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result(s) for
"Lleo, Sébastien"
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Jump-diffusion risk-sensitive benchmarked asset management with traditional and alternative data
This paper addresses errors in mean return estimates in continuous-time asset allocation models. A standard approach postulates that stochastic factors explain expected asset returns. The problem is then to estimate these factors from observed asset prices via filtering. Recent advances have also combined asset prices with expert opinions to improve the estimates. However, these methods have limitations: stocks prices favor momentum strategies, and expert opinions require careful debiasing. To resolve these issues, we propose a jump-diffusion risk-sensitive benchmarked asset management model in which investors estimate the factors from both traditional and alternative data. We show that this model admits a unique C1,2 solution, and we derive the optimal investment policy in quasi-closed form. We find that investors construct their portfolios from a passive core and an active satellite. The passive core adds considerations for jump risk to a simple benchmark replication. The active satellite blends security selection, and factor tilts with event-driven strategies unique to jump-diffusion problems. Thus, our model explains the most popular investment strategies. Furthermore, the improved expert forecast model and the introduction of alternative data provide factor tilters with new tools to sharpen their asset allocation.
Journal Article
Jump-Diffusion Risk-Sensitive Asset Management II: Jump-Diffusion Factor Model
In this article we extend our earlier work on the jump-diffusion risk-sensitive asset management problem in a factor model [SIAM J. Financial Math., 2 (2011), pp. 22--54] by allowing jumps in both the factor process and the asset prices, as well as stochastic volatility and investment constraints. In this case, the Hamilton--Jacobi--Bellman (HJB) equation is a partial integro-differential equation (PIDE). We are able to show that finding a viscosity solution to this PIDE is equivalent to finding a viscosity solution to a related PDE, for which classical results give uniqueness. With this in hand, a policy improvement argument and classical results on parabolic PDEs show that the HJB PIDE admits a unique smooth solution. The optimal investment strategy is given by the feedback control that minimizes the Hamiltonian function appearing in the HJB PIDE. [PUBLICATION ABSTRACT]
Journal Article
Predicting Stock Market Crashes in China
Predicting stock market crashes is extremely valuable for all investors. Several useful prediction models have been developed, focusing on mature financial markets, in North America, Europe, and Japan. The authors investigate whether traditional crash predictors--the price-to-earnings ratio (P/E), the cyclically adjusted price-to-earnings ratio (CAPE), and the bond-stock earnings yield differential model (BSEYD)--predict crashes for the Shanghai Stock Exchange Composite Index and the Shenzhen Stock Exchange Composite Index in mainland China. Using data from the early 1990s to the end of 2016, the authors find that the P/E ratio has predictive value for both exchanges over the entire period. When testing the P/E, CAPE, and BSEYD over a shorter nine-year period, the authors find that all measures had a higher predictive value for the Shenzhen index, where smaller, privately owned companies are listed, than for the Shanghai index, where larger, often state-owned enterprises trade.
Journal Article
Jump-Diffusion Risk-Sensitive Asset Management I: Diffusion Factor Model
This paper considers a portfolio optimization problem in which asset prices are represented by SDEs driven by Brownian motion and a Poisson random measure, with drifts that are functions of an auxiliary diffusion factor process. The criterion, following earlier work by Bielecki, Pliska, Nagai, and others, is risk-sensitive optimization (equivalent to maximizing the expected growth rate subject to a constraint on variance). By using a change of measure technique introduced by Kuroda and Nagai we show that the problem reduces to solving a certain stochastic control problem in the factor process, which has no jumps. The main result of this paper is to show that the risk-sensitive jump-diffusion problem can be fully characterized in terms of a parabolic Hamilton-Jacobi-Bellman PDE rather than a partial integro-differential equation, and that this PDE admits a classical $(C^{1,2})$ solution.
Journal Article
The Swiss black swan bad scenario: Is Switzerland another casualty of the Eurozone crisis?
Financial disasters to hedge funds, bank trading departments and individual speculative traders and investors seem to always occur because of non-diversification in all possible scenarios, being overbet and being hit by a bad scenario. Black swans are the worst type of bad scenario: unexpected and extreme. The Swiss National Bank decision on 15 January 2015 to abandon the 1.20 peg against the Euro was a tremendous blow for many Swiss exporters, but also Swiss and international investors, hedge funds, global macro funds, banks, as well as the Swiss central bank. In this paper, we discuss the causes for this action, the money losers and the few winners, what it means for Switzerland, Europe and the rest of the world, what kinds of trades were lost and how they have been prevented.
Journal Article
Jump-diffusion asset–liability management via risk-sensitive control
In this paper, we use risk-sensitive control methods to solve a jump-diffusion asset–liability management (ALM) problem. We show that the ALM problem admits a unique classical (
C
1
,
2
) solution under two different sets of assumptions.
Journal Article
A Simple Procedure for Combining Expert Opinion with Statistical Estimates to Achieve Superior Portfolio Performance
In this article, the authors describe a simple procedure for combining statistical estimates with expert opinions to produce a view of future asset performance. The authors discuss the impact of behavioral bias on these views and propose general modeling principles to reduce this bias. They use standard linear filtering techniques to combine statistical estimates with expert opinions seamlessly and discuss applications to dynamic portfolio optimization.
Journal Article