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Visual tracking technology enables industrial robots interacting with human beings intelligently. However, due to the complexity of the tracking problem, the accuracy of visual target tracking still has great space for improvement. This paper aims to propose an accurate visual target tracking method based on standard hedging and feature fusion.

For this study, the authors first learn the discriminative information between targets and similar objects in the histogram of oriented gradients by feature optimization method, and then use standard hedging algorithms to dynamically balance the weights between different feature optimization components. Moreover, they penalize the filter coefficients by incorporating spatial regularization coefficient and extend the Kernelized Correlation Filter for robust tracking. Finally, a model update mechanism to improve the effectiveness of the tracking is proposed.

Extensive experimental results demonstrate the superior performance of the proposed method comparing to the state-of-the-art tracking methods.

Improvements to existing visual target tracking algorithms are achieved through feature fusion and standard hedging algorithms to further improve the tracking accuracy of robots on targets in reality.

This research applies a static hedging portfolio method derived from Derman, Ergener, and Kani (1995) (henceforth Derman's SHP method) and a new SHP method with European cash-or-nothing binary options developed by Chung, Shih, and Tsai (2013) to price European continuous double barrier (ECDB) options and the rebates of the ECDB options. Our numerical results indicate that the new SHP method outperforms Derman's SHP method in terms of efficiency and effectiveness under all circumstances.

By using higher moments, this paper extends the quadratic local risk-minimizing approach in a general discrete incomplete financial market. The local optimization subproblems are convex or nonconvex, depending on the moment variants used in the modeling. Inspired by Lai et al. (2006), the authors propose a new multiobjective approach for the combination of moments that is transformed into a multigoal programming problem.

The authors evaluate financial derivatives with American features using local risk-minimizing strategies. The financial structure is in line with Schweizer (1988): the market is discrete, self-financing is not guaranteed, but deviations are controlled and reduced by minimizing the second moment. As for the quadratic approach, the algorithm proceeds backwardly.

In the context of evaluating American option, a transposition of this multigoal programming leads not only to nonconvex optimization subproblems but also to the undesirable fact that local zero deviations from self-financing are penalized. The analysis shows that issuers should consider some higher moments when evaluating contingent claims because they help reshape the distribution of global cumulative deviations from self-financing.

A detailed numerical analysis that compares all the moments or some combinations of them is performed.

The quadratic approach is extended by exploring other higher moments, positive combinations of moments and variants to enforce asymmetry. This study also investigates the impact of two types of exercise decisions and multiple assets.

The purpose of this paper is to examine the possibility of creating hedge funds “clones” using liquid exchange traded instruments.

Authors analyze the performance of fixed weight and extended Kalman filter generated clone portfolios (EKF) for 14 hedge fund strategies from February 2004 to September 2009. EKF approach does not indeed impose any normality constraints on the error terms which allow the filter to find the optimal recursive process by itself. Such models could adjust even faster to sudden shifts in market conditions vs a standard Kalman filter.

For five strategies out of 14, this work finds that EKF clones outperform their corresponding indices. Thus, for certain strategies, the possibility of cloning hedge fund returns is indeed real. Results should be however considered with caution.

This paper suggests that the most important benefits of clones are to serve as benchmarks and to help investors to better understand the various risk factors that impact hedge fund returns.

Rather than using fixed‐weight and rolling windows approaches (as Hasanhodzic and Lo), this work considers an extended version of the Kalman filter, a computational algorithm that better captures the time changing dynamics of hedge fund returns. Also, in order to be practical, this research considers investable factors and that the models themselves could not be constant over time.

The study aims to examine the relationship among economic policy uncertainty (EPU), geopolitical-risks (GPR), the interaction (EPGR) of EPU and GPR and the returns of gold, silver, platinum, palladium and rhodium using monthly data from January (1997) to May (2021).

The paper employs the Markov-switching and the novel Shi et al. (2020) bootstrap time-varying Granger-causality approach.

Though the Markov-switching shows variation in the responses of precious metals to EPU, GPR and EPGR across low and high states, the paper observes the safe-haven potential of the precious metals in the high regime while the hedging potency is also evident in the results. To further substantiate the safe-haven and hedging properties, the time-varying Granger-causality shows the causal effect of EPU on all the selected precious metal returns coinciding with global events. While the authors show that GPR Granger causes platinum, palladium and rhodium consistently under the rolling/recursive-evolving tests, the authors cannot find the causal effect of GPR on gold and silver returns across the algorithms. The paper also observes persistence in the causal effect of EPGR on palladium and platinum across all the algorithms, while gold and rhodium only show consistency in the responses under the rolling- and recursive-evolving algorithms given the conditions of homoscedasticity and heteroscedasticity.

The authors' results are essential to investors and policymakers since both typically leverage the hedging and safe-haven characteristics of precious metals to obviate downside risks during highly uncertain periods.

The authors' techniques allow examining the hedging and safe-haven properties of precious metals across regimes and date-stamp critical periods of causation inherent in the relationship.

In this paper, I propose an algorithm combining adaptive sampling and Reversible Jump MCMC to deal with the problem of variable selection in time-varying linear model. These types of model arise naturally in financial application as illustrated by a motivational example. The methodology proposed here, dubbed adaptive reversible jump variable selection, differs from typical approaches by avoiding estimation of the factors and the difficulties stemming from the presence of the documented single factor bias. Illustrated by several simulated examples, the algorithm is shown to select the appropriate variables among a large set of candidates.

The authors investigate whether firm-specific vulnerability to climate change influences foreign exchange hedging, using a novel text-based measure of firm-level climate change exposure generated by state-of-the-art machine-learning algorithms.

The authors' empirical analysis includes firm-fixed effects, random-effects regressions, propensity score matching (PSM), entropy balancing, an instrumental-variable analysis and using an exogenous shock as a quasi-natural experiment.

The authors' findings suggest that greater climate change exposure brings about a significant reduction in exchange rate hedging. Companies more exposed to climate change may invest significant resources to address climate change risk, such that they have fewer resources available for currency risk management. Additionally, firms seriously coping with climate change risk may view exchange rate risk as relatively less important in comparison to the risk posed by climate change. Notably, the authors also find that the negative effect of climate change exposure on currency hedging can be specifically attributed to the regulatory aspect of climate change risk rather than the physical dimension, suggesting that companies view the regulatory dimension of climate change as more critical.

Recent studies have demonstrated that climatic fluctuations represent one of the most recent sources of unpredictability, thereby impacting the economy and financial markets (Barnett et al., 2020; Bolton and Kacperczyk, 2020; Engle et al., 2020). The authors' study advances this field of research by revealing that company-specific exposure to climate change serves as a significant determinant of corporate currency hedging, thus expanding the existing knowledge base.

There is an increasing interest in the use of knowledge graphs to represent real-world knowledge and a common need to manage imprecise knowledge in many real-world applications. This paper aims to study approaches to solve flexible queries over knowledge graphs.

By introducing fuzzy logic in the query answering process, the authors are able to obtain a novel algorithm to solve flexible queries over knowledge graphs. This approach is implemented in the FUzzy Knowledge Graphs system, a software tool with an intuitive user-graphical interface.

This approach makes it possible to reuse semantic web standards (RDF, SPARQL and OWL 2) and builds a fuzzy layer on top of them. The application to a use case shows that the system can aggregate information in different ways by selecting different fusion operators and adapting to different user needs.

This approach is more general than similar previous works in the literature and provides a specific way to represent the flexible restrictions (using fuzzy OWL 2 datatypes).

Demonstrates the feasibility of, and introduces a practical approach to enhancing, reinsurance efficiency using index‐based instruments.

First reviews the general mathematical framework of reinsurance optimization. Next, illustrates how index‐based instruments can potentially enhance reinsurance efficiency through a simple yet self‐contained example. The simplicity allows the analytical examination of the cost and benefits of an index‐based contract. Finally, introduces a real‐world model that optimizes index‐based reinsurance instruments using the genetic algorithm.

Identifies the key factors that determine the efficiency of index‐based reinsurance contracts and demonstrates that, in the property catastrophe reinsurance market, the combined effect of these factors frequently allows the construction of an index‐based hedging program that is more efficient than a traditional excess‐of‐loss reinsurance contract. A robust optimization model based on the genetic algorithm is introduced and shown to be effective in optimizing index‐based reinsurance contracts.

Most financial optimization procedures are subject to parameter risk, which can adversely affect the robustness of their solutions. The reinsurance optimization approach presented in this paper is not completely immune from this problem. It remains a challenging problem for actuarial researchers and practitioners.

The concept and method proposed in this paper can be applied to designing real‐world reinsurance programs.

This paper makes two contributions to the risk finance literature: a systematic approach for evaluating the costs and benefits of index‐based reinsurance instruments, and an innovative and practical model for optimizing reinsurance efficiency.

The paper aims to propose a consistent and robust pricing/hedging methodology for callable fixed income structures with embedded caplet‐linked options.

A range of recently published (1997‐2003) works about the Libor Market Model (LMM) tackle the problems of modelling the forward curve with more than two factors and calibrating it to caps either/or to swaps. Other articles involve the pricing of Bermudan options using Monte Carlo simulation. In the form of case study, the very popular structure of multicallable range accrual bonds is used. A complete calibration methodology is described in detail, which links the structure's price to the market caps and swaptions prices as well as to the historical correlations between forward rates. We present the direct implementation of the Monte Carlo technique for this particular problem. Furthermore, we explore the application of the Longstaff–Schwartz least squares algorithm and its variations for the estimation of the expected value of continuation.

This paper suceeds in producing a consistent and robust pricing/hedging methodology for callable fixed income structures with embedded caplet‐linked options.

The increased complexity of similar fixed income structures makes traditional approaches like Black–Derman–Toy or Hull‐White trees inadequate for the task of consistent pricing and hedging. Therefore, care must be taken to ensure consisted hedging across the different volatility markets.

This article explores variations and settings of the popular LMM and the Longstaff‐Scwartz algorithm that can be relatively consistent with both the cap and swaption volatility market. The framework is built using as a benchmark the most liquid fixed income structure so that it can be tested for robustness.