Preface: application of operations research to financial markets

Can commodities dominate stock and bond portfolios? Abstract In this article we discuss whether commodities should be included as an asset class when establishing portfolios. By investigating second order stochastic dominance relations, we find that the stock and bond indices tend to dominate the individual commodities. We further study if we can find a combination of stocks, bonds and commodities that dominate others. Compared to a 60% stock and 40% bond portfolio mix, portfolios consisting of long positions in gold futures and two different actively managed indices are the only commodity investments to be included as long positions in a stock/bond portfolio. The results should be of interest for fund managers and traders that seek to improve their risk-return trade off compared to the traditional 60/40 portfolio.

Managing portfolio diversity within the mean variance theory Abstract It is well documented that the classical mean variance theory (MVT) may yield portfolios (MVTP) that are highly concentrated and/or are outperformed by equal weight portfolios (EWP). In this work, it is proposed to expand the MVT minimizing objective function with an additional term that explicitly controls portfolio diversity (diversity booster DB). DB decreases with growing number of non-zero portfolio weights and has a minimum when all weights are equal. As a result, high values of DB yield EWP. For performance analysis, portfolio constructed with 12 major US equity ETFs is considered. Out-of-sample performance of maximum Sharpe portfolios is tested using statistics of bootstrapped Sharpe ratios for monthly rebalancing periods. It is found that for the 3-year calibrating window, the diversified MVT portfolio (DMVTP) outperformed both MVTP and EWP in 2012–2015. While the MVTP weights were highly concentrated and had sharp jumps between rebalancing periods, the DMVTP weights slowly changed with time.

Forecasting government bond spreads with heuristic models: evidence from the Eurozone periphery Abstract This study investigates the predictability of European long-term government bond spreads through the application of heuristic and metaheuristic support vector regression (SVR) hybrid structures. Genetic, krill herd and sine–cosine algorithms are applied to the parameterization process of the SVR and locally weighted SVR (LSVR) methods. The inputs of the SVR models are selected from a large pool of linear and non-linear individual predictors. The statistical performance of the main models is evaluated against a random walk, an Autoregressive Moving Average, the best individual prediction model and the traditional SVR and LSVR structures. All models are applied to forecast daily and weekly government bond spreads of Greece, Ireland, Italy, Portugal and Spain over the sample period 2000–2017. The results show that the sine–cosine LSVR is outperforming its counterparts in terms of statistical accuracy, while metaheuristic approaches seem to benefit the parameterization process more than the heuristic ones.

Did long-memory of liquidity signal the European sovereign debt crisis? Abstract This paper analyses high frequency MTS data to comprehensively evaluate the liquidity of the European sovereign bond markets before and during the European sovereign debt crisis for eleven countries. The Hill index, Generalized Hurst exponent and Dynamic Conditional Score are employed to evaluate the properties of the bid-ask spread. Sovereign bonds exhibit the stylized facts reported for a range of financial markets. The 1-min interval analysis indicates the level of bid-ask spread exhibits long-memory and the change in bid-ask spread experiences volatility clustering. In a dynamic setting, the volatility of bid-ask spread also exhibits long-memory in most European sovereign bond markets across all three maturities. Long-memory effects diminish (disappear) for 5-min (15-min) interval, and for short-term maturity (peripheral countries) is stronger than long-term maturity (core countries). Analysis of sub-periods indicates that long-memory process reached its peak during European sovereign debt crisis from May 2010 to December 2011. This analysis suggests that estimating long-memory parameters for high-frequency data could be a useful tool to monitor market stability.

Is stock liquidity transferred and upgraded in acquisitions? Evidence from liquidity synergies in US freeze-outs Abstract This paper investigates the value successful bidders generate from acquiring less liquid targets. This synergy is traced with both theoretical and empirical evidence from the squeeze-out stage of going private transactions, when bidders hold sizeable toeholds in target shares. By transferring their superior liquidity, acquirers can add value to the valuation of their toeholds in fully acquired target assets. We use a sample of US delisted targets from globally listed acquirers over 25 years, and, in line with our theoretical analysis, a nonlinear relation is evidenced between the expected added value from liquidity transfer and illiquidity differences. The adjustment of target market prices for the attached option to participate in the bid in a new stochastic volatility framework reveals that the bulk of deal-generated wealth depends on the offered option. Although the market penalizes the mean acquirer with negative abnormal returns, those with higher liquidity differences from their targets are suffering less because of their greater potential of liquidity transfer synergy. The analysis of the probability that the offered option gets in the money reveals that liquidity transfer acts as a catalyst for successfully concluding the deal and underlies underbidding.

On the calibration of the Schwartz two-factor model to WTI crude oil options and the extended Kalman Filter Abstract The Schwartz (J Finance 52(3):923–973, 1997) two factor model serves as a benchmark for pricing commodity contracts, futures and options. It is normally calibrated to fit the term-structure of a range of future contracts with varying maturities. In this paper, we investigate the effects on parameter estimates, if the model is fitted to prices of options, with varying maturities and strikes instead of futures, as is commonly done. The use of option prices rather than futures in the calibration leads to non-linearities, which the standard Kalman filter approach is unable to cope with. To overcome these issues, we use the extended Kalman Filter. We find that some parameters sensitively depend on the choice of strikes of the corresponding options, and are different from those estimates obtained from using futures prices. This effect is analogue to varying implied volatilities in the Black–Scholes model. This realization is important, as the use of ill-fitted models for pricing options in the Schwartz (1997) framework may cause traders to bear serious financial losses.

Quantization meets Fourier: a new technology for pricing options Abstract In this paper we introduce a novel pricing methodology for a broad class of models for which the characteristic function of the log-asset price can be efficiently computed. The method is based on a new quantization procedure, crucially exploiting for the first time the Fourier transform of the asset process, which fully characterizes the distribution of the log-asset. As opposed to previous quantizations based on Euler (or more sophisticated) discretization schemes, our method reveals to be fast and accurate, to the point that it is possible to calibrate the models on real data. Moreover, our approach allows to price options in multi factor stochastic volatility models including jumps. As a motivating example, we calibrate a Tempered Stable model on market data. This represents the first application of quantization to a pure jump process.

Closed-form variance swap prices under general affine GARCH models and their continuous-time limits Abstract Fully explicit closed-form expressions are developed for the fair strike prices of discrete-time variance swaps under general affine GARCH type models that have been risk-neutralized with a family of variance dependent pricing kernels. The methodology relies on solving differential recursions for the coefficients of the joint cumulant generating function of the log price and the conditional variance processes. An alternative derivation is provided in the case of Gaussian innovations. Using standard assumptions on the asymptotic behavior of the GARCH parameters as the sampling frequency increases, the diffusion limit of a Gaussian GARCH model is derived and the convergence of the variance swap prices to its continuous-time limit is further investigated. Numerical examples on the term structure of the variance swap rates and on the convergence results are also presented.

Time-consistent risk-constrained dynamic portfolio optimization with transactional costs and time-dependent returns Abstract Dynamic portfolio optimization has a vast literature exploring different simplifications by virtue of computational tractability of the problem. Previous works provide solution methods considering unrealistic assumptions, such as no transactional costs, small number of assets, specific choices of utility functions and oversimplified price dynamics. Other more realistic strategies use heuristic solution approaches to obtain suitable investment policies. In this work, we propose a time-consistent risk-constrained dynamic portfolio optimization model with transactional costs and Markovian time-dependence. The proposed model is efficiently solved using a Markov chained stochastic dual dynamic programming algorithm. We impose one-period conditional value-at-risk constraints, arguing that it is reasonable to assume that an investor knows how much he is willing to lose in a given period. In contrast to dynamic risk measures as the objective function, our time-consistent model has relatively complete recourse and a straightforward lower bound, considering a maximization problem. We use the proposed model for approximately solving: (i) an illustrative problem with 3 assets and 1 factor with an autoregressive dynamic; (ii) a high-dimensional problem with 100 assets and 5 factors following a discrete Markov chain. In both cases, we empirically show that our approximate solution is near-optimal for the original problem and significantly outperforms selected (heuristic) benchmarks. To the best of our knowledge, this is the first systematic approach for solving realistic time-consistent risk-constrained dynamic asset allocation problems.