Probabilistic analysis reveals substantial losses for reverse convertible note holders.
problem Substantial losses to reverse convertible note holders due to complex pricing.
method Probabilistic analysis using Law of Total Expectation.
result Note-holders likely suffered substantial losses under various market scenarios.
Chebyshev technique reduces FRTB-IMA equity autocallables computation costs by 90%.
problem Efficient computation of FRTB-IMA capital for equity autocallables.
method Orthogonal Chebyshev Sliding Technique applied to equity autocallables.
result Computational cost reduction of about 90% for equity autocallables.
Paper presents a machine learning-based method for efficiently pricing and hedging autocallable structured notes with multiple underlying assets.
problem Complex pricing and hedging of autocallable notes with multiple underlying assets.
method Machine learning-based pricing method and Distributional Reinforcement Learning (RL) for hedging.
result Significantly improved efficiency in pricing and hedging, with faster computation and better risk management.
Quantum algorithm speeds up pricing of financial derivatives.
problem Pricing autocallable options efficiently.
method Integration-based exponential amplitude loading technique.
result 50x reduction in circuit depth for payoff component.
A new method reduces Monte Carlo variance for financial payoffs.
problem Reducing variance in Monte Carlo estimators for financial payoffs.
method Path-dependent importance sampling using neural networks.
result Significant variance reduction (2-9 times) for various financial payoffs.
Optimizes a portfolio for an investor preferring accepted securities over a reference security.
problem Investor preference for a set of securities over a reference security with constraints.
method Mean-variance optimization with Sharpe Ratio performance measurement.
result Derives an optimal portfolio that maximizes returns while minimizing risk.
This paper optimizes a dynamic portfolio using novel dynamic programming.
problem Maximizing a portfolio's value over time with changing prices.
method Novel theoretical approach based on dynamic programming for both deterministic and stochastic cases.
result Theoretical approach successfully maximizes portfolio value using dynamic programming.
Paper proposes a recursive PLS model for optimal response to security threats.
problem Optimal response to security threats after violations have occurred.
method Recursive Partial Least Squares (PLS) model with factorial analysis of security events.
result The model optimally estimates security administrators' responses to threats.
Optimizes crypto-oriented neural architectures for faster secure inference.
problem Privacy conflicts between model users and providers in neural network applications.
method Proposes a novel Partial Activation layer to optimize the initial design of crypto-oriented neural architectures.
result Significant improvement in the efficiency of secure inference on common evaluation metrics.
Contextual bandit framework improves revenue optimization in securities lending market.
problem Optimizing revenue for agent lenders in a dynamic securities lending market.
method Utilized contextual bandit frameworks to address dynamic pricing problems in an e-commerce-like securities lending market.
result Contextual bandit approach consistently outperforms traditional methods by at least 15% in total revenue generated.
We prove dual attainment for multi-asset financial derivatives pricing.
problem Model-independent pricing and hedging of complex financial derivatives.
method Established duality and attained optimizers for multimarginal, multi-asset martingale optimal transport.
result Existence of dual optimizers under mild conditions for arbitrary numbers of assets and time periods.
The emph{securities market} is the fundamental theoretical framework in economics and finance for resource allocation under uncertainty. Securities serve both to reallocate risk and to disseminate probabilistic information. emph{Complete} securities markets - which contain one security for every possible state of natur…
A fast method for pricing various financial options.
problem Efficient pricing of discretely monitored early-exercise options.
method A quadrature technique-based method using elementary calculations and a fixed grid.
result Convergence rate of O(1/N4) and complexity of O(MNlogN). The paper analyzes security issues in blockchain ecosystems with multiple SSPs and proposes two models for better stake management.
problem Security issues in blockchain ecosystems with multiple SSPs and stake fragmentation.
method Formalized the Multiple SSP Problem and analyzed two architectures: Model M and Model S through convex optimization and game-theoretic lens. result Model S achieves tighter security guarantees through single validator sets and aggregated slashing logic. We discuss a class of risk-sensitive portfolio optimization problems. We consider the portfolio optimization model investigated by Nagai in 2003. The model by its nature can include fixed income securities as well in the portfolio. Under fairly general conditions, we prove the existence of optimal portfolio in both fin…
HASP enhances mobile security against ASR by adding imperceptible noise.
problem Security leakage from ASR on mobile devices.
method Adversarial noise to perturb WER and enhance security.
result HASP reduces WER by 84.55% and speeds up data processing.
Secure and efficient distributed learning on devices with limited communication.
problem Limited communication and security in distributed on-device learning.
method Proposes SLSGD, a robust distributed optimization algorithm with efficient communication and attack tolerance.
result Stabilizes convergence and tolerates data poisoning on a small number of workers.
Study optimal portfolio selection in a complex market with jumps and regime shifts.
problem Optimal portfolio selection in a market with jumps and regime shifts.
method Modeling a market with Lévy processes and regime switching, using various securities to complete the market, solving the portfolio selection problem for power and logarithmic utilities.
result Conditions for asymptotic-arbitrage-free market and solutions for optimal portfolio selection.
Optimal securities lending mechanism incentivizes truthfulness and privacy.
problem Maximizing resource usage in securities lending while ensuring truthful reporting and privacy.
method Bayesian optimal algorithm adapted for differential privacy, combined with market equilibrium dynamics.
result An algorithm that is simultaneously private, approximately optimal, and approximately dominant-strategy truthful.
Secure blockchain architectures protect data privacy in distributed learning.
problem Data privacy and trust in distributed learning across organizations.
method Adequate encryption and blockchain mechanisms ensure data privacy and trust in iterative learning.
result Secure sharing of a learned model among coalition members without revealing data.
A model for choosing crypto assets based on security and stability.
problem Optimal selection of crypto assets considering security and stability.
method A recommender app-like system that presents pairs of crypto assets and collects investor preferences.
result A variety of possible outcomes for crypto asset investments and adoption.
Study on risk sharing in capital requirements for diverse security markets.
problem Risk sharing for capital adequacy tests in heterogeneous security markets.
method Analyzes conditions for a representative agent, studies polyhedral and distribution-based constraints, proves existence of optimal allocations and equilibria.
result Existence of optimal risk allocations and equilibria under different capital adequacy constraints.
The paper uses a simulator and optimisation to defend against cyber threats.
problem Defending against cyber threats in simulated networks.
method Dynamic causal Bayesian optimisation (DCBO) integrated with a cyber security simulator.
result DCBO optimally reduces the cost of intrusions in simulated networks.
A quantum financial approach to finite games of strategy is addressed, with an extension of Nash's theorem to the quantum financial setting, allowing for an entanglement of games of strategy with two-period financial allocation problems that are expressed in terms of: the consumption plans' optimization problem in pure…
Safeguarding privacy in machine learning is highly desirable, especially in collaborative studies across many organizations. Privacy-preserving distributed machine learning (based on cryptography) is popular to solve the problem. However, existing cryptographic protocols still incur excess computational overhead. Here,…
In this paper, we generalize the Almgren-Chriss's market impact model to a more realistic and flexible framework and employ it to derive and analyze some aspects of optimal liquidation problem in a security market. We illustrate how a trader's liquidation strategy alters when multiple venues and extra information are b…
Paper tackles optimization challenges in deep neural nets, presenting Newton-based methods.
problem Optimization challenges in solving deep neural net models for classification problems.
method Newton-based method incorporating negative curvature directions.
result Promising numerical results on security anomaly detection data.
Paper proposes a new method to secure power system operation using machine learning.
problem Ensuring secure power system operation under high uncertainty.
method Embedding disjunctive rules from Decision Trees in an optimization framework using GDP and a two-step search method.
result The method achieves efficient system control at a marginal increase in system price compared to an oracle model.
This paper deals with an optimal position management problem for a market maker who has to face uncertain customer order flows in an illiquid market, where the market maker's continuous trading incurs a stochastic linear price impact. Although the execution timing is uncertain, the market maker can also ask its OTC cou…
This study examines how banks and securities markets coevolved in 19th century Belgium.
problem The role of banks and markets in the evolution of financial architecture.
method Case study of Belgium in the 1830s, focusing on the development of secondary securities markets and banks' activities.
result Cyclical market conditions influenced banks' activities and vice versa, suggesting non-neutral coevolution.
Study proposes hybrid machine learning models for crop yield prediction.
problem Crop yield prediction for food security.
method Hybrid machine learning models (ANN-ICA and ANN-GWO).
result ANN-GWO model outperformed ANN-ICA in crop yield prediction.
SANNS secures k-NNS queries and results without revealing them.
problem Securely searching for nearest neighbors without exposing sensitive data.
method Optimized linear scan and clustering-based algorithm with cryptographic primitives.
result SANNS achieves up to 31x faster response times and scales to 10 million entries.
We study the Heston-Cox-Ingersoll-Ross++ stochastic-local volatility model in the context of foreign exchange markets and propose a Monte Carlo simulation scheme which combines the full truncation Euler scheme for the stochastic volatility component and the stochastic domestic and foreign short interest rates with the …
Model for open, decentralized network with task load balancing.
problem Complex computational tasks in open, decentralized networks.
method Incentive-based load balancing using economic mechanisms.
result Optimized resource allocation and enhanced system resilience.
We consider a class of generalized capital asset pricing models in continuous time with a finite number of agents and tradable securities. The securities may not be sufficient to span all sources of uncertainty. If the agents have exponential utility functions and the individual endowments are spanned by the securities…
Paper analyzes InstaHide's security, recovering all private images with provable guarantee.
problem Protecting privacy of training data in neural networks.
method Unified framework to understand and analyze attacks on InstaHide, presenting a new algorithm to recover all private images with provable guarantee.
result InstaHide is computationally secure but not information-theoretically secure when mixing two private images.
This paper optimizes SMPC for neural network inference, reducing memory and time.
problem Memory and time constraints in secure neural network inference.
method Implemented ABY2.0 protocol, optimized memory usage, and used a helper node.
result MNIST inference reduced from 8.03 GB RAM and 200s to 0.2 GB RAM and 32s.
This paper optimizes portfolio rebalancing under uncertain security returns using meta-heuristic algorithms.
problem Optimizing portfolio rebalancing under uncertain security returns with transaction costs.
method Meta-heuristic algorithms (genetic algorithm) for solving the portfolio rebalancing problem.
result Meta-heuristic algorithms provide better results than global optimization solvers for portfolio rebalancing under uncertainty.
Study optimal investment strategy for pension schemes to hedge longevity risk.
problem Hedging longevity risk in defined contribution pension schemes.
method Transformed optimal investment problem into an unconstrained problem using dynamic programming and numerical studies.
result Longevity risk significantly impacts investment strategies, supporting the use of mortality-linked securities.
This paper optimizes cybersecurity resource allocation in networks with heterogeneous attacker and defender valuations.
problem Optimizing cybersecurity resource allocation in networks with heterogeneous attacker and defender valuations.
method Combining strategic behavior of players with contagion dynamics, a method is extended to determine optimal resource allocation based on simple network metrics weighted by risk profiles.
result The asymmetry between attacker and defender valuations drives optimal attack and defense strategies, shaping system resilience.
Study shows Bitcoin security tied to mining rewards and prices.
problem Understanding Bitcoin security's dependency on market outcomes.
method Used ARDL approach with daily blockchain and Bitcoin data from 2014-2019.
result Bitcoin security outcomes linked to Bitcoin price and mining rewards.
The problem of secure friend discovery on a social network has long been proposed and studied. The requirement is that a pair of nodes can make befriending decisions with minimum information exposed to the other party. In this paper, we propose to use community detection to tackle the problem of secure friend discovery…
This research highlights the secrecy potential of nonlinear generative models and their all-or-nothing phase transition.
problem Secrecy potential of nonlinear generative models in statistical learning.
method Replica method to derive asymptotic normalized cross entropy and statistical decoupling of Bayesian estimator.
result Strictly nonlinear models exhibit an all-or-nothing phase transition, leading to perfect secrecy.
In this paper, we employ the Heston stochastic volatility model to describe the stock's volatility and apply the model to derive and analyze the optimal trading strategies for dealers in a security market. We also extend our study to option market making for options written on stocks in the presence of stochastic volat…
A pair of points in a riemannian manifold makes a secure configuration if the totality of geodesics connecting them can be blocked by a finite set. The manifold is secure if every configuration is secure. We investigate the security of compact, locally symmetric spaces.
Study reveals how correlation matrix eigenvalues change with time scale in U.S. stocks.
problem Understanding how correlation structure of securities changes with time scale.
method Aggregated one-minute returns of 533 U.S. stocks at different time scales, estimated correlation matrix, lead-lag factor model.
result Emergence of several dominant eigenvalues as time scale increases.
Paper tackles robustness in adversarial noise with a meta-optimizer.
problem Sensitivity to adversarial noise hinders machine learning deployment.
method Meta-optimizer learns to robustly optimize models using adversarial examples.
result Meta-optimizer transfers adversarial knowledge to new models without generating new examples.
A portfolio of different stocks and a risk-less security whose composition is dynamically maintained stable by trading shares at any time step leads to a growth of the capital with a nonrandom rate. This is the key for the theory of optimal-growth investment formulated by Kelly. In presence of transaction costs, the op…