arXiv subjects

Tomasz Dubiel-Teleszynski

Publications and source records attributed to Tomasz Dubiel-Teleszynski.

3 recordsLinked to original sources

arXiv2026

Dynamic Inference in Term Structure Models with Unspanned Latent Risks

We propose a parsimonious class of arbitrage-free, yields-only dynamic term structure models (DTSMs) with unspanned latent risks. To enable sequential estimation and forecasting, we develop a Sequential Monte Carlo framework that combines particle learning for static parameters with Kalman filter updates for latent states, yielding joint posterior inference and predictive distributions that account for both parameter and state uncertainty. We use this framework to assess the out-of-sample statistical and economic value of bond return predictability from the perspective of a Bayesian investor. Empirically, we find that unspanned latent factors contain predictive information beyond that embedded in the yield curve, improving out-of-sample forecasting performance relative to standard benchmark models. These gains translate into economically meaningful utility improvements across a range of portfolio settings. Finally, we show that the hidden component of the slope-related risk factor is countercyclical and associated with real economic activity, suggesting that the latent factors capture economically relevant variation not directly reflected in yields.

stat.AP↗

arXiv2023

Dynamic Term Structure Models with Nonlinearities using Gaussian Processes

The importance of unspanned macroeconomic variables for Dynamic Term Structure Models has been intensively discussed in the literature. To our best knowledge the earlier studies considered only linear interactions between the economy and the real-world dynamics of interest rates in DTSMs. We propose a generalized modelling setup for Gaussian DTSMs which allows for unspanned nonlinear associations between the two and we exploit it in forecasting. Specifically, we construct a custom sequential Monte Carlo estimation and forecasting scheme where we introduce Gaussian Process priors to model nonlinearities. Sequential scheme we propose can also be used with dynamic portfolio optimization to assess the potential of generated economic value to investors. The methodology is presented using US Treasury data and selected macroeconomic indices. Namely, we look at core inflation and real economic activity. We contrast the results obtained from the nonlinear model with those stemming from an application of a linear model. Unlike for real economic activity, in case of core inflation we find that, compared to linear models, application of nonlinear models leads to statistically significant gains in economic value across considered maturities.

stat.AP↗

arXiv2022

Sequential Learning and Economic Benefits from Dynamic Term Structure Models

We explore the statistical and economic importance of restrictions on the dynamics of risk compensation from the perspective of a real-time Bayesian learner who predicts bond excess returns using dynamic term structure models (DTSMs). The question on whether potential statistical predictability offered by such models can generate economically significant portfolio benefits out-of-sample, is revisited while imposing restrictions on their risk premia parameters. To address this question, we propose a methodological framework that successfully handles sequential model search and parameter estimation over the restriction space in real time, allowing investors to revise their beliefs when new information arrives, thus informing their asset allocation and maximising their expected utility. Empirical results reinforce the argument of sparsity in the market price of risk specification since we find strong evidence of out-of-sample predictability only for those models that allow for level risk to be priced and, additionally, only one or two of these risk premia parameters to be different than zero. Most importantly, such statistical evidence is turned into economically significant utility gains, across prediction horizons, different time periods and portfolio specifications. In addition to identifying successful DTSMs, the sequential version of the stochastic search variable selection (SSVS) scheme developed can be applied on its own and also offer useful diagnostics monitoring key quantities over time. Connections with predictive regressions are also provided.

stat.AP↗