This study analyzes the impacts of exchange rate and inflation on exports in Turkiye. Annual data for the period 1995-2023 were used in the analysis. The Johansen cointegration analysis and Dynamic Least Squares (DOLS) method were employed in the study. Identifying the cointegration relationship enabled the estimation of the long-run coefficients. The results show that an increase in the real effective exchange rate (appreciation of the Turkish lira) and inflation reduce exports with coefficients of -0.185 and -0.125, respectively. Foreign direct investment and imports, added to the study as control variables, have a positive impact on exports with coefficients of 0.117 and 0.849, respectively. These findings indicate that exchange rate stability and inflation control are priorities for improving foreign trade performance. Furthermore, policies that increase foreign direct investment and strategically manage imports complement this process.

We develop a Quantile Bayesian Vector Autoregression (QBVAR) to forecast real oil prices across different quantiles of the conditional distribution. The model allows predictor effects to vary across quantiles, capturing asymmetries that standard mean-focused approaches miss. Using monthly data from 1975 to 2025, we document three findings. First, the QBVAR improves median forecasts by 2-5\% relative to Bayesian VARs, demonstrating that quantile-specific dynamics matter even for point prediction. Second, uncertainty and financial condition variables strongly predict downside risk, with left-tail forecast improvements of 10-25\% that intensify during crisis episodes. Third, right-tail forecasting remains difficult; stochastic volatility models dominate for upside risk, though forecast combinations that include the QBVAR recover these losses. The results show that modeling the conditional distribution yields substantial gains for tail risk assessment, particularly during major oil market disruptions.

This study analyses the potential of renewable energy to reduce inflationary pressures arising from energy imports in Turkiye. Annual data for the period 1980-2022 are used in the analysis. In this study, unit root properties are examined using the Zivot-Andrews and Lee-Strazicich tests, both of which explicitly account for structural breaks. Cointegration is investigated via the Johansen and Hatemi-J cointegration tests. Long-run coefficients are subsequently estimated using the DOLS and FMOLS estimators. The robustness of the empirical findings is further assessed using the ARDL approach. In addition, an interaction term is constructed to measure the impact of renewable energy in alleviating inflationary pressures arising from energy imports. The results show that energy imports and exchange rate have an increasing impact on inflation, while renewable energy and the interaction term have a decreasing impact. DOLS, FMOLS, and ARDL results support each other. Moreover, in both models, the impact of renewable energy in mitigating inflationary pressures stemming from energy imports is stronger than the direct disinflationary impact of renewable energy.

We model stochastic choice as environment-dependent switching among a small library of deterministic decision rules. A Random Rule Model generates menu-level choice probabilities via named, interpretable rules weighted by observable menu characteristics. Identification has a two-step structure: within-feature decisive-side variation identifies relative rule weights; cross-feature richness identifies the gate. Applied to binary lottery choices, the estimated weights concentrate on a small subset of rules and shift systematically with complexity and dispersion asymmetry. The model closes nearly all of the prediction gap to a flexible neural-network benchmark, while remaining interpretable, restrictive under permutation diagnostics, and portable to an independent dataset.

We develop a framework for stochastic portfolio theory (SPT), which incorporates modern nonlinear price impact and impact decay models. Our main result is the derivation of the celebrated master formula for additive functional generation of trading strategies in a general high-dimensional market model with price impact. We also derive formulas for an investor's relative wealth with respect to the market portfolio, conditions that guarantee positive observed market prices and a stochastic differential equation governing the dynamics of the observed price, the investor's holdings and the price impact state processes. As an application of these results, we develop conditions for relative arbitrage in the price impact setting analogous to previously obtained results for the frictionless setting. We then apply our framework to backtest the quadratic and entropy generating functions on historical US equity data, illustrating how price impact can negatively affect portfolio performance.

The Nelson-Siegel model is widely used in fixed income markets to produce yield curve dynamics. The multiple time-dependent parameter model conveniently addresses the level, slope, and curvature dynamics of the yield curves. In this study, we present a novel state-space functional regression model that incorporates a dynamic Nelson-Siegel model and functional regression formulations applied to multi-economy setting. This framework offers distinct advantages in explaining the relative spreads in yields between a reference economy and a response economy. To address the inherent challenges of model calibration, a kernel principal component analysis is employed to transform the representation of functional regression into a finite-dimensional, tractable estimation problem. A comprehensive empirical analysis is conducted to assess the efficacy of the functional regression approach, including an in-sample performance comparison with the dynamic Nelson-Siegel model. We conducted the stress testing analysis of yield curves term-structure within a dual economy framework. The bond ladder portfolio was examined through a case study focused on spread modelling using historical data for US Treasury and UK bonds.

Factor models characterize the joint behavior of large sets of financial assets through a smaller number of underlying drivers. We develop a network-based framework in which factors emerge naturally from the structure of interactions among assets rather than being imposed statistically. The market is modeled as a system of coupled iterated maps, where assets' return depends on its own past returns and those of related assets. Effectively modeling the influence of irrational traders whose decisions are based on the past movements of a collection of stocks. The interaction structure between stock returns is defined by a coupling matrix derived from an orthogonal transformation of a Laplacian matrix that gradually links initially isolated clusters into a fully connected network. Within this structure, stable patterns of co-movement arise and can be interpreted as financial factors. The relationship between the initial clustering and the number of observed factors is consistent with a center manifold reduction. We identify an optimal regime in which assets' variance is effectively explained by the set of factors produced by the network. Our framework offers a structural perspective based on interaction-based factor formation and dimension reduction in financial markets.

This paper bridges social security design and general equilibrium theory to conceive optimally balanced pay-as-you-go systems. The design is based on the backward calculation algorithm from Dognini (2025), which is used to find optimal monetary equilibria of prone-to-savings non-stationary overlapping generations economies with heterogeneous households. In particular, this algorithm makes the design applicable for reforming pay-as-you-go systems in countries undergoing demographic transitions. Due to households balanced budgets under equilibrium prices (i.e., Walras' law), these optimally balanced pay-as-you-go systems resemble the well-known notional accounts systems. The design is illustrated in a simplified framework using the past and forecast demographic and productivity dynamics of Brazil, China, India, Italy, and the United States from 1950 to 2070.

We propose a decomposition method for solving a general class of linear-quadratic (LQ) McKean-Vlasov control problems involving conditional expectations and random coefficients, where the system dynamics are driven by two independent Wiener processes. Unlike existing approaches in the literature for these problems, such as the extended stochastic maximum principle and the extended dynamic programming methods, which often involve additional technical complexities and sometimes impose restrictive conditions on control inputs, our approach decomposes the original McKean-Vlasov control problem into two decoupled stochastic optimal control problems, one of which has a constrained admissible control set. These auxiliary problems can be solved using classical methods. We establish an equivalence between the well-posedness and solvability of the auxiliary problems and those of the original problem, and show that the sum of the optimal controls of the auxiliary problems yields the optimal control of the original problem. Moreover, by applying a variational method, we characterize the optimal solution to the McKean-Vlasov control problem via two decoupled sets of (non-McKean-Vlasov) linear forward-backward stochastic differential equations, each corresponding to one of the auxiliary problems. Finally, we show that standard dynamic programming can also be applied to solve the resulting auxiliary problems.

What drives cross-state differences in U.S. energy consumption? We combine LMDI decomposition, stochastic frontier analysis, and variable-importance methods on a panel of 50 states plus DC over the 2006--2022 period. The observed 12.8% decline in per capita energy use is driven almost entirely by intensity improvements. A variance decomposition attributes 63% of cross-state variation in log energy use to the demand frontier, 34\% to inefficiency above it, and 3% to noise. Within the frontier, energy prices account for roughly 26% of cross-state variation and state efficiency policies for about 13%, while GDP and climate together explain only around 10\%. Efficiency policies also operate through a second channel by reducing inefficiency, adding a further 6 percentage points to their total contribution. The results suggest that pricing and regulation are the primary drivers of cross-state energy use differences.

As global political tensions rise and the anticipation of additional tariffs from the United States on international trade increases, the issues of economic independence and supply chain resilience become more prominent. The importance of supply chain resilience has been further underscored by disruptions caused by the COVID-19 pandemic and the ongoing war in Ukraine. In light of these challenges, ranging from geopolitical instability to product supply uncertainties, governments are increasingly focused on adopting new trade policies. This study explores the impact of several of these policies on the global electric vehicle (EV) supply chain network, with a particular focus on their effects on country clusters and the broader structure of international trade. Specifically, we analyse three key policies: Country Plus One, Friendshoring, and Reshoring. Our findings show that Friendshoring, contrary to expectations, leads to greater globalisation by increasing the number of supply links across friendly countries, potentially raising transaction costs. The Country Plus One policy similarly enhances network density through redundant links, while the Reshoring policy creates challenges in the EV sector due to the high number of irreplaceable products. Additionally, the effects of these policies vary across industries; for instance, mining goods being less affected in Country Plus One than the Friendshoring policy.

The global financial architecture is undergoing a shift from intermediary centric-settlement to programmable infrastructure, to transmute trillions in static illiquid capital into active, high-velocity instruments. We argue that Real World Asset (RWA) tokenization represents a conceptual evolution beyond mere digitization, converting passive ledger entries into programmable economic agents capable of autonomous settlement and algorithmic collateralization. However, achieving such seamless capital efficiency necessitates resolving the fundamental friction between deterministic on-chain code and probabilistic off-chain reality, navigating the oracle problem and jurisdictional interoperability. This systematization of knowledge presents a taxonomy for the RWA lifecycle and deconstructs the multi-layered architecture, spanning legal custody, technical standards, and cryptoeconomic valuation, required to enforce off-chain rights within on-chain environments. We study systemic constraints such as latency and regulatory fragmentation through a comparative overview of sovereign debt, private credit, and real estate protocols, complemented by an empirical case study of on-chain U.S. Treasuries. We synthesize these findings to propose a prognostic outlook, positing that while asset tokenization provides a transitional bridge, it is not necessarily the inevitable shift compared to the emergence of unified, programmable ledgers.

This paper studies cross-market return predictability through a machine learning framework that preserves economic structure. Exploiting the non-overlapping trading hours of the U.S. and Chinese equity markets, we construct a directed bipartite graph that captures time-ordered predictive linkages between stocks across markets. Edges are selected via rolling-window hypothesis testing, and the resulting graph serves as a sparse, economically interpretable feature-selection layer for downstream machine learning models. We apply a range of regularized and ensemble methods to forecast open-to-close returns using lagged foreign-market information. Our results reveal a pronounced directional asymmetry: U.S. previous-close-to-close returns contain substantial predictive information for Chinese intraday returns, whereas the reverse effect is limited. This informational asymmetry translates into economically meaningful performance differences and highlights how structured machine learning frameworks can uncover cross-market dependencies while maintaining interpretability.

We prove that a two-cycle equilibrium in a general equilibrium model with infinitely-lived agents (GEILA) constitutes an equilibrium in an overlapping generations (OLG) model. Conversely, an equilibrium in an OLG model that satisfies additional conditions is part of an equilibrium in a GEILA model. Our framework, which includes three assets (physical capital, a Lucas tree, and fiat money), encompasses both exchange and production economies. As an application, we demonstrate that equilibrium indeterminacy and rational asset price bubbles can arise not only in OLG models but also in GEILA models.

The growth of Low Earth Orbit (LEO) broadband satellite megaconstellations is rapidly increasing the number of rocket launches. While improving broadband Internet helps achieve the Sustainable Development Goals (SDGs), there are also significant environmental emissions produced from burning rocket fuels. We present sustainability analytics for phase 1 of the three main LEO constellations including Amazon Kuiper (3,236 satellites), Eutelsat Group's OneWeb (648 satellites), and SpaceX Starlink (4,425 satellites). We find that LEO megaconstellations provide substantially improved broadband speeds for rural and remote communities but are roughly 6-8 times more emissions intensive (250 kg CO2eq/subscriber/year) than comparative terrestrial 4G mobile broadband. Policy makers must carefully consider the trade-off between improving broadband Internet to further the SDGs while mitigating the growing space sector environmental footprint, particularly regarding phase 2 plans to launch an order-of-magnitude more satellites.

We examine whether news can improve realised volatility forecasting using a modern yet operationally simple NLP framework. News text is transformed into embedding-based representations, and forecasts are evaluated both as a standalone, news-only model and as a complement to standard realised volatility benchmarks. In out-of-sample tests on a cross-section of stocks, news contains useful predictive information, with stronger effects for stock-related content and during high volatility days. Combining the news-based signal with a leading benchmark yields consistent improvements in statistical performance and economically meaningful gains, while explainability analysis highlights the news themes most relevant for volatility.

High order discretization schemes of SDEs by using free Lie algebra valued random variables are introduced by Kusuoka, Lyons-Victoir, Ninomiya-Victoir and Ninomiya-Ninomiya. These schemes are called KLNV methods. They involve solving the flows of vector fields associated with SDEs and it is usually done by numerical methods. The authors found a special Lie algebraic structure on the vector fields in the major financial diffusion models. Using this structure, we can solve the flows associated with vector fields analytically and efficiently. Numerical examples show that our method saves the computation time drastically.