Vol. 9 No. 1 (2026)

A forecasting contest cannot identify the best tail-risk model if the specification favoured by the descriptive-fit comparison is never allowed to compete. This study examines a selection–evaluation gap and shows that including the omitted specification changes the preferred model family in the present application. Using 2,383 synchronised daily returns on the DSEX, DS30, and DSES indices of the Dhaka Stock Exchange from 3 January 2016 to 19 July 2026, this study runs a single, fully specified out-of-sample competition among GARCH-t, GJR-t, GJR-EVT, EGARCH-t, and EGARCH-EVT across 744 one-day-ahead forecasts during 2023–2026. Every competitor — econometric and algorithmic alike — is estimated on an expanding window, refitted on the same four annual dates, held fixed within each forecast year, and evaluated using information available only through t-1. EGARCH-t achieves the “best” descriptive BIC for all three indices, and most importantly, in all 24 matched comparisons, the EGARCH family also achieves lower values of the pinball loss than the GJR alternatives. In the nine comparisons where Diebold–Mariano tests reject equal predictive accuracy, they never favour GJR. The ranking is not purchased through undercoverage since all five econometric specifications pass the unconditional-coverage and independence tests and EGARCH-EVT achieves one per cent VaR which is 2.44 to 6.43 basis points less conservative in calm states and 8.88 to 14.33 basis points less conservative in turbulent states with identical numbers of violations in each cell of the index and state matrix. Nevertheless, a histogram gradient-boosting quantile learner is fitted with fixed settings and lagged market features and refitted on the same real-time schedule and fails unconditional coverage on both of the tail levels for each index showing a calibration failure for this specification. If the contest was restricted to GARCH and GJR then the wrong family was crowned in every cell. Because the empirical results are limited to one exchange, with three indices that have overlapping constituents and share the same institutional environment, the design principle that they derive is general.

Bhutan’s financial system has advanced into the digital space, driven by Digital Drukyul, a significant national information and communication technology (ICT) transformation flagship programme, fostering the use of more ICT to make Bhutan a more digitally connected, smart, and inclusive society. However, such a transformation comes at a cost, and it puts real strain on the integrity of financial reporting, from how assets are valued to whether traditional accounting standards are maintained. This review argues that cyber risk should not be regarded as just an IT-related issue reported in the system. Instead, it builds a framework that integrates digital security breaches directly into the vulnerability of financial statements in an emerging economy of the country. In line with Bhutan’s own regulatory and international accounting standards, the study examines seven core accounting areas: (a) how assets and liabilities are valued; (b) how losses are measured; (c) how well internal mechanism controls hold up; (d) how insurance coverage is recognised; (e) whether disclosures are adequate; (f) ensuring regulatory compliance; and (g) how well going concern is assessed. By linking Normative Accounting Theories, Agency, and Stakeholder, the analysis indicates that disconnected ledger systems and relationship-based banking create risk that can shirk cyber-related operational costs and paint a distorted picture of a bank’s finances. This study offers a phased, prioritised roadmap for bank leaders, policymakers, and auditors, addressing the gap between digital ambition and fiscal stability as well as aligning stronger resilience with a current digital transformation approach.