Financial Optimization: optimization paradigms and financial planning under uncertainty

Consigli, Giorgio; Brandimarte, Paolo; Kühn, Daniel

doi:10.1007/s00291-015-0406-y

Cited by 3 publications

(2 citation statements)

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“…We refer to Consigli et al (2015) and references therein for a summary on the steps from the stochastic program formulation to its solution and the associated output analysis. In this project we rely on a Matlab 7.4 and GAMS 23.2 interface for scenario generation, alegbraic model formulation and MPS file generation for a large scale deterministic equivalent problem Birge and Louveaux (2007) (spiegare referenza).…”

Section: Case-study: Goal-based Investing and Stochastic Dominancementioning

confidence: 99%

Long-term individual financial planning under stochastic dominance constraints

2019

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We analyse an optimal households' asset-liability management (ALM) problem formulated as a goal-based problem in which the family aims over an extended planning horizon at achieving an investment goal, in the form of a real-estate investment and a retirement goal at the end of the planning horizon. The problem is formulated as a multistage stochastic program (MSP) and we evaluate in this article the impact of second order stochastic dominance (SSD) constraints on different specifications of the family objective functions and with respect to three alternative benchmark policies adopted to generate the SSD constraint set. The problem is formulated as a linear stochastic program and, following Kopa et al. ( 2018) the SSD constraints are based on a simple permuation matrix, whose effectiveness in determining the decision maker strategies is confirmed in a case study developed in the second part of the article. We show that depending on the adopted benchmark policy, SSD feasibility even if far away on the planning horizon may influence root node decisions and affect both the adopted investment and the liability optimal policies. Interestingly, SSD feasibility, depending benchmark policy may also imply first-oder stochastic dominance (FSD). Finally we analyse in the article from a qualitative viewpoint the relationship between a minimum shortfall

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Section: Case-study: Goal-based Investing and Stochastic Dominancementioning

confidence: 99%

Long-term individual financial planning under stochastic dominance constraints

2019

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“…Thus, the model can be interpreted in terms of different layers of protection to respond to risky market conditions. The approach is inspired by asset management models [41,31,13] carrying several decision criteria and a stochastic tracking component which is tested over recent periods in the US equity market. The article carries three relevant contributions: (i) the formulation of an optimal risk control problem based on a set of commonly used derivative-based payoff functions; (ii) the benchmarking of the resulting optimal strategies against derivatives-based market indices.…”

Section: Introductionmentioning

confidence: 99%

Volatility versus downside risk: performance protection in dynamic portfolio strategies

2018

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Volatility-based and volatility targeting approaches have become popular among equity fund managers after the introduction in 1993 of the VIX, the implied volatility index on the S&P500 at the Chicago Board of Exchange (CBOE), followed, in 2004, by futures and option contracts on the VIX: since then we have assisted to an increasing interest in risk control strategies based on market signals. In January 2000 also the FTSE implied volatility index (FTSEIVI) was introduced at the London Stock Exchange. As a result, specifically in the US, portfolio strategies based on combinations of market indices and derivatives have been proposed by Stock Exchanges and investment banks: one such example is the S&P500 protective put index (PPUT). Early in 2016, relevant to the definition of optimal bond-equity strategies, CBOE launched an Index called TYVIX/VIX featuring an investment rotation strategy based jointly on signals coming from the VIX and the 10-year Treasury Yield implied volatility (TYVIX). All these are rule-based portfolio strategies in which no optimization methods are involved. While rather effective in reducing the downside risk, those index-based portfolio approaches do not allow an optimal risk-reward trade-off and may not be sufficient to control financial risk originated by extreme market drops. To overcome these limits we propose an optimization-based approach to portfolio management jointly focusing on volatility and tail risk controls and able to accommodate effectively the return payoffs associated with option strategies, whose cost as market volatility increases may become excessive. The model is based on a mean absolute deviation formulation and tested in the US equity market over the 2000-2016 period and with a focus on three periods of high volatility, in 2000, 2001 and 2008. The results confirm that optimal volatility controls produce better risk-adjusted returns if compared with rule-based approaches. Moreover the portfolio return distribution is dynamically shaped depending on the adopted risk management approach.

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