Do High-Frequency Volatility Methods Improve the Accuracies of Risk Forecasts? Evidence from Stock Indexes and Portfolio

2020 ◽  
pp. 2150032
Author(s):  
Siqi Xu ◽  
Kun Yang ◽  
Yifeng Zhang ◽  
Bo Li
Keyword(s):  
High Frequency ◽  
Value At Risk ◽  
Financial Risk ◽  
Low Frequency ◽  
Stock Index ◽  
Extreme Risk ◽  
Volatility Models ◽  
Risk Forecasting ◽  
The Garch Model ◽  
Stock Indexes

Though the high-frequency volatility approaches are increasingly introduced to forecast financial risk in recent years, whether they can improve the accuracies of risk forecasts remains controversial. This paper compares the risk forecasting abilities of four pairs of low- and high-frequency volatility models, by calculating and evaluating the downside and upside value-at-risk and expected shortfall of stock indexes and portfolio. The empirical results show that, first, all the volatility models can well filter the serial dependence in the extremes, and the conditional standard deviation obtained from the GARCH model performs best in filtering the dependence. Secondly, the backtesting results of stock index and portfolio risk forecasts are consistent. More specifically, the traditional low-frequency volatility models produce more accurate risk forecasts in most cases, whereas the high-frequency volatility methods also manifest some advantages in the upside extreme risk forecasting.

Dynamic relation between global Islamic and conventional sectoral stock and bonds indexes

2020 ◽  
Vol 07 (02) ◽  
pp. 2050006
Author(s):  
Sukriye Tuysuz
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Stock Index ◽  
High Frequency Part ◽  
Dynamic Relation ◽  
The Relationship

This paper examines the relationship between 10 Global sectoral conventional and Islamic assets. For each sector, a conventional, an Islamic stock index and a bond are retained. The analyzed relations are done by taking into account diverse investment horizons by using MODWT and GARCH-DCC-type models. Our results indicate that adding bond indexes into a portfolio composed with conventional stock or Islamic stock is efficient. As for the correlations between conventional and Islamic sectoral indexes, they depend on the sector. Relations between returns of securities are quite similar to the relations between high-frequency part of these series and are very volatile at low frequency.

scholarly journals Chinese Stock Index Futures Price Fluctuation Analysis and Prediction Based on Complementary Ensemble Empirical Mode Decomposition

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Ruoyang Chen ◽  
Bin Pan
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Ensemble Empirical Mode Decomposition ◽  
Stock Index ◽  
Stock Index Futures ◽  
Futures Prices ◽  
Index Futures ◽  
Price Fluctuations ◽  
Mode Decomposition ◽  
Svm Model

Since the CSI 300 index futures officially began trading on April 15, 2010, analysis and predictions of the price fluctuations of Chinese stock index futures prices have become a popular area of active research. In this paper, the Complementary Ensemble Empirical Mode Decomposition (CEEMD) method is used to decompose the sequences of Chinese stock index futures prices into residue terms, low-frequency terms, and high-frequency terms to reveal the fluctuation characteristics over different time scales of the sequences. Then, the CEEMD method is combined with the Particle Swarm Optimization (PSO) algorithm-based Support Vector Machine (SVM) model to forecast Chinese stock index futures prices. The empirical results show that the residue term determines the long-term trend of stock index futures prices. The low-frequency term, which represents medium-term price fluctuations, is mainly affected by policy regulations under the analysis of the Iterated Cumulative Sums of Squares (ICSS) algorithm, whereas short-term market disequilibrium, which is represented by the high-frequency term, plays an important local role in stock index futures price fluctuations. In addition, in forecasting the daily or even intraday price data of Chinese stock index futures, the combination prediction model is superior to the single SVM model, which implies that the accuracy of predicting Chinese stock index futures prices will be improved by considering fluctuation characteristics in different time scales.

scholarly journals The Role of High-Frequency Intra-daily Data, Daily Range and Implied Volatility in Multi-period Value-at-Risk Forecasting

2013 ◽  
Vol 32 (6) ◽  
pp. 561-576 ◽  
Author(s):  
Dimitrios P. Louzis ◽  
Spyros Xanthopoulos-Sisinis ◽  
Apostolos P. Refenes
Keyword(s):  
At Risk ◽  
High Frequency ◽  
Value At Risk ◽  
Implied Volatility ◽  
Daily Range ◽  
Daily Data ◽  
Risk Forecasting

scholarly journals Complexity Changes in the US and China’s Stock Markets: Differences, Causes, and Wider Social Implications

Entropy ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 75
Author(s):  
Jianbo Gao ◽  
Yunfei Hou ◽  
Fangli Fan ◽  
Feiyan Liu
Keyword(s):  
Stock Market ◽  
Time Scales ◽  
Stock Markets ◽  
High Frequency ◽  
Large Scale ◽  
Low Frequency ◽  
Stock Index ◽  
Chinese Market ◽  
Index Data ◽  
The Us

How different are the emerging and the well-developed stock markets in terms of efficiency? To gain insights into this question, we compared an important emerging market, the Chinese stock market, and the largest and the most developed market, the US stock market. Specifically, we computed the Lempel–Ziv complexity (LZ) and the permutation entropy (PE) from two composite stock indices, the Shanghai stock exchange composite index (SSE) and the Dow Jones industrial average (DJIA), for both low-frequency (daily) and high-frequency (minute-to-minute)stock index data. We found that the US market is basically fully random and consistent with efficient market hypothesis (EMH), irrespective of whether low- or high-frequency stock index data are used. The Chinese market is also largely consistent with the EMH when low-frequency data are used. However, a completely different picture emerges when the high-frequency stock index data are used, irrespective of whether the LZ or PE is computed. In particular, the PE decreases substantially in two significant time windows, each encompassing a rapid market rise and then a few gigantic stock crashes. To gain further insights into the causes of the difference in the complexity changes in the two markets, we computed the Hurst parameter H from the high-frequency stock index data of the two markets and examined their temporal variations. We found that in stark contrast with the US market, whose H is always close to 1/2, which indicates fully random behavior, for the Chinese market, H deviates from 1/2 significantly for time scales up to about 10 min within a day, and varies systemically similar to the PE for time scales from about 10 min to a day. This opens the door for large-scale collective behavior to occur in the Chinese market, including herding behavior and large-scale manipulation as a result of inside information.

scholarly journals On Forecasting Taiwanese Stock Index Option Prices: The Role of Implied Volatility Index

2017 ◽  
Vol 9 (9) ◽  
pp. 133 ◽  
Author(s):  
Jying-Nan Wang ◽  
Hung-Chun Liu ◽  
Lu-Jui Chen
Keyword(s):  
Implied Volatility ◽  
Option Price ◽  
Garch Model ◽  
Predictive Performance ◽  
Stock Index ◽  
Additional Information ◽  
Volatility Index ◽  
Volatility Models ◽  
Rolling Window ◽  
The Garch Model

This paper aims to propose four volatility measures: The first is the GARCH model advocated by Bollerslev (1986); the second is the GARCHVIX model which extends the GARCH model by including the volatility index (VIX) as explanatory variable for volatility; the last two are HS20D and HS252D, which represent the historical volatilities generated by traditional rolling window technique with 20- and 252-day historical index returns data, respectively. We examine the price information on VIX to improve the predictive performance of GARCH model for valuing TAIEX stock index call options (TXO) over the period from January 2014 to May 2015. Empirical results firstly indicate that both the GARCH and GARCHVIX models consistently perform better than the historical volatility models for forecasting call value of TXO under different moneynesses. Secondly, the GARCHVIX model significantly outperforms the GARCH model for most cases, indicating that the GARCH-based option price forecasts can be effectively improved with the additional information contained in VIX. Finally, the use of GARCHVIX model can greatly reduce model mispricing especially for out-the-money TXO option case. Thus, volatility index is crucial for option traders to efficiently predict TXO option value with GARCH model.

Value-at-Risk Forecasting using Realized Volatility Models and GARCH-type Models

2013 ◽  
Vol 21 (2) ◽  
pp. 135-167
Author(s):  
Chan-Soo Jeon
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Realized Volatility ◽  
Distribution Model ◽  
Distribution Models ◽  
Volatility Models ◽  
Risk Forecasting ◽  
T Distribution ◽  
Daily Returns ◽  
Skewed T Distribution

The aim of this paper is to compare the performance of VaR (value-at-risk) using Realized Volatility Models (which use intraday returns) with VaR the performance of GARCH-type Models (which use daily returns) with three different distribution innovations (normal distribution, t-distribution, skewed t-distribution). In this paper, we empirically examine VaR forecast of korean stock market using KOSPI and KOSDAQ. Empirical results indicate that the Realized Volatility models is superior to the GARCH-type models in forecasting VaR. We also find Var forecast by skewed t-distribution model are more accurate than those using the normal and t-distribution models. Thus, VaR using Realized Volatility models and skewed t-distribution enhances the performance of risk management in Korean financial markets.

scholarly journals The Efficiency of GARCH Models in Realizing Value at Risk Estimates

2020 ◽  
Vol 14 (1) ◽  
pp. 32-50
Author(s):  
Tomáš Jeřábek
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Financial Risk ◽  
Market Risk ◽  
Conditional Volatility ◽  
Maximum Loss ◽  
Specific Distribution ◽  
Volatility Models ◽  
Volatility Model ◽  
Traditional Approaches

Market risk is an important type of financial risk that is usually caused by price fluctuations in financial markets. One determinant of market risk comprises Value at Risk (VaR), which is defined as the maximum loss that can be achieved within a certain time horizon and at a given reliability level. The aim of the article is to determine the importance of selecting conditional volatility model within the parametric and semi-parametric approach for VaR estimation. The results ascertained show that the application of these models tends to provide more accurate predictions of actual losses as compared to traditional approaches to VaR estimates. Overall, the application of conditional volatility models ensures that VaR estimates are more flexible to adapt to changing market conditions – especially in the periods associated with higher return volatility. Furthermore, the results show that the differences between individual models of contingent volatility are primarily determined by selecting the specific distribution of the standardized residue series

Sign in / Sign up

Export Citation Format

Share Document