Technical paper/Portfolio
A dynamic method-of-moments copula model approach for market risk estimates
The authors propose a method-of-moments copula technique for estimating asset portfolios' market risk, demonstrating a significant reduction in copula estimation time.
Formulations to select assets for constructing sparse index tracking portfolios
The authors put forward methods to chose assets for sparse index tracking portfolios and demonstrate the tracking performance with numerical examples.
Bridging the gap risk reloaded: modelling wrong-way risk and leverage
A model extends the counterparty risk calculation to include nonlinear and complex portfolios
Weighting for leverage
A credit exposure model for leveraged collateralised counterparties is presented
The impact of greenhouse gas aversion on optimal portfolios
The author applies greenhouse gas aversion to the mean-variance portfolio framework and proposes a new portfolio performance measure for greenhouse-gas-averse investors.
Leveraged wrong-way risk
A model to assess the exposure to leveraged and collateralised counterparties is presented
Integrated stock–bond portfolio management
The authors put forward a stock-bond portfolio selection model which is based on CreditMetrics principles in which market and credit risks are naturally integrated.
Pricing the transition of Scope 3 emissions
A framework to measure banks’ costs associated with carbon emissions is proposed
Sherman ratio optimization: constructing alternative ultrashort sovereign bond portfolios
This paper explores the Sherman ratio and find that it has merit in the optimization of portfolio construction.
A model for small basket equities financing
A haircut model for equity baskets based on credit and equity indexes is introduced
Momentum transformer: an interpretable deep learning trading model
An attention-based deep learning model for trading is presented
Allocating and forecasting changes in risk
This paper considers time-dependent portfolios and discuss the allocation of changes in the risk of a portfolio to changes in the portfolio’s components.
Pricing options using expected profit and loss measures
The authors investigate the pricing of options using an EP-EL approach, finding that this methodology generates large amounts of useful information for option traders.
Exploring the equity–bond relationship in a low-rate environment with unsupervised learning
The authors apply k-means clustering to low interest rate periods in order to analyze the equity hedging property of government bonds.
Future portfolio returns and the VIX term structure
The authors use a measure that captures the expected evolution of risk and generate results supportive of the concept that there are multiple facets within volatility risk that are priced individually.
Detecting prudence and temperance in risk exposure: the hybrid variance framework
This paper analyses the correlations between returns and HVs in the short and long terms while developing a risk measure designed to contain the impacts of prudence and temperance on risk aversion.
Are there multiple independent risk anomalies in the cross section of stock returns?
Using multivariate portfolio sorts, firm-level cross-sectional regressions and spanning tests, this paper shows that, in the cross section of stock returns, most commonly used risk measures in academia and in practice are separate return predictors with…
Nonlinear risk decomposition for any type of fund
A risk decomposition by fund manager, factor or instrument is proposed
Deep learning profit and loss
The P&L distribution of a complex derivatives portfolio is computed via deep learning
Correlated idiosyncratic volatility shocks
To capture the commonality in idiosyncratic volatility, the authors propose a novel multivariate generalized autoregressive conditional heteroscedasticity (GARCH) model called dynamic factor correlation (DFC).
Quant investing in cluster portfolios
This paper discusses portfolio construction for investing in N given assets, eg, constituents of the Dow Jones Industrial Average (DJIA) or large cap stocks, based on partitioning the investment universe into clusters.