Stock Market Volatility – Conceptual Perspective through Literature Survey

Mamtha, D.; Srinivasan, Kathiravan

doi:10.5901/mjss.2016.v7n1p208

Cited by 15 publications

(22 citation statements)

References 20 publications

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“…Measuring the stock market volatility is an incredibly complex job for researchers. Since volatility tends to cluster, if today’s volatility is high, it is likely to be high tomorrow but they have also had an attractive high hit rate with major disasters [ 4 , 7 , 11 , 12 ]. GARCH models have a strong background, recently having crossed 30 years of the fast progress of GARCH-type models for investigating the volatility of market data.…”

Section: Review Of Different Studiesmentioning

confidence: 99%

“…From this theme, this study’s authentic approach is compared with earlier models in the literature review for input variables used for forecasting volatility and how precise they are in analyzing the direction of the related time series. There are other review studies on return and volatility analysis and GARCH-family based financial forecasting methods done by a number of researchers [ 9 , 10 , 11 , 12 , 13 ]. Consequently, the aim of this manuscript is to put forward the importance of sufficient and necessary conditions for model selection and contribute for the better understanding of academic researchers and financial practitioners.…”

Section: Introductionmentioning

confidence: 99%

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Stock Market Volatility and Return Analysis: A Systematic Literature Review

Bhowmik

Wang

2020

Entropy

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In the field of business research method, a literature review is more relevant than ever. Even though there has been lack of integrity and inflexibility in traditional literature reviews with questions being raised about the quality and trustworthiness of these types of reviews. This research provides a literature review using a systematic database to examine and cross-reference snowballing. In this paper, previous studies featuring a generalized autoregressive conditional heteroskedastic (GARCH) family-based model stock market return and volatility have also been reviewed. The stock market plays a pivotal role in today’s world economic activities, named a “barometer” and “alarm” for economic and financial activities in a country or region. In order to prevent uncertainty and risk in the stock market, it is particularly important to measure effectively the volatility of stock index returns. However, the main purpose of this review is to examine effective GARCH models recommended for performing market returns and volatilities analysis. The secondary purpose of this review study is to conduct a content analysis of return and volatility literature reviews over a period of 12 years (2008–2019) and in 50 different papers. The study found that there has been a significant change in research work within the past 10 years and most of researchers have worked for developing stock markets.

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Section: Review Of Different Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stock Market Volatility and Return Analysis: A Systematic Literature Review

Bhowmik

Wang

2020

Entropy

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“…If there is a wide range of fluctuations in the prices over short time, it means that there is high volatility and vice versa. On the other hand, if the price moves slowly, there is low volatility [14]. The importance of volatility and returns and the tradeoff between these two stock indicators has received tremendous attentions.…”

Section: Financial Time Series Feature Vectormentioning

confidence: 99%

An autoencoder-based deep learning approach for clustering time series data

et al. 2020

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This paper introduces a two-stage deep learning-based methodology for clustering time series data. First, a novel technique is introduced to utilize the characteristics (e.g., volatility) of the given time series data in order to create labels and thus enable transformation of the problem from an unsupervised into a supervised learning. Second, an autoencoderbased deep learning model is built to model both known and hidden non-linear features of time series data. The paper reports a case study in which the selected financial and stock time series data of over 70 stock indices are clustered into distinct groups using the introduced two-stage procedure. The results show that the proposed methodology is capable of achieving 87.5% accuracy in clustering and predicting the labels for unseen time series data. The paper also reports an important finding in which it is observed that the performance of both techniques (i.e., autoencoder and Kmeans) are comparable. However, there are a few instances of time series data that are classified differently by the autoencoder-based methodology compared to the Kmeans algorithm. The results may indicate that the proposed deep learning-based approach is taking into account additional hidden features that might be overlooked by conventional Kmeans. The finding raises the question whether the explicit features of data should be analyzed for clustering or more advanced techniques such as deep learning need to be adapted by which hidden features and relationships are explored for clustering purposes.

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“…The pertinent financial literature on the relationship between macroeconomic factors and stock return volatility is not very mature and symmetric around both developed and emerging markets (Mamtha & Srinivasan, 2016). Moreover, the focus of these studies is mainly on how the volatility in macroeconomic fundamentals affects the volatility of stock returns at aggregate market levels using various volatility models (Khan, Khan, & Khan, 2016).…”

Section: Empirical Relationshi P Between Macroeconomic Factors and Stmentioning

confidence: 99%

Return Volatility and Macroeconomic Factors: A Comparison of US and Pakistani Firms

Jan¹,

Khan²

2018

BER

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This study explores the effect of selected economic factors on stock return volatility along with asymmetry and leverage effects on comparative basis of the New York Stock Exchange (NYSE) and Pakistan Stock Exchange (PSX). These dimensions are further investigated with respect to size and age of the firms. The daily stock returns of all the firms on both NYSE and PSX and macroeconomic factors are considered for the period 2000-2015. The results from GARCH (1, 1) revealed that all the economic factors have proven their significance in determining the stock returns volatility in both the markets with respect to firm's size and age. More precisely, a negative relationshi p is observed for market returns (MR), exchange rate (EXR), and oil returns (OIL) with stock return volatility for majority of the firms in both stock markets. Furthermore, risk-free rate (RFR) showed positive and negative effect on the stock return volatility of majority of the firms in NYSE and PSX markets respectively. However, with respect to size and age effects, firms in both the markets exhibited entirely different behavior for all the macroeconomic factors. Further, using EGARCH model, an evidence of asymmetry and leverage effect (with negative coefficient) is found in NYSE and partial evidence (both negative and positive coefficients) in PSX is observed. Again, these results vary with respect to firms' features in both the markets. Therefore, the results of the current study clearly show that there are significant differences in both markets and the investors can diversify their investments and shape their liquidity positions in both markets in order to exploit the maximum benefits from the market and firms specific factors.

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Stock Market Volatility – Conceptual Perspective through Literature Survey

Abstract: In recent years, understanding the volatility has become more significant among investors.

Cited by 15 publications

References 20 publications

Stock Market Volatility and Return Analysis: A Systematic Literature Review

Stock Market Volatility and Return Analysis: A Systematic Literature Review

An autoencoder-based deep learning approach for clustering time series data

Return Volatility and Macroeconomic Factors: A Comparison of US and Pakistani Firms

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