Integer‐Valued Asymmetric GARCH Modeling

AbstractThis paper studies the option valuation problem of non-Gaussian and asymmetric GARCH models from a state-space structure perspective. Assuming innovations following an infinitely divisible distribution, we apply different estimation methods including filtering and learning approaches. We then investigate the performance in pricing S&P 500 index short-term options after obtaining a proper change of measure. We find that the sequential Bayesian learning approach (SBLA) significantly and robustly decreases the option pricing errors. Our theoretical and empirical findings also suggest that, when stock returns are non-Gaussian distributed, their innovations under the risk-neutral measure may present more non-normality, exhibit higher volatility, and have a stronger leverage effect than under the physical measure.

Download Full-text

Multivariate Analysis of Cryptocurrencies

Econometrics ◽

10.3390/econometrics9030028 ◽

2021 ◽

Vol 9 (3) ◽

pp. 28

Author(s):

Vincenzo Candila

Keyword(s):

Great Recession ◽

Market Value ◽

Conditional Correlation ◽

Dynamic Conditional Correlation ◽

Mixed Frequency ◽

The Great Recession ◽

The World ◽

Asymmetric Garch ◽

Portfolio Managers ◽

Points Of View

Recently, the world of cryptocurrencies has experienced an undoubted increase in interest. Since the first cryptocurrency appeared in 2009 in the aftermath of the Great Recession, the popularity of digital currencies has, year by year, risen continuously. As of February 2021, there are more than 8525 cryptocurrencies with a market value of approximately USD 1676 billion. These particular assets can be used to diversify the portfolio as well as for speculative actions. For this reason, investigating the daily volatility and co-volatility of cryptocurrencies is crucial for investors and portfolio managers. In this work, the interdependencies among a panel of the most traded digital currencies are explored and evaluated from statistical and economic points of view. Taking advantage of the monthly Google queries (which appear to be the factors driving the price dynamics) on cryptocurrencies, we adopted a mixed-frequency approach within the Dynamic Conditional Correlation (DCC) model. In particular, we introduced the Double Asymmetric GARCH–MIDAS model in the DCC framework.

Download Full-text

Information-Theoretic Measures and Modeling Stock Market Volatility: A Comparative Approach

Risks ◽

10.3390/risks9050089 ◽

2021 ◽

Vol 9 (5) ◽

pp. 89

Author(s):

Muhammad Sheraz ◽

Imran Nasir

Keyword(s):

Stock Market ◽

Stock Returns ◽

Stock Exchange ◽

Approximate Entropy ◽

Market Volatility ◽

Comparative Approach ◽

Stock Market Volatility ◽

Information Theoretic ◽

Information Theoretic Measures ◽

Garch Modeling

The volatility analysis of stock returns data is paramount in financial studies. We investigate the dynamics of volatility and randomness of the Pakistan Stock Exchange (PSX-100) and obtain insights into the behavior of investors during and before the coronavirus disease (COVID-19 pandemic). The paper aims to present the volatility estimations and quantification of the randomness of PSX-100. The methodology includes two approaches: (i) the implementation of EGARCH, GJR-GARCH, and TGARCH models to estimate the volatilities; and (ii) analysis of randomness in volatilities series, return series, and PSX-100 closing prices for pre-pandemic and pandemic period by using Shannon’s, Tsallis, approximate and sample entropies. Volatility modeling suggests the existence of the leverage effect in both the underlying periods of study. The results obtained using GARCH modeling reveal that the stock market volatility has increased during the pandemic period. However, information-theoretic results based on Shannon and Tsallis entropies do not suggest notable variation in the estimated volatilities series and closing prices. We have examined regularity and randomness based on the approximate entropy and sample entropy. We have noticed both entropies are extremely sensitive to choices of the parameters.

Download Full-text