◽  
Predictive Capacity ◽  
Australian Dollar ◽  
Forecasting Performance ◽  
Out Of Sample ◽  
Japanese Yen

This paper provides a thorough overview and further clarification surrounding the volatility behavior of the major six cryptocurrencies (Bitcoin, Ripple, Litecoin, Monero, Dash and Dogecoin) with respect to world currencies (Euro, British Pound, Canadian Dollar, Australian Dollar, Swiss Franc and the Japanese Yen), the relative performance of diverse GARCH-type specifications namely the SGARCH, IGARCH (1,1), EGARCH (1,1), GJR-GARCH (1,1), APARCH (1,1), TGARCH (1,1) and CGARCH (1,1), and the forecasting performance of the Value at Risk measure. The sampled period extends from October 13th 2015 till November 18th 2019. The findings evidenced the superiority of the IGARCH model, in both the in-sample and the out-of-sample contexts, when it deals with forecasting the volatility of world currencies, namely the British Pound, Canadian Dollar, Australian Dollar, Swiss Franc and the Japanese Yen. The CGARCH alternative modeled the Euro almost perfectly during both periods. Advanced GARCH models better depicted asymmetries in cryptocurrencies’ volatility and revealed persistence and “intensifying” levels in their volatility. The IGARCH was the best performing model for Monero. As for the remaining cryptocurrencies, the GJR-GARCH model proved to be superior during the in-sample period while the CGARCH and TGARCH specifications were the optimal ones in the out-of-sample interval. The VaR forecasting performance is enhanced with the use of the asymmetric GARCH models. The VaR results provided a very accurate measure in determining the level of downside risk exposing the selected exchange currencies at all confidence levels. However, the outcomes were far from being uniform for the selected cryptocurrencies: convincing for Dash and Dogcoin, acceptable for Litecoin and Monero and unconvincing for Bitcoin and Ripple, where the (optimal) model was not rejected only at the 99% confidence level.

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