Regime-Dependent Volatility and Return Dynamics: A Comparative RS-DCC-GARCH Analysis of Large- and Small-Cap Stocks

About the Project

The long-standing notion that small-cap stocks exhibit a “high-risk, high-return” pattern has been a cornerstone of asset pricing theory since Fama and French (1993) introduced their influential three-factor model. Numerous studies, including Switzer (2010), Blitz (2016), and Blitz, Van Vliet, and Baltussen (2019), provide empirical evidence supporting this relationship. These works generally uphold the traditional positive link between volatility and abnormal returns, challenging the persistence of the so-called low-volatility effect.

However, recent financial markets suggest that even large-cap, technology-driven firms can display extreme volatility. Evidence shows that volatility and cross-asset correlations are not static but evolve dynamically in response to financial crises, monetary policy changes, and structural market shifts. In the post–COVID-19 era, several large-cap U.S. firms have exhibited unusually high price volatility, contradicting the expectation that large-cap equities are inherently stable.

For instance, Tesla (TSLA) has consistently shown a beta exceeding 2, indicating amplified sensitivity to market movements, while NVIDIA (NVDA) has experienced substantial post-earnings price swings. Similarly, Palantir (PLTR) and Arm (ARM) are large-cap firms characterised by elevated beta values, reflecting their exposure to rapidly changing industries such as artificial intelligence and semiconductors. Shopify (SHOP) further demonstrates that even large e-commerce growth firms can undergo substantial price fluctuations.

These observations raise a crucial question: do large-cap stocks still exhibit low volatility? The evidence implies that large market capitalization alone no longer guarantees stability. Instead, sectoral dynamics, growth expectations, and investor sentiment play increasingly significant roles. For researchers examining the low-volatility effect and large-cap stock behaviour, these firms serve as compelling examples that challenge traditional assumptions. The growing prevalence of high-volatility large-cap stocks may indicate structural shifts in equity markets, calling into question whether the Fama–French size factor remains valid in the modern investment landscape.

Accordingly, this research aims to empirically test whether the traditional risk–return trade-off associated with small-cap stocks continues to play a central role in asset pricing. Moreover, it seeks to determine whether small-cap outperformance intensifies under specific market conditions, such as during crises or periods of heightened volatility.

Prospective applicants interested in pursuing this project are invited to submit a detailed research proposal (maximum 2,000 words) that builds on this outline. Further details about the programme can be found at:

https://www.abdn.ac.uk/business/courses/pgr/

If you are interested in this project, please contact Dr Seungho Lee (seungho.lee@abdn.ac.uk), including a Curriculum Vitae and a cover letter outlining your motivation and research interests.

Essential background of student (first degree, necessary experience)

The successful applicant is expected to have (or be close to completing) an MSc in Finance or a related discipline, such as Economics, Accounting, or Data Science, with prior knowledge of predictive modelling. Applicants should demonstrate a strong interest in, or experience with, data preparation and project implementation. A solid understanding of relevant econometric models, particularly the Dynamic Conditional Correlation (DCC) GARCH framework (Engle, 2002) and the Regime-Switching DCC GARCH model (Pelletier, 2006), would be highly advantageous. The project also requires strong skills in statistical applications or programming (e.g., EViews, Stata, Python, or R). In addition, comprehensive knowledge of stock market regulations across different jurisdictions is desirable.

Funding Notes

This PhD project has no funding attached and is therefore available to students (U.K./International) who are able to seek their own funding or sponsorship. Supervisors will not be able to respond to requests to source funding.