Corporate Governance and Risk Management (GRC): theoretical frameworks and empirical analysis in firms

The integration of corporate governance with enterprise risk management (GRC) is one of the most active research agendas in finance and audit. This guide systematizes the theoretical frameworks, measurable variables and most-used methods so your thesis delivers original, publishable empirical evidence.

GRC as a graduate research problem

The acronym GRC (Governance, Risk and Compliance) denotes the integration of board strategic direction, risk management and regulatory compliance. For a master’s or doctoral thesis it is attractive for three reasons: it has stable normative frameworks (COSO ERM 2017 and ISO 31000:2018), offers public data on listed firms and connects with highly current ESG goals. The challenge is translating conceptual integration into observable variables and a defensible methodological design.

Reference theoretical and normative frameworks

A solid thesis combines three theoretical lenses with professional frameworks. Agency theory (Jensen and Meckling) justifies the control mechanisms that align interests between managers and shareholders, and remains the most-cited basis for studying board independence and risk committees. Stewardship theory and resource dependence qualify that view: the board not only controls, it also brings resources and expertise. Watkins Fassler et al. (2022), for instance, examine how board independence and ownership concentration affect profitability in Mexico and Chile through these lenses.

On the normative side, COSO ERM 2017 (“Integrating with Strategy and Performance”) reoriented risk management toward strategy, while ISO 31000:2018 provides generic principles applicable to any sector. Citing both legitimizes your model and lets you build a measurable adherence index.

Empirical variables and proxies for corporate governance

The methodological heart lies in operationalization. The most-used proxies are:

• Board size: number of directors.
• Independence: percentage of external or non-executive directors.
• Gender diversity: proportion of women on the board (Muhammad, Migliori and Mohsni, 2023, show its moderating role in risk-taking).
• Audit and risk committees: existence, size, meetings and independence (Toumeh, 2023).
• Ownership concentration and presence of institutional investors.

Build a data panel with these indicators (Bloomberg, Thomson Reuters Eikon, or local databases like BMV or BVL) for at least five fiscal years.

How to measure financial performance and risk

The dependent variable determines your study’s contribution. For performance, use accounting measures (ROA, ROE) and market measures (Tobin’s Q, stock returns). For risk, options include Altman’s Z-Score as a bankruptcy-risk proxy, return volatility, systematic beta and, where disclosures suffice, an ERM implementation index. Chairani and Siregar (2021) link ERM with financial performance and firm value, introducing ESG performance as a mediator; that “ERM → performance → value” pattern is an excellent starting point to replicate in a Latin American setting.

Recommended quantitative methods

Dominant techniques in the 2020-2026 literature are:

• Panel data with fixed/random effects to control unobserved heterogeneity.
• Difference GMM (Arellano-Bover/Blundell-Bond) when dynamics and likely endogeneity exist.
• PLS-SEM if your model integrates latent constructs (corporate-governance quality, risk culture).
• Instrumental-variable regressions to address reverse causality.

Always report robustness statistics (Hausman, Sargan/Hansen, AR2 autocorrelation test).

Trends 2020-2026 and common mistakes

Current agendas shift toward three fronts: climate risk and ESG (Naseer, 2024, shows climate-change risk hurts firm value and boosts ESG performance), cyber risk and post-pandemic resilience, and the digitalization of internal control. Including at least one differentiates your thesis from purely replicative studies.

Frequent mistakes that weaken the work: confusing correlation with causation without valid instruments; using accounting proxies without controlling for industry and size; ignoring the self-selection bias of firms that disclose risks; and citing frameworks without operationalizing them. Avoid them from the design stage.

References

• Chairani, C., & Siregar, S. V. (2021). The effect of enterprise risk management on financial performance and firm value: the role of environmental, social and governance performance. Meditari Accountancy Research, 29(3), 647-670. https://doi.org/10.1108/MEDAR-09-2019-0549
• Muhammad, H., Migliori, S., & Mohsni, S. (2023). Corporate governance and firm risk-taking: the moderating role of board gender diversity. Meditari Accountancy Research, 31(3), 706-728. https://doi.org/10.1108/MEDAR-07-2020-0949
• Naseer, M. M. (2024). Firm climate change risk and financial flexibility: Drivers of ESG performance and firm value. Finance Research Letters, 59, 104437. https://doi.org/10.1016/j.frl.2023.104437
• Toumeh, A. A. (2023). The Effect of Risk Management Committee Characteristics on Firm Performance: An Empirical Investigation. Indian Journal of Corporate Governance, 16(2), 323-342. https://doi.org/10.1177/09746862231213422
• Watkins Fassler, K., Briano-Torrent, N. del C., Franco-Ramírez, D. L., & Román-Sánchez, J. L. (2022). Independencia de los Consejos de Administración, concentración de la propiedad y rentabilidad de las empresas listadas en México y Chile. Revista Finanzas y Política Económica, 14(1), 245-269. https://doi.org/10.14718/revfinanzpolitecon.v14.n1.2022.10