| dc.description.abstract | This article targets the volatility of publicly listed companies in the U.S. durable
goods, energy, technology, and manufacturing sectors, utilizing the Inter-QuantileRange-based Volatility (IQRBV) as a proxy for volatility forecasting. The data spans
from January 1990 to December 2021 with a monthly frequency, incorporating 414
variables across categories including financial fundamentals, operations, technology,
supply, corporate social responsibility, as well as sentiment and macroeconomic
indicators that represent additional future information. Dimensionality reduction
algorithms are employed to extract categorical factors, which are then input into
quantile regression to predict volatility at the 10%, 50%, and 90% quantiles for the
construction of IQRBV.
Among the four sectors, technology exhibits the best performance in volatility
forecasting, followed by energy. Moreover, the factors contributing to volatility evolve
over time, indicating the critical importance of selecting appropriate variables and
models for volatility forecasting under different temporal and economic contexts. Most
importantly, the models developed in this study are capable not only of forecasting
industry-specific upward and downward volatility but also of integrating these
volatilities with economic factors to delve deeper into the underlying drivers of
volatility. This approach aids market participants in enhancing the substantiation of
their decision-making processes, thereby improving the causal relationship between
asset allocation and risk management decisions. | en_US |