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on Forecasting |
| By: | Adebola K. Ojo; Ifechukwude Jude Okafor |
| Abstract: | Investors and stock market analysts face major challenges in predicting stock returns and making wise investment decisions. The predictability of equity stock returns can boost investor confidence, but it remains a difficult task. To address this issue, a study was conducted using a Long Short-term Memory (LSTM) model to predict future stock market movements. The study used a historical dataset from the Nigerian Stock Exchange (NSE), which was cleaned and normalized to design the LSTM model. The model was evaluated using performance metrics and compared with other deep learning models like Artificial and Convolutional Neural Networks (CNN). The experimental results showed that the LSTM model can predict future stock market prices and returns with over 90% accuracy when trained with a reliable dataset. The study concludes that LSTM models can be useful in predicting financial time-series-related problems if well-trained. Future studies should explore combining LSTM models with other deep learning techniques like CNN to create hybrid models that mitigate the risks associated with relying on a single model for future equity stock predictions. |
| Date: | 2025–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2507.01964 |
| By: | Hyung Joo Kim; Dong Hwan Oh |
| Abstract: | We propose a novel estimation framework for option pricing models that incorporates local, state-dependent information to improve out-of-sample forecasting performance. Rather than modifying the underlying option pricing model, such as the Heston-Nandi GARCH or the Heston stochastic volatility framework, we introduce a local M-estimation approach that conditions on key state variables including VIX, realized volatility, and time. Our method reweights historical observations based on their relevance to current market conditions, using kernel functions with bandwidths selected via a validation procedure. This adaptive estimation improves the model’s responsiveness to evolving dynamics while maintaining tractability. Empirically, we show that local estimators substantially outperform traditional non-local approaches in forecasting near-term option implied volatilities. The improvements are particularly pronounced in low-volatility environments and across the cross-section of options. The local estimators also outperform the non-local estimators in explaining future option returns. Our findings suggest that local information, when properly incorporated into the estimation process, can enhance the accuracy and robustness of option pricing models. |
| Keywords: | Local maximum likelihood; Implied volatility forecasting; Option pricing; Model misspecification |
| JEL: | C14 C51 C53 C58 G13 |
| Date: | 2025–08–27 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-76 |
| By: | Arash Peik; Mohammad Ali Zare Chahooki; Amin Milani Fard; Mehdi Agha Sarram |
| Abstract: | Precise short-term price prediction in the highly volatile cryptocurrency market is critical for informed trading strategies. Although Temporal Fusion Transformers (TFTs) have shown potential, their direct use often struggles in the face of the market's non-stationary nature and extreme volatility. This paper introduces an adaptive TFT modeling approach leveraging dynamic subseries lengths and pattern-based categorization to enhance short-term forecasting. We propose a novel segmentation method where subseries end at relative maxima, identified when the price increase from the preceding minimum surpasses a threshold, thus capturing significant upward movements, which act as key markers for the end of a growth phase, while potentially filtering the noise. Crucially, the fixed-length pattern ending each subseries determines the category assigned to the subsequent variable-length subseries, grouping typical market responses that follow similar preceding conditions. A distinct TFT model trained for each category is specialized in predicting the evolution of these subsequent subseries based on their initial steps after the preceding peak. Experimental results on ETH-USDT 10-minute data over a two-month test period demonstrate that our adaptive approach significantly outperforms baseline fixed-length TFT and LSTM models in prediction accuracy and simulated trading profitability. Our combination of adaptive segmentation and pattern-conditioned forecasting enables more robust and responsive cryptocurrency price prediction. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.10542 |
| By: | Hao Wang; Jingshu Peng; Yanyan Shen; Xujia Li; Lei Chen |
| Abstract: | Stock recommendation is critical in Fintech applications, which use price series and alternative information to estimate future stock performance. Although deep learning models are prevalent in stock recommendation systems, traditional time-series forecasting training often fails to capture stock trends and rankings simultaneously, which are essential consideration factors for investors. To tackle this issue, we introduce a Multi-Task Learning (MTL) framework for stock recommendation, \textbf{M}omentum-\textbf{i}ntegrated \textbf{M}ulti-task \textbf{Stoc}k \textbf{R}ecommendation with Converge-based Optimization (\textbf{MiM-StocR}). To improve the model's ability to capture short-term trends, we novelly invoke a momentum line indicator in model training. To prioritize top-performing stocks and optimize investment allocation, we propose a list-wise ranking loss function called Adaptive-k ApproxNDCG. Moreover, due to the volatility and uncertainty of the stock market, existing MTL frameworks face overfitting issues when applied to stock time series. To mitigate this issue, we introduce the Converge-based Quad-Balancing (CQB) method. We conducted extensive experiments on three stock benchmarks: SEE50, CSI 100, and CSI 300. MiM-StocR outperforms state-of-the-art MTL baselines across both ranking and profitable evaluations. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.10461 |
| By: | Ekaterina V. Peneva; Jeremy B. Rudd; Daniel Villar Vallenas |
| Abstract: | This paper examines the Board staff's inflation forecast misses over the years following the COVID-19 outbreak, focusing on a timeline of what staff members knew when and lessons learned along the way. The staff significantly underestimated both the size and persistence of the inflationary surge that followed the reopening of the U.S. economy. As a result, staff members made various changes to their forecasting procedures, including using new types of data to inform their assessment of supply-demand imbalances in product and labor markets and to guide their judgmental forecast. Throughout, an important difficulty was the lack of similar historical episodes upon which to base a quantitative analysis. Over time, the innovations helped improve the staff's ability to understand and forecast inflation during this period. However, considerable uncertainty remains about the quantitative contributions of the various drivers of the pandemic-period inflation as well as the applicability of the lessons from this episode for forecasting. |
| Keywords: | Inflation forecasting; Inflation dynamics; Phillips curve; covid-19 pandemic |
| JEL: | E31 E37 |
| Date: | 2025–08–22 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-69 |
| By: | Nicolas Apfel; Holger Breinlich; Nick Green; Dennis Novy; J. M. C. Santos Silva; Tom Zylkin |
| Abstract: | Gravity equations are often used to evaluate counterfactual trade policy scenarios, such as the effect of regional trade agreements on trade flows. In this paper, we argue that the suitability of gravity equations for this purpose crucially depends on their out-of-sample predictive power. We propose a methodology that compares different versions of the gravity equation, both among themselves and with machine learning-based forecast methods such as random forests and neural networks. We find that the 3-way gravity model is difficult to beat in terms of out-of-sample average predictive performance, further justifying its place as the predominant tool for applied trade policy analysis. However, when the goal is to predict individual bilateral trade flows, the 3-way model can be outperformed by an ensemble machine learning method. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.11271 |
| By: | Vahab Rostampour |
| Abstract: | Estimating lifetime probabilities of default (PDs) under IFRS~9 and CECL requires projecting point--in--time transition matrices over multiple years. A persistent weakness is that macroeconomic forecast errors compound across horizons, producing unstable and volatile PD term structures. This paper reformulates the problem in a state--space framework and shows that a direct Kalman filter leaves non--vanishing variability. We then introduce an anchored observation model, which incorporates a neutral long--run economic state into the filter. The resulting error dynamics exhibit asymptotic stochastic stability, ensuring convergence in probability of the lifetime PD term structure. Simulation on a synthetic corporate portfolio confirms that anchoring reduces forecast noise and delivers smoother, more interpretable projections. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.10586 |
| By: | Jirong Zhuang; Xuan Wu |
| Abstract: | Constructing the implied volatility surface (IVS) is reframed as a meta-learning problem training across trading days to learn a general process that reconstructs a full IVS from few quotes, eliminating daily recalibration. We introduce the Volatility Neural Process, an attention-based model that uses a two-stage training: pre-training on SABR-generated surfaces to encode a financial prior, followed by fine-tuning on market data. On S&P 500 options (2006-2023; out-of-sample 2019-2023), our model outperforms SABR, SSVI, Gaussian Process, and an ablation trained only on real data. Relative to the ablation, the SABR-induced prior reduces RMSE by about 40% and dominates in mid- and long-maturity regions where quotes are sparse. The learned prior suppresses large errors, providing a practical, data-efficient route to stable IVS construction with a single deployable model. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.11928 |
| By: | Todd Prono |
| Abstract: | In heavy-tailed cases, variance targeting the Student's-t estimator proposed in Bollerslev (1987) for the linear GARCH model is shown to be robust to density misspecification, just like the popular Quasi-Maximum Likelihood Estimator (QMLE). The resulting Variance-Targeted, Non-Gaussian, Quasi-Maximum Likelihood Estimator (VTNGQMLE) is shown to possess a stable limit, albeit one that is highly non-Gaussian, with an ill-defined variance. The rate of convergence to this non-standard limit is slow relative √n and dependent upon unknown parameters. Fortunately, the sub-sample bootstrap is applicable, given a carefully constructed normalization. Surprisingly, both Monte Carlo experiments and empirical applications reveal VTNGQMLE to sizably outperform QMLE and other performance-enhancing (relative to QMLE) alternatives. In an empirical application, VTNGQMLE is applied to VIX (option-implied volatility of the S&P 500 Index). The resulting GARCH variance estimates are then used to forecast option-implied volatility of volatility (VVIX), thus demonstrating a link between historical volatility of VIX and risk-neutral volatility-of-volatility. |
| Keywords: | GARCH; VIX; VVIX; Heavy tails; Robust estimation; Variance forecasting; Volatility; Volatility-of-volatility |
| JEL: | C13 C22 C58 |
| Date: | 2025–08–27 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedgfe:2025-75 |
| By: | Yi Lu; Aifan Ling; Chaoqun Wang; Yaxin Xu |
| Abstract: | In recent years, China's bond market has seen a surge in defaults amid regulatory reforms and macroeconomic volatility. Traditional machine learning models struggle to capture financial data's irregularity and temporal dependencies, while most deep learning models lack interpretability-critical for financial decision-making. To tackle these issues, we propose EMDLOT (Explainable Multimodal Deep Learning for Time-series), a novel framework for multi-class bond default prediction. EMDLOT integrates numerical time-series (financial/macroeconomic indicators) and unstructured textual data (bond prospectuses), uses Time-Aware LSTM to handle irregular sequences, and adopts soft clustering and multi-level attention to boost interpretability. Experiments on 1994 Chinese firms (2015-2024) show EMDLOT outperforms traditional (e.g., XGBoost) and deep learning (e.g., LSTM) benchmarks in recall, F1-score, and mAP, especially in identifying default/extended firms. Ablation studies validate each component's value, and attention analyses reveal economically intuitive default drivers. This work provides a practical tool and a trustworthy framework for transparent financial risk modeling. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.10802 |
| By: | Francois-Michel Boire; Thibaut Duprey; Alexander Ueberfeldt |
| Abstract: | This paper studies how financial shocks shape the distribution of output growth by introducing a quantile-augmented vector autoregression (QAVAR), which integrates quantile regressions into a structural VAR framework. The QAVAR preserves standard shock identification while delivering flexible, nonparametric forecasts of conditional moments and tail risk measures for gross domestic product (GDP). Applying the model to financial conditions and credit spread shocks, we find that adverse financial shocks worsen the downside risk to GDP growth significantly, while the median and upper percentiles respond more moderately. This underscores the importance of nonlinearities and heterogeneous tail dynamics in assessing macro-financial risks. |
| Keywords: | Central bank research; Econometric and statistical methods; Financial markets; Financial stability; Monetary and financial indicators |
| JEL: | C32 C53 E32 E44 G01 |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:bca:bocawp:25-25 |
| By: | Angelini, Elena; Bokan, Nikola; Ciccarelli, Matteo; Lalik, Magdalena; Zimic, Srečko |
| Abstract: | This paper introduces the European Central Bank’s Multi Country model (ECB-MC), a coherent macroeconomic framework designed to support economic forecasting and policy analysis within the Eurosystem. The ECB-MC captures the economic dynamics of the five major economies in the euro area – Germany, France, Italy, Spain, and the Netherlands – which account for more than 80 percent of the euro area total GDP. By incorporating detailed structural features and data-driven insights, the model provides the main reference for the ECB’s staff macroeconomic projections, acting as a disciplined tool for forecasting, enabling scenario, risk and sensitivity analyses, and giving a framework to understand the transmission channels of various economic shocks. The paper offers a detailed account of the structure, the estimation and the model properties, and provides a primer on the potential uses of the ECB-MC in the Eurosystem macroeconomic projections. JEL Classification: C3, C5, E5, E6 |
| Keywords: | euro area countries, forecasting, monetary policy, semi-structural model |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20253119 |
| By: | Kuok Sin Un; Marcel Ausloos |
| Abstract: | Through a novel approach, this paper shows that substantial change in stock market behavior has a statistically and economically significant impact on equity risk premium predictability both on in-sample and out-of-sample cases. In line with Auer's ''Bullish ratio'', a ''Bullish index'' is introduced to measure the changes in stock market behavior, which we describe through a ''fluctuation detrending moving average analysis'' (FDMAA) for returns. We consider 28 indicators. We find that a ''positive shock'' of the Bullish Index is closely related to strong equity risk premium predictability for forecasts based on macroeconomic variables for up to six months. In contrast, a ''negative shock'' is associated with strong equity risk premium predictability with adequate forecasts for up to nine months when based on technical indicators. |
| Date: | 2025–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.10483 |
| By: | Stéphane Lhuissier |
| Abstract: | I propose a dynamic factor model with time-varying skewness to assess asymmetric risk around the economic outlook across a set of macroeconomic aggregates. Applied to U.S. data, the model shows that macroeconomic skewness is procyclical, displays significant independent variations from GDP growth skewness, and does not require conditioning on financial variables to manifest. Compared to univariate benchmarks, the model improves the detection of downside risk to growth and delivers more accurate predictive distributions, especially during downturns. These findings underscore the value of using a richer information set to quantify the balance of macroeconomic risks. |
| Keywords: | Dynamic Factor Models, Markov-Switching, Skewness |
| JEL: | C34 C38 C53 E37 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:bfr:banfra:1004 |