Volatilidade Dos ndices De AAAes Mid-Large Cap E Small Cap: Uma Investigaaao A Partir De Modelos ARIMA/GARCH (Volatility of Stock Index Mid-Large Cap and Small Cap: An Investigation from ARIMA/GARCH Models)

2014 ◽  
Author(s):  
Israel J S Felipe ◽  
Anderson Luiz Mol
Keyword(s):  
Stock Index ◽  
Garch Models

Early warning signals using AVaRs of infinitely divisible GARCH models — evidence from stock index markets

2015 ◽  
Vol 47 (43) ◽  
pp. 4630-4652 ◽  
Author(s):  
Chia-Chien Chang ◽  
Te-Chung Hu ◽  
Chiu-Fen Kao ◽  
Ya-Chi Chang
Keyword(s):  
Early Warning ◽  
Stock Index ◽  
Garch Models ◽  
Infinitely Divisible ◽  
Warning Signals ◽  
Early Warning Signals

scholarly journals PERAMALAN VOLATILITAS SAHAM MENGGUNAKAN MODEL EXPONENTIAL GARCH DAN THRESHOLD GARCH

2019 ◽  
Vol 8 (4) ◽  
pp. 309
Author(s):  
SITI RAHAYU NINGSIH ◽  
I WAYAN SUMARJAYA ◽  
KARTIKA SARI
Keyword(s):  
Asset Returns ◽  
Financial Data ◽  
Stock Index ◽  
Garch Models ◽  
Leverage Effect ◽  
Financial Asset ◽  
Asymmetric Volatility ◽  
High Volatility ◽  
Financial Asset Returns ◽  
Forecasting Volatility

In financial data there is asymmetric volatility, which denotes the different movements on conditional volatility of increase and decrease financial asset returns. The exponential GARCH and threshold GARCH models can be used to capture asymmetric volatility, called leverage effect. The aim of this research is to determine the best model between exponential GARCH and threshold GARCH models, and to know the results of forecasting volatility the LQ-45 stock index using the best model. The research showed that the best model to predicting volatility is EGARCH(2,1), because it has the smallest AIC value compared to other models. Then forecasting volatility of the LQ-45 stock index using EGARCH(2,1) showed that volatility increase from the first period until fourteenth period, this means that it has high volatility.

Estimating Long-Term Volatility on National Stock Exchange of India

2019 ◽  
pp. 229-237
Keyword(s):  
Stock Exchange ◽  
Stock Index ◽  
Garch Models ◽  
Conditional Heteroskedasticity ◽  
Large Sample ◽  
Market Capitalization ◽  
Stock Indices ◽  
Autoregressive Conditional Heteroskedasticity

The main objective of this chapter is to provide an elaborate framework on the long-term volatility of the National Stock Exchange of India based on Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models. The CNX-100 index is one of the most diversified Indian stock indices which includes over 38 sectors of the economy. This stock index represents about 81.57% of the free-floating market capitalization of stocks listed on the National Stock Exchange (NSE) of India from March 2014. Moreover, this book chapter empirically tested volatility clusters of CNX100 index using a large sample database from October 2007 to July 2014.

Effects of index option introduction on stock index volatility: a procedure for empirical testing based on SSC-GARCH models

2000 ◽  
Vol 10 (3) ◽  
pp. 323-341 ◽  
Author(s):  
LEONARDO BECCHETTI ◽  
ANDREA CAGGESE
Keyword(s):  
Stock Index ◽  
Garch Models ◽  
Empirical Testing ◽  
Index Option

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.

scholarly journals The Forecasting Ability of GARCH Models for the 2003–07 Crisis: Evidence from S&P500 Index Volatility

2012 ◽  
Vol 1 (1) ◽  
pp. 37-58
Author(s):  
Mahreen Mahmud
Keyword(s):  
Financial Crisis ◽  
Realized Volatility ◽  
Stock Index ◽  
Garch Models ◽  
Crisis Period ◽  
Forecasting Volatility ◽  
Stock Index Returns

This article studies the ability of the GARCH family of models to accurately forecast the volatility of S&P500 stock index returns across the financial crisis that affected markets in 2003–07. We find the GJR-GARCH (1,1) model to be superior in its ability to forecast the volatility of the initial crisis period (2003– 06) compared to its realized volatility, which acts as a proxy for the actual. This model is then extended to make forecasts for the crisis period. We conclude that the model’s ability to forecast volatility across the crisis is not substantially affected, thus supporting the use of the GARCH family of models in forecasting volatility.

scholarly journals WAVELET TRANSFORMS FOR THE STATISTICAL ANALYSIS OF RETURNS GENERATING STOCHASTIC PROCESSES

2001 ◽  
Vol 04 (03) ◽  
pp. 511-534 ◽  
Author(s):  
ENRICO CAPOBIANCO
Keyword(s):  
Data Analysis ◽  
Statistical Analysis ◽  
Stochastic Processes ◽  
High Frequency ◽  
Wavelet Transforms ◽  
Exploratory Data Analysis ◽  
Statistical Procedure ◽  
Stock Index ◽  
Garch Models ◽  
Exploratory Data

We study high frequency Nikkei stock index series and investigate what certain wavelet transforms suggest in terms of volatility features underlying the observed returns process. Several wavelet transforms are applied for exploratory data analysis. One of the scopes is to use wavelets as a pre-processing smoothing tool so to de-noise the data; we believe that this procedure may help in identifying, estimating and predicting the latent volatility. Evidence is shown on how a non-parametric statistical procedure such as wavelets may be useful for improving the generalization power of GARCH models when applied to de-noised returns.

scholarly journals Testing an Algorithm with Asymmetric Markov-Switching GARCH Models in US Stock Trading

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2346
Author(s):  
Oscar V. De la Torre-Torres ◽  
Dora Aguilasocho-Montoya ◽  
José Álvarez-García
Keyword(s):  
Markov Switching ◽  
Stock Index ◽  
Garch Models ◽  
Algorithmic Trading ◽  
Stock Trading ◽  
Markov Switching Models ◽  
Switching Models ◽  
Asymmetric Garch ◽  
The Impact ◽  
T Distribution

In the present paper, we extend the current literature in algorithmic trading with Markov-switching models with generalized autoregressive conditional heteroskedastic (MS-GARCH) models. We performed this by using asymmetric log-likelihood functions (LLF) and variance models. From 2 January 2004 to 19 March 2021, we simulated 36 institutional investor’s portfolios. These used homogenous (either symmetric or asymmetric) Gaussian, Student’s t-distribution, or generalized error distribution (GED) and (symmetric or asymmetric) GARCH variance models. By including the impact of stock trading fees and taxes, we found that an institutional investor could outperform the S&P 500 stock index (SP500) if they used the suggested trading algorithm with symmetric homogeneous GED LLF and an asymmetric E-GARCH variance model. The trading algorithm had a simple rule, that is, to invest in the SP500 if the forecast probability of being in a calm or normal regime at t + 1 is higher than 50%. With this configuration in the MS-GARCH model, the simulated portfolios achieved a 324.43% accumulated return, of which the algorithm generated 168.48%. Our results contribute to the discussion on using MS-GARCH models in algorithmic trading with a combination of either symmetric or asymmetric pdfs and variance models.

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