Modelling commodity value at risk with Psi Sigma neural networks using open–high–low–close data

The motivation for this paper is to investigate the use of a promising class of neural network models, Psi Sigma (PSI), when applied to the task of forecasting the one-day ahead value at risk (VaR) of the oil Brent and gold bullion series using open–high–low–close data. In order to benchmark our results, we also consider VaR forecasts from two different neural network designs, the multilayer perceptron and the recurrent neural network, a genetic programming algorithm, an extreme value theory model along with some traditional techniques such as an ARMA-Glosten, Jagannathan, and Runkle (1,1) model and the RiskMetrics volatility. The forecasting performance of all models for computing the VaR of the Brent oil and the gold bullion is examined over the period September 2001–August 2010 using the last year and half of data for out-of-sample testing. The evaluation of our models is done by using a series of backtesting algorithms such as the Christoffersen tests, the violation ratio and our proposed loss function that considers not only the number of violations but also their magnitude. Our results show that the PSI outperforms all other models in forecasting the VaR of gold and oil at both the 5% and 1% confidence levels, providing an accurate number of independent violations with small magnitude

Using CAViaR models with implied volatility for value-at-risk estimation

This paper proposes VaR estimation methods that are a synthesis of conditional autoregressive value at risk (CAViaR) time series models and implied volatility. The appeal of this proposal is that it merges information from the historical time series and the different information supplied by the market’s expectation of risk. Forecast combining methods, with weights estimated using quantile regression, are considered. We also investigate plugging implied volatility into the CAViaR models, a procedure that has not been considered in the VaR area so far. Results for daily index returns indicate that the newly proposed methods are comparable or superior to individual methods, such as the standard CAViaR models and quantiles constructed from implied volatility and the empirical distribution of standardised residual. We find that the implied volatility has more explanatory power as the focus moves further out into the left tail of the conditional distribution of S&P500 daily returns

Risk Management using Model Predictive Control

Forward planning and risk management are crucial for the success of any system or business dealing with the uncertainties of the real world. Previous approaches have largely assumed that the future will be similar to the past, or used simple forecasting techniques based on ad-hoc models. Improving solutions requires better projection of future events, and necessitates robust forward planning techniques that consider forecasting inaccuracies. This work advocates risk management through optimal control theory, and proposes several techniques to combine it with time-series forecasting. Focusing on applications in foreign exchange (FX) and battery energy storage systems (BESS), the contributions of this thesis are three-fold. First, a short-term risk management system for FX dealers is formulated as a stochastic model predictive control (SMPC) problem in which the optimal risk-cost profiles are obtained through dynamic control of the dealers’ positions on the spot market. Second, grammatical evolution (GE) is used to automate non-linear time-series model selection, validation, and forecasting. Third, a novel measure for evaluating forecasting models, as a part of the predictive model in finite horizon optimal control applications, is proposed. Using both synthetic and historical data, the proposed techniques were validated and benchmarked. It was shown that the stochastic FX risk management system exhibits better risk management on a risk-cost Pareto frontier compared to rule-based hedging strategies, with up to 44.7% lower cost for the same level of risk. Similarly, for a real-world BESS application, it was demonstrated that the GE optimised forecasting models outperformed other prediction models by at least 9%, improving the overall peak shaving capacity of the system to 57.6%

The History of the Quantitative Methods in Finance Conference Series. 1992-2007

This report charts the history of the Quantitative Methods in Finance (QMF) conference from its beginning in 1993 to the 15th conference in 2007. It lists alphabetically the 1037 speakers who presented at all 15 conferences and the titles of their papers.

Risk Management using Model Predictive Control

Forward planning and risk management are crucial for the success of any system or business dealing with the uncertainties of the real world. Previous approaches have largely assumed that the future will be similar to the past, or used simple forecasting techniques based on ad-hoc models. Improving solutions requires better projection of future events, and necessitates robust forward planning techniques that consider forecasting inaccuracies. This work advocates risk management through optimal control theory, and proposes several techniques to combine it with time-series forecasting. Focusing on applications in foreign exchange (FX) and battery energy storage systems (BESS), the contributions of this thesis are three-fold. First, a short-term risk management system for FX dealers is formulated as a stochastic model predictive control (SMPC) problem in which the optimal risk-cost profiles are obtained through dynamic control of the dealers’ positions on the spot market. Second, grammatical evolution (GE) is used to automate non-linear time-series model selection, validation, and forecasting. Third, a novel measure for evaluating forecasting models, as a part of the predictive model in finite horizon optimal control applications, is proposed. Using both synthetic and historical data, the proposed techniques were validated and benchmarked. It was shown that the stochastic FX risk management system exhibits better risk management on a risk-cost Pareto frontier compared to rule-based hedging strategies, with up to 44.7% lower cost for the same level of risk. Similarly, for a real-world BESS application, it was demonstrated that the GE optimised forecasting models outperformed other prediction models by at least 9%, improving the overall peak shaving capacity of the system to 57.6%

Heuristic optimisation in financial modelling

There is a large number of optimisation problems in theoretical and applied finance that are difficult to solve as they exhibit multiple local optima or are not ‘well-behaved' in other ways (e.g., discontinuities in the objective function). One way to deal with such problems is to adjust and to simplify them, for instance by dropping constraints, until they can be solved with standard numerical methods. We argue that an alternative approach is the application of optimisation heuristics like Simulated Annealing or Genetic Algorithms. These methods have been shown to be capable of handling non-convex optimisation problems with all kinds of constraints. To motivate the use of such techniques in finance, we present several actual problems where classical methods fail. Next, several well-known heuristic techniques that may be deployed in such cases are described. Since such presentations are quite general, we then describe in some detail how a particular problem, portfolio selection, can be tackled by a particular heuristic method, Threshold Accepting. Finally, the stochastics of the solutions obtained from heuristics are discussed. We show, again for the example from portfolio selection, how this random character of the solutions can be exploited to inform the distribution of computation

Uncertainty aversion in a heterogeneous agent model of foreign exchange rate formation

This paper provides what we believe to be the first empirical test of whether investors in the foreign exchange market are uncertainty averse. We do this using a heterogeneous agents model in which fundamentalist and chartist beliefs of the exchange rate co-exist and are allowed to be either uncertainty neutral or uncertainty averse. Uncertainty aversion is modelled using the maxmin expected utility approach. We find significant evidence of uncertainty aversion in the FX market where in particular fundamentalists are found to be largely uncertainty neutral while chartists are mainly uncertainty averse. Inclusion of uncertainty averse agents significantly improves the performance of the model

Portfolio management using computational intelligence approaches. Forecasting and Optimising the Stock Returns and Stock Volatilities with Fuzzy Logic, Neural Network and Evolutionary Algorithms.

Portfolio optimisation has a number of constraints resulting from some practical matters and regulations. The closed-form mathematical solution of portfolio optimisation problems usually cannot include these constraints. Exhaustive search to reach the exact solution can take prohibitive amount of computational time. Portfolio optimisation models are also usually impaired by the estimation error problem caused by lack of ability to predict the future accurately. A number of Multi-Objective Genetic Algorithms are proposed to solve the problem with two objectives subject to cardinality constraints, floor constraints and round-lot constraints. Fuzzy logic is incorporated into the Vector Evaluated Genetic Algorithm (VEGA) to but solutions tend to cluster around a few points. Strength Pareto Evolutionary Algorithm 2 (SPEA2) gives solutions which are evenly distributed portfolio along the effective front while MOGA is more time efficient. An Evolutionary Artificial Neural Network (EANN) is proposed. It automatically evolves the ANN¿s initial values and structures hidden nodes and layers. The EANN gives a better performance in stock return forecasts in comparison with those of Ordinary Least Square Estimation and of Back Propagation and Elman Recurrent ANNs. Adaptation algorithms for selecting a pair of forecasting models, which are based on fuzzy logic-like rules, are proposed to select best models given an economic scenario. Their predictive performances are better than those of the comparing forecasting models. MOGA and SPEA2 are modified to include a third objective to handle model risk and are evaluated and tested for their performances. The result shows that they perform better than those without the third objective

Essays on Exchange Rate Forecasting

The literature in exchange rate forecasting has met a lot of interest from the academia and practitioners, as well. Since most exchange rates entered the free floating regime, the forecasting ability of the models has been challenged. In this thesis, we explore several aspects of exchange rate forecasting. We first examine the contribution of technical indicators to exchange rate forecasting. Next, we create a new methodological approach that is a hybrid of the Iterated Model Combination and the Constrained Predictors approach, on which we also apply positivity constraints in the forecasts. Last, we focus on the realized volatility of exchange rates. In Chapter 2, we test the forecasting ability of several theoretically motivated models along with the forecasting ability of technical indicators, an atheoretical tool that identifies patterns and produces market signals. We use monthly data ranging from January 1974 to December 2014 for six widely traded currencies. We show that both types of predictors provide valuable information about future currency movements. To efficiently summarize the information content in candidate predictors, we extract the principal components of each group of predictors. Our findings suggest that combining information from both technical indicators and macroeconomic variables significantly improves and stabilizes exchange rate forecasts versus using either type of information alone. In Chapter 3, we focus on forecasting daily exchange rate returns of six widely traded currencies using financial predictors and combination and dimensionality reduction methods. We propose a hybrid Iterated Combination with Constrained Predictors (ICCP) approach. In addition, we examine the impact of positivity constraints on the forecasting ability of each method. Our results indicate that the proposed hybrid method outperforms the simple linear bivariate method and both the Iterated Combination and the Predictor Constrained approaches. Furthermore, positivity constraints significantly improve the forecasting ability of all methods. We provide several robustness tests by changing several specications of the forecasting experiment. Chapter 4 provides empirical evidence in forecasting realized volatility in exchange rates. Forecasting realized volatility in exchange rates is very important for practitioners and has been vividly discussed among academics. Our target is to contribute to this dialogue by providing a comprehensive analysis of forecasting realized volatility in exchange rates. For the purposes of our analysis, we use data from January 1986 to December 2012 for four widely traded currencies, GBP, CHF, YEN, EUR and a composite one (FX Aggregate). We show that macroeconomic and financial variables provide additional information to the autoregressive term and can benefit the forecasting accuracy. We apply a large set of 38 variables, supported by the literature, which shed light on different macroeconomic aspects. We answer the question of variables selection by extracting all available information with the use of several shrinkage methods, machine learning techniques, dimensionality reduction techniques and combination forecasts. In order to resolve the problem of method selection the forecaster faces, we aggregate all methods and form an amalgamation of forecasts. We test whether outliers drive the performance of this type of naive combination. We apply different specifications of naive combination by trimming the first, second and third outlier from the top and bottom. Our findings suggest that macroeconomic variables should be accounted when forecasting realized volatility. Moreover, the amalgamation of forecasts benefits the forecasting experiment significantly, irrespective of the specification under consideration