Page 192 - IJOCTA-15-1

H.H. Yildirim, A. Akusta / IJOCTA, Vol.15, No.1, pp.183-201 (2025)
for financial analysts and investors, offering en- Introduces functional data analysis for volatil-
hanced tools for risk assessment and investment ity, helping identify persistent patterns and pre-
planning. Furthermore, the utilization of senti- dict future volatility through functional regression
ment analysis on financial disclosures has demon- techniques. 41 This methodology improves volatil-
strated potential in forecasting volatility by in- ity modeling, providing more accurate forecasts
tegrating textual and market data sources, as for financial decision-making.
evidenced by. 34 Integrating these disparate data
Volatility spillover and market dynamics high-
sets through fusion methods enhances the preci- light the interconnectedness of financial markets
sion of forecasts, thereby providing a comprehen- and the impact of external factors on volatil-
sive understanding of market dynamics. More- ity. Presents a semiparametric approach for
over, comparative studies of MIDAS and HAR continuous-time volatility regression in large pan-
volatility forecasting models, such as the work by, els, enhancing understanding of relationships be-
emphasize the significance of integrating diverse tween noisy processes and stochastic volatility. 42
volatility measures, including realized variance This approach can improve risk management by
and power variation, to enhance out-of-sample providing more accurate volatility forecasts for
accuracy. 35 These developments in volatility pre- large datasets.
diction techniques illustrate the increasing signifi-
Uses multivariate regression analysis to show that
cance of both text-based models and conventional
oil price volatility significantly impacts Kenya’s
financial methodologies in enhancing forecasting 43
GDP growth rate. Policymakers should consider
precision.
stabilizing oil prices and controlling inflation to
The relationship between financial ratios and mitigate negative impacts on GDP growth. Con-
stock price volatility is another important area of ducted a meta-regression analysis, revealing that
research. Discuss different approaches to deter- exchange rate volatility negatively affects interna-
mining stock market volatility and the role of op- tional trade. This suggests that policymakers can
tion premiums. Understanding these approaches implement measures to stabilize exchange rates
can help investors and portfolio managers use and support international trade. 44
derivatives to hedge risks and optimize financial Explore the asymmetric relationship between
decisions. 36
price and implied volatility using linear and non-
Examine the Brazilian stock market, identifying linear quantile regression, providing insights into
key determinants such as assets with higher port- portfolio optimization and risk management. 45
folio weight and foreign currency rates. 37 These Quantile regression models can help investors op-
findings suggest that investors should consider timize their portfolios and develop effective hedg-
these factors when assessing market volatility, and ing strategies. Analyzes the determinants of
policymakers might focus on economic recovery stock price volatility at the Nairobi Securities
efforts to stabilize markets. Find that dividend Exchange, offering valuable insights for local in-
payout ratio, earnings volatility, and firm size sig- vestors. Understanding these determinants can
nificantly impact stock price volatility in the ce- help investors make informed decisions and man-
ment industry. 38 This highlights the importance age risks more effectively. 46
of managing dividend policies and other financial
metrics to reduce volatility.
Uses quantile regression to explore the asymmet- 3. Data and methodology
ric return-volatility relationship, supporting be-
havioral explanations like the affect heuristic. 39 Building upon the theoretical framework in the
This indicates that incorporating behavioral fac- literature review, this research now turns to the
tors into volatility models can enhance the accu- empirical analysis of volatility among BIST100
racy of risk assessments and investment strate- firms. This study focuses on a comprehensive
gies. Propose a regression-based approach to analysis of volatility among firms listed on the
capture level dependence in stock return volatil- BIST100 index from 2006 to 2023. The dataset
ity, finding an upward bias in the Cov Ratio comprises daily trading data of selected non-
estimator. 40 This suggests that while the Cov Ra- financial companies in the BIST100 during this
tio is applicable, adjustments may be needed to period.
account for biases. Moving beyond firm-specific The sample was refined to exclude financial com-
factors, we must consider the broader market con- panies and firms with incomplete data records,
text and the interconnected nature of global finan- resulting in a focused dataset of 46 firms (Appen-
cial systems. dix 1). This careful selection process ensures a
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