Risks

This study examines the dynamic interactions among gross domestic product (GDP), external public debt, and domestic public debt in Iraq and discusses their implications for fiscal sustainability. The observed annual data cover the period 2005–2024, while the values for 2025 are based on IMF projections. Because continuous official monthly observations were unavailable, the annual series were temporally disaggregated using cubic-spline interpolation to construct an analytical monthly series. These interpolated values do not represent additional independent observations; therefore, the empirical findings are interpreted as exploratory. A recursive Structural Vector Autoregression (SVAR) model was estimated using Cholesky identification with the ordering GDP, external debt, and domestic debt. The variables were transformed into second differences in their natural logarithms, and a three-lag specification was employed. Impulse-response functions and forecast-error variance decomposition were used to examine the transmission and relative importance of the identified innovations. At the 24-month forecast horizon, GDP shocks explained 93.3% of GDP variation, while external debt was predominantly explained by its own shocks (86.3%). Domestic-debt variation was explained by its own shocks (47.3%), GDP shocks (44.1%), and external-debt shocks (8.6%). These results suggest that domestic debt is more closely associated with changes in domestic economic activity, whereas external debt follows a comparatively persistent path. The findings emphasize the importance of debt composition, non-oil revenue diversification, expenditure management, and coordination between domestic and external borrowing. Because oil revenues and government expenditure are not included as separate endogenous variables, the model does not directly identify oil-revenue or government-spending shocks. Future research should employ genuinely observed quarterly or monthly data and incorporate these fiscal variables explicitly.

Non-life insurance pricing is forward-looking, yet its principal cost signal, reported claims, is delayed by the occurrence-to-reporting process. The rating cell is treated as a stylised, homogeneous unit without renewal, lapse, expiry, or cohort dynamics; a fully annual-contract formulation would require these dynamics to be modelled jointly with the risk and claims processes. This paper develops a partially observed risk-sensitive control framework in which premium-sensitive exposure generates claims in a latent risk regime, while unreported claims form an atomic population governed by an age-structured transport equation. The numerical instance solved and validated in this research restricts the general model to a memoryless (one-phase) reporting process for tractability. It is best matched to lines with predominantly short-to-medium reporting tails, rather than to the most extreme long-tailed liability or cyber exposures the general model is designed to eventually accommodate. A finite-state reporting reservoir and nominal Bayesian filter provide the decision state, and compound Poisson–Gamma loss enters an entropic Bellman recursion through a closed-form exponential-tilting identity, checked against an independently coded Bellman-residual test. Every dynamic policy is benchmarked against alternatives matched at the same risk-sensitivity parameter and evaluated using common random numbers. This is a general feature of the results, not a single statistic: $C{E}_{0.8}$ is a standardised yardstick applied uniformly across strategies, while $C{E}_{\gamma }$ at the policy’s own $\gamma$ is what that policy actually optimises, and the two need not agree. In 3000 out-of-model paths at $\gamma$ = 1.2, the delay-aware dynamic policy increases mean profit by EUR 0.148 million and a standardised ${\gamma }_{\mathrm{eval}}$ = 0.8 certainty equivalent by EUR 0.034 million relative to a matched static price. Its fifth percentile and TVaR 5%, by contrast, are lower by EUR 0.056 and 0.078 million. At the policy’s own optimisation level, however, the ${\gamma }_{\mathrm{eval}}$ = 1.2 certainty-equivalent difference is EUR −0.006 million, with a 95% interval reaching zero. The dynamic policy, therefore, does not clearly outperform the matched static price on the exact objective it was optimised to maximise. Matched comparisons attribute EUR 0.039–0.043 million of mean profit to reporting-delay modelling and EUR 0.017 million to dynamic continuation. Separating state observation from transition-law knowledge attributes EUR 0.161–0.182 million to observing the regime exactly, and a small, sign-changing EUR −0.010 to +0.003 million to knowing the true transition law itself. Across an eight-scenario misspecification stress suite, the dynamic policy’s mean-profit advantage over the matched static price is directionally robust in seven of eight scenarios, but it reverses sign under a +30% true-severity shock, indicating that this advantage is sensitive to substantial severity misspecification specifically. Grid, Bellman-residual, and out-of-model filter diagnostics indicate that state reconstruction is economically primary, while entropy risk sensitivity is a secondary overlay whose apparent benefit depends materially on which certainty-equivalent level and evaluation model are used to judge it.

Exchange rate forecasting is a core issue in empirical finance, but in the majority of studies, the impact of target construction, feature representation, and model structure is not disaggregated, and the evaluation is confined to one prediction problem. The three tasks are: (1) prediction of $E{R}_{t+1}$; (2) prediction of ; and (3) prediction of future volatility defined as the sample standard deviation of , where the horizon is the next 10 recorded observations. The seven model families are evaluated with a strict chronological 70%/15%/15% split; $ER$ is excluded as a direct raw predictor, deep models use seeds 7, 42, and 123, and task-specific financial benchmarks are included. The findings reveal markedly different out-of-sample forecasting behavior across the three tasks. For level prediction, the no-change random walk gives RMSE = 0.0005923 and $R^2=0.99815$, while Linear Regression gives RMSE = 0.0005939 and $R^2=0.99814$; the difference is not significant (DM = 0.480, $p=0.631$). For return prediction, Linear Regression gives RMSE = 0.0005920 and $R^2=-0.00061$ and does not significantly outperform the zero-return benchmark (DM = $-0.083$, $p=0.934$). For future volatility, the best average finance-aware learned model is FeatureAttention_Only (RMSE = $0.0004407\pm 0.0000180$; ), and its three-seed ensemble gives RMSE = 0.0004298 and $R^2=0.024$. FeatureAttention_Only improves squared-error loss relative to EWMA and GARCH in the fixed hold-out, but is not significantly superior to GARCH under QLIKE; rankings also vary across forecast origins.