Portfolio optimisation
Capitolis to acquire LMRKTS
Deal for multilateral compression provider latest in wave of post-trade tie-ups, as SA-CCR bites
The case for reinforcement learning in quant finance
The technology behind Google’s AlphaGo has been strangely overlooked by quants
Goal-based wealth management with reinforcement learning
A combination of machine learning techniques provides multi-period portfolio optimisation
Turning challenges into solutions
With margin requirements a potential drain on financial resources, delivering healthy returns while meeting regulatory obligations is paramount. To help participants optimise more of their risk, Varqa Abyaneh, chief product officer, Quantile, discusses…
Margin matters – A smarter approach to margin risk
Michael Hollingsworth, head of financial risk analytics in the Data and Access Solutions division at Cboe Global Markets, reveals how trading firms are calculating margin in real time to manage pre- and post-trade risk and end-of-day clearing-house…
Powering through tough times
Hitachi ABB Power Grids’ dominant position in Energy Risk’s 2021 Software Rankings reflects its deep understanding of current market challenges
Simm template to be expanded for SA-CCR and FRTB
Crif-plus will capture risk exposures for all instruments, boosting optimisation potential
Optimisation firms prep for SA-CCR boom
Flush with new cash, vendors ready rebalancing services ahead of risk-sensitive leverage framework
Optimization of systemic risk: reallocation of assets based on bank networks
In this paper, the authors investigate the optimization of systemic risk based on DebtRank by considering two contagion channels: interbank lending and common asset holdings.
Mixed response to Esma’s clearing carve-out for optimisation
Long-awaited proposal must be replicated by US and UK to be effective, participants say
Smaller drawdowns, higher average and risk-adjusted returns for equity portfolios, using options and power-log optimization based on a behavioral model of investor preferences
The authors use a power-log utility optimization algorithm based on a behavioral model of investor preferences, along with either a call or a put option overlay, to reverse the negative skewness of monthly Standard & Poor’s 500 (S&P 500) index returns…
Fulcrum hangs ESG designs on honing hard numbers
ESG risks will become part of investment and risk management processes across all funds at the firm
Quant funds look to AI to master correlations
Machine learning shows promise in grouping assets better, predicting regime shifts
Stock-picking finds unlikely champion in ex-Winton CIO
Matthew Beddall’s Havelock restyles value investing for the big data age
The standard market risk model of the Swiss solvency test: an analytic solution
This paper derives an alternative fast Fourier transform-based computational approach for calculating the target capital of the SST that is more than 600 times faster than a Monte Carlo simulation.
Factor investing: get your exposures right!
This paper is devoted to the question of optimal portfolio construction for equity factor investing. The authors discuss the question of multifactor portfolio construction and show that the simplistic approaches often used by practitioners tend to be…
Risk premia strategies – Lessons learned for the future
After a difficult 2018, investors are increasingly wary of risk premia, concerned that factors leading to underperformance might be a recurring problem. Imene Moussa, executive director at UBS, clarifies this issue
From log-optimal portfolio theory to risk measures: logarithmic expected shortfall
In this paper, the authors propose a modification of expected shortfall that does not treat all losses equally. We do this in order to represent the worries surrounding big drops that are typical of multiperiod investors.
Time to put real problems to the quantum machines
There is a lot to learn before quantum computers can be applied to specific financial problems
Skewed target range strategy for multiperiod portfolio optimization using a two-stage least squares Monte Carlo method
In this paper, the authors propose a novel investment strategy for portfolio optimization problems.
Portfolio optimization for American options
In this paper, the authors construct strategies for an American option portfolio by exercising options at optimal timings with optimal weights determined concurrently.
Risk Markets Technology Awards 2019: Vendors enter the pick-and-mix era
Modular tech and micro-services – plus new risk and regulatory needs – are creating openings for insurgents and incumbents
Value-ranked equity portfolios via entropy pooling
This paper demonstrates how to directly incorporate common value-investing idea into the portfolio optimization process.
Genetic algorithm-based portfolio optimization with higher moments in global stock markets
This paper investigates the distributional characteristics of stock market returns and analyzes the significance of higher moments.