Real Market Data Research Articles

Variance and volatility swaps valuations with the stochastic liquidity risk

This paper focuses on the variance and volatility swaps pricing in the framework of a liquidity-adjusted underlying assets model. The pricing formulas of discretely-sampled variance and volatility swaps are obtained under the stochastic liquidity risk by deriving the characteristic function and solving the governing PDEs. The limiting properties for the pricing formulas of discretely-sampled variance and volatility swaps, namely the pricing formulas of continuous sampling, are also given. Finally, to illustrate the performance of the pricing formulas, some detailed numerical experiments are provided, including the comparisons with Monte Carlo (MC) simulations results, the effects of stochastic liquidity and parameters variation on derivative prices as well as the empirical studies from real-market data.

Early Warning Signs of Financial Market Turmoils

Volatility clustering and fat tails are prominently observed in financial markets. Here, we analyze the underlying mechanisms of three agent-based models explaining these stylized facts in terms of market instabilities and compare them on empirical grounds. To this end, we first develop a general framework for detecting tail events in stock markets. In particular, we introduce Hawkes processes to automatically identify and date onsets of market turmoils which result in increased volatility. Second, we introduce three different indicators to predict those onsets. Each of the three indicators is derived from and tailored to one of the models, namely quantifying information content, critical slowing down or market risk perception. Finally, we apply our indicators to simulated and real market data. We find that all indicators reliably predict market events on simulated data and clearly distinguish the different models. In contrast, a systematic comparison on the stocks of the Forbes 500 companies shows a markedly lower performance. Overall, predicting the onset of market turmoils appears difficult, yet, over very short time horizons high or rising volatility exhibits some predictive power.

Portfolio management for a wind‐storage system based on distributionally robust optimisation considering a flexible ramping product

A wind-storage system (WSS) can use the synergy between wind farms and battery storages to mitigate the uncertainty of wind. With the flexible ramping capability of battery storages, a WSS can diversify its portfolio by selling energy, committing regulation services and offering flexible ramping products in a power market. The portfolio management problem of a WSS is studied here. The uncertainties of market prices and wind power are considered. In order to maximise the expected profit in the worst case, a distributionally robust optimisation model is proposed. The model is then reformulated as a solvable semidefinite optimisation problem. Real market data are used in the simulation, which validates the proposed method.

Maximizing performance with an eye on the finances: a chance-constrained model for football transfer market decisions

Composing a team of professional players is among the most crucial decisions in association football. Nevertheless, transfer market decisions are often based on myopic objectives and are questionable from a financial point of view. This paper introduces a chance-constrained model to provide analytic support to club managers during transfer windows. The model seeks a top-performing team while adapting to different budgets and financial-risk profiles. In addition, it provides a new rating system that is able to numerically reflect the on-field performance of football players and thus contribute to an objective assessment of football players. The model and rating system are tested on a case study based on real market data. The data from the case study are available online for the benefit of future research.

Optimal Day-Ahead Scheduling of Microgrids with Battery Energy Storage System

Optimal scheduling is a requirement for microgrids to participate in current and future energy markets. Although the number of research articles on this subject is on the rise, there is a shortage of papers containing detailed mathematical modeling of the distributed energy resources available in a microgrid. To address this gap, this paper presents in detail how to mathematically model resources such as battery energy storage systems, solar generation systems, directly controllable loads, load shedding, scheduled intentional islanding, and generation curtailment in the microgrid optimal scheduling problem. The proposed modeling also includes a methodology to determine the availability cost of battery and solar systems assets. Simulations were carried out considering energy prices from an actual time-of-use tariff, costs based on real market data, and scenarios with scheduled islanding. Simulation results provide support to validate the proposed model. Data illustrate how energy arbitrage can reduce microgrid costs in a time-of-use tariff. Results also show how the microgrid’s self-sufficiency and the storage system’s capacity can impact the microgrid’s energy bill. The findings also bring out the need to consider the scheduled islanding event in the day-ahead optimization for microgrids.

Coordinated Bidding Strategy of Wind Farms and Power-to-Gas Facilities Using a Cooperative Game Approach

Power-to-gas (P2G) technology is an effective way to provide additional flexibility to electricity systems with high penetration of renewable energy. Effective P2G bidding strategies in electricity markets can bring in arbitrage opportunities. This article proposes a coordinated bidding strategy for wind farms and P2G facilities using a cooperative game approach. A coordination framework for optimizing the profit of wind farms and P2G facilities is presented, in which wind power participates in day-ahead, real-time and reserve markets, and the P2G facilities participate in day-ahead and reserve markets for arbitrage. Wind farms strategically purchase reserve to avoid high imbalance penalties. As a coalition, wind farms can provide energy to P2G facilities and receive reserve capacity in return. The coordinated bidding strategy is modeled on stochastic programming to maximize the expected joint profit of wind farms and P2G facilities. Nucleolus and Shapley-value based methods are proposed to allocate profits to each player in the coalition. Numerical results based on real world market data demonstrate that the proposed bidding strategy can increase the profits of both wind farms and P2G facilities. Strategically purchasing reserve can reduce the imbalance penalties of wind farms, and participating in reserve markets of P2G facilities can increase profit.

Locational Marginal Price Forecasting: A Componential and Ensemble Approach

Short-term locational marginal price (LMP) forecasting is the traditional problem of market participants and other institutions maximizing their profit. Most electricity market organizers in the world release the data of LMP along with its three components, i.e., the energy, congestion, and loss components. The series of the three components have their own patterns and driving factors, and can be utilized to improve the accuracy of LMP forecasting. However, most existing studies have focused on direct LMP forecasting and have barely noticed this characteristic. In this paper, we aim to bridge the gap between the released data of the three components and LMP forecasting through a componential and ensemble approach. Three individual forecasting models are selected and trained for these components, and an ensemble framework that stacks the summation LMP results and the direct results is proposed to enhance the overall accuracy and robustness. Numerical experiments with real market data are conducted to show the good performance of this novel approach.

Modelling Stock Markets by Multi-agent Reinforcement Learning

Quantitative finance has had a long tradition of a bottom-up approach to complex systems inference via multi-agent systems (MAS). These statistical tools are based on modelling agents trading via a centralised order book, in order to emulate complex and diverse market phenomena. These past financial models have all relied on so-called zero-intelligence agents, so that the crucial issues of agent information and learning, central to price formation and hence to all market activity, could not be properly assessed. In order to address this, we designed a next-generation MAS stock market simulator, in which each agent learns to trade autonomously via reinforcement learning. We calibrate the model to real market data from the London Stock Exchange over the years 2007 to 2018, and show that it can faithfully reproduce key market microstructure metrics, such as various price autocorrelation scalars over multiple time intervals. Agent learning thus enables accurate emulation of the market microstructure as an emergent property of the MAS.

A Q-learning agent for automated trading in equity stock markets

Trading strategies play a vital role in Algorithmic trading, a computer program that takes and executes automated trading decisions in the stock market. The conventional wisdom is that the same trading strategy is not profitable for all stocks all the time. The selection of a trading strategy for the stock at a particular time instant is the major research problem in the stock market trading. An optimal dynamic trading strategy generated from the current pattern of the stock price trend can attempt to solve this problem. Reinforcement Learning can find this optimal dynamic trading strategy by interacting with the actual stock market as its environment. The representation of the state of the environment is crucial for performance. We have proposed two different ways to represent the discrete states of the environment. In this work, we trained the trading agent using the Q-learning algorithm of Reinforcement Learning to find optimal dynamic trading strategies. We experimented with the two proposed models on real stock market data from the Indian and American stock markets. The proposed models outperformed the Buy-and-Hold and Decision-Tree based trading strategy in terms of profitability.

Principal component analysis based construction and evaluation of cryptocurrency index

Decentralized nature of cryptocurrencies, irrational cryptocurrency valuations and severe price volatility in cryptocurrency market makes it a formidable task for investors to pick individual coins, and rather investors would prefer to invest money on the entire cryptocurrency market accurately represented by a cryptocurrency index. This paper proposes the design of a Principal Component Analysis based methodology to construct a dynamic cryptocurrency index that accurately tracks the movement of the entire cryptocurrency market. Our analysis on real market data shows why first component derived from PCA is sufficient to construct the cryptocurrency index and how to determine the number of constituents while building the index. Proposed PCA based tool, tested on actual historical cryptocurrency data, takes into account the changing dynamics of the cryptocurrency market by regularly shifting the number of constituents as well as the weights and is able to capture the evolving pattern in the cryptocurrency market. The proposed index has been validated by three-factor pricing model, consisting of market, size and momentum factors. Subsequently, the proposed index has been compared with other existing indexes based on the results of three-factor model. In summary, the paper presents a robust mathematical model for construction of a dynamic cryptocurrency index that can be used as a tool to analyze the return on investments as well as to study the fluctuations present in the cryptocurrency market.

A new algorithm for calibrating local regime-switching models

Abstract In quantitative finance practice, model calibration is a key challenge. This is especially so when a local regime-switching model needs to be calibrated because designing an efficient and reliable algorithm to obtain local volatility values as a function of underlying price and time is important for the model to be successfully used in practice. Therefore, this paper proposes a new algorithm for calibrating local regime-switching models with observed option prices available for a particular market that is suitable for this type of model. The newly proposed algorithm is tested with calibrations performed on synthetic as well as real market data. Our empirical test results indicate that the algorithm has great potential to be used in financial risk management.

A data-driven framework for consistent financial valuation and risk measurement

In this paper, we propose a general data-driven framework that unifies the valuation and risk measurement of financial derivatives, which is especially useful in markets with thinly-traded derivatives. We first extract the empirical characteristic function from market-observable time series for the underlying asset prices, and then utilize Fourier techniques to obtain the physical nonparametric density and cumulative distribution function for the log-returns process, based on which we compute risk measures. Then we risk-neutralize the nonparametric density and distribution functions to model-independently valuate a variety of financial derivatives, including path-independent European options and path-dependent exotic contracts. By estimating the state-price density explicitly, and utilizing a convenient basis representation, we are able to greatly simplify the pricing of exotic options all within a consistent model-free framework. Numerical examples, and an empirical example using real market data (Brent crude oil prices) illustrate the accuracy and versatility of the proposed method in handling pricing and risk management of multiple financial contracts based solely on observable time series data.

A minimax regret approach for robust multi-objective portfolio selection problems with ellipsoidal uncertainty sets

Since security return cannot be accurately estimated using past data, in this paper it is assumed to take values in a given ellipsoidal uncertainty set. This paper aims to discuss a robust multi-objective portfolio selection problem based on the minimax regret criterion under an ellipsoidal uncertainty sets, in which the two objective functions are the portfolio return to be maximized and the mean absolute deviation as a risk measure to be minimized. The robust counterpart formulation for the proposed model is firstly presented, then an algorithm based on the relaxation procedure is designed to solve the robust counterpart formulation with second-order cone constraints and infinite constraints. Finally, a practical example based on real market data is presented to illustrate the effectiveness of the proposed model and the algorithm. Compared with the traditional robust portfolio model based on minimax robustness, the robust minimax regret optimal solutions proposed in this paper have better performance on several evaluation criteria.

A Data-Driven Framework for Consistent Financial Valuation and Risk Measurement

In this paper, we propose a general data-driven framework that unifies the valuation and risk measurement of financial derivatives, which is especially useful in markets with thinly-traded derivatives. We first extract the empirical characteristic function from market-observable time series for the underlying asset prices, and then utilize Fourier techniques to obtain the physical non-parametric density and cumulative distribution function for the log-returns process, based on which we compute risk measures. Then we risk-neutralize the non-parametric density and distribution functions to model-independently valuate a variety of financial derivatives, including path-independent European options and path-dependent exotic contracts. By estimating the state-price density explicitly, and utilizing a convenient basis representation, we are able to greatly simplify the pricing of exotic options all within a consistent model-free framework. Numerical examples, and an empirical example using real market data (Brent crude oil prices) illustrate the accuracy and versatility of the proposed method in handling pricing and risk management of multiple financial contracts based solely on observable time series data.

Entropy of Graphs in Financial Markets

This article analyzes the eigenvalues of financial graphs and discusses different types of graphs using random graph theory. We found that the energy-based Renyi index is an effective tool for studying the spectrum of financial graphs. The entropy of financial graphs is usually different from the theoretical predictions of random graph theory, which implies the existence of rich structures. This article also constructed some benchmark graphs for comparative analysis through the classic financial models. The calculations show that the geometric Brownian motion and the one factor model correspond to completely different entropy values based on eigenvalues, thus providing two extreme cases for characterizing real graph entropy. In particular, we find a high correlation between the degree-based Renyi index and the eigenvalue-based Renyi index based on real market data. This article shows the analysis of the structure and complexity of financial graphs from the perspective of graph entropy, thus providing a new way to analyze different types of financial graphs.

Portfolio Management on an Emerging Market: Dynamic Strategy or Passive Strategy?

At first glance, the portfolio management strategy seems like a resolved question, but practitioners continue to perform poorly on the stock markets. This paper highlights the portfolio management in the specific case of the West African regional stock exchange, regarding two management strategies. These are dynamic strategy and passive strategy. Within this framework, we will compare an investor who is constantly betting on price fluctuations with another who is betting on dividends. Its originality lies in the approach that is used. Through a simulation methodology based on real market data, the main results indicate that an emerging market is a savings market more than it is a speculation market. Besides, other results indicate that, one can predict on the West African regional stock exchange tomorrow’s prices from today’s prices. This does not mean that investors are making good predictions because the predictability of prices is due to the absence of changes in asset prices on the market. We draw the conclusion that it is difficult for one speculator to outperform the other. A rational investor would benefit from anticipating the distribution of dividends rather than focusing on price fluctuations. Consequently, the buy and hold strategy is therefore the best to be rewarded in an emerging market. Nonetheless, this practice can lead to a decline in liquidity.

Real Estate Market Data Analysis and Prediction Based on Minor Advertisements Data and Locations’ Geo-codes

Real Estate Market Data Analysis and Prediction Based on Minor Advertisements Data and Locations’ Geo-codes

A new artificial intelligence GANNATS model predicts gasoline demand of Saudi Arabia

This paper develops a novel AI and data-driven predictive model to analyze and forecast energy markets, and tests it for gasoline demand of Saudi Arabia. The AI model is based on a genetic algorithm (GA), artificial neural network (ANN), and data mining (DM) approach for time-series (TS) analysis, referred to as GANNATS. The GANNATS predictive model was successfully designed, trained, validated, and tested using real historical market data. Results show that the model yields accurate predictions with robust key performance indicators. A double cross-validation of the model verified that Saudi Arabia's gasoline demand declined by 2.5% in 2017 from its 2016 level. The model forecasts that Saudi gasoline demand will maintain a mild growth over the short-term outlook. Variables impact and screening analysis was performed to identify the influencing factors driving the gasoline demand. The recent decline in Saudi gasoline demand is primarily attributed to the improvements in vehicle efficiency, lifting of fuel price subsidies, declining population growth, and changes in consumer behavior. This paper enriches existing knowledge of best practices for forecasting domestic and global gasoline demand. In addition, the methodology presented improves on traditional econometric models and enhances the predictability and accuracy of forecasts of gasoline demand.

Design and operation strategy for multi-use application of battery energy storage in wind farms

Abstract Wind power plants have become a mainstay of renewable energy production, especially in Germany, and they accounted for 21 % of the gross electricity production there in 2019. Nonetheless, energy production from wind turbines depends on the weather and wind farms require active power from the electrical power system in windless periods. Battery energy storage systems (BESS) are well suited to increase the integration and optimal utilisation of wind energy and reduce the significant energy consumption cost. In this paper, the authors present a methodology to size a BESS for self-consumption in windless times optimally and operate the BESS in a technically and economically optimal way. For the latter, a multi-use operation strategy is developed, where the BESS reduces energy demand in windless periods, balances forecast deviations in its balancing zone and stores energy from wind power curtailment. Mixed integer linear programming is used to identify the optimal operation strategy for the wind farm storage system considering intra-day energy market prices and imbalances. Furthermore, BESS market participation factors are introduced that are used to consider BESS’ payback time and lifetime in the development of the optimal multi-use operation strategy. The methodology proposed is verified in a case study using real wind farm measurements, forecasts and market data for Germany. It has been verified that the multi-use operation developed is economically attractive. Furthermore, the energy demand of a wind farm has been reduced by about 13 % and imbalance costs by about 37.5 %.

Complexity behaviours of agent-based financial dynamics by hetero-distance contact process

To model the nonlinear and complex dynamics of financial systems, a new model for the formation of financial prices is developed, taking into account heterogeneity in the communication range of market agents. Specifically, one type of agents can potentially gather and disseminate information via additional long-distance contacts compared to the other type, and interactions among these agents are imitated by the contact process. The financial price series of the model are simulated, analysed, and compared with multiple major stock indices in nonlinear fluctuation behaviours. To better investigate the complexity structure of the financial time series, a generalization of the multiscale entropy method is developed to consider various moments in coarse graining. Overall, the modelled series are found to follow a fat-tail distribution and a pattern of complexity structure over both moments and time scales similar to real market data. This similarity is also shown by applying alternative complexity measure, matching energy method. Moreover, the wealth inequality among agents is found to increase over time within each type as well as across two types, further revealing nonlinear price and welfare dynamics of the model.