100 research outputs found
Deep learning for trading and hedging in financial markets
Deep learning has achieved remarkable results in many areas, from image classification, language translation to question answering. Deep neural network models have proved to be good at processing large amounts of data and capturing complex relationships embedded in the data. In this thesis, we use deep learning methods to solve trading and hedging problems in the financial markets. We show that our solutions, which consist of various deep neural network models, could achieve better accuracies and efficiencies than many conventional mathematical-based methods.
We use Technical Analysis Neural Network (TANN) to process high-frequency tick data from the foreign exchange market. Various technical indicators are calculated from the market data and fed into the neural network model. The model generates a classification label, which indicates the future movement direction of the FX rate in the short term. Our solution can surpass many well-known machine learning algorithms on classification accuracies.
Deep Hedging models the relationship between the underlying asset and the prices of option contracts. We upgrade the pipeline by removing the restriction on trading frequency. With different levels of risk tolerances, the modified deep hedging model can propose various hedging solutions. These solutions form the Efficient Hedging Frontier (EHF), where their associated risk levels and returns are directly observable. We also show that combining a Deep Hedging model with a prediction algorithm ultimately increases the hedging performances.
Implied volatility is the critical parameter for evaluating many financial derivatives. We propose a novel PCA Variational Auto-Enocder model to encode three independent features of implied volatility surfaces from the European stock markets. This novel encoding brings various benefits to generating and extrapolating implied volatility surfaces. It also enables the transformation of implied volatility surfaces from a stock index to a single stock, significantly improving the efficiency of derivatives pricing
Neural Tangent Kernel in Implied Volatility Forecasting: A Nonlinear Functional Autoregression Approach
Implied volatility (IV) forecasting is inherently challenging due to its high dimensionality across various moneyness and maturity, and nonlinearity in both spatial and temporal aspects. We utilize implied volatility surfaces (IVS) to represent comprehensive spatial dependence and model the nonlinear temporal dependencies within a series of IVS. Leveraging advanced kernel-based machine learning techniques, we introduce the functional Neural Tangent Kernel (fNTK) estimator within the Nonlinear Functional Autoregression framework, specifically tailored to capture intricate relationships within implied volatilities. We establish the connection
between fNTK and kernel regression, emphasizing its role in contemporary nonparametric statistical modeling. Empirically, we analyze S&P 500 Index options from January 2009 to December 2021, encompassing more than 6 million European calls and puts, thereby showcasing the superior forecast accuracy of fNTK.We demonstrate the significant economic value of having an accurate implied volatility forecaster within trading strategies. Notably, short
delta-neutral straddle trading, supported by fNTK, achieves a Sharpe ratio ranging from 1.45 to 2.02, resulting in a relative enhancement in trading outcomes ranging from 77% to 583%
Deep learning-based pricing in stochastic volatility models
Diplomová práce je zaměřena na oceňování opcí v modelech stochastické volatility pomocí neuronových sítí. Nejprve jsou vygenerovány ceny opcí v Hestonově modelu pomocí Heston-Lewisovy formule. Pomocí těchto cen je natrénovaná neuronová síť, která nejprve odhadne parametry Hestonova modelu a poté z těchto parametrů zpět odhadne ceny opcí. Natrénovaná neuronová síť je také použita na odhad cen opcí pro reálná tržní data.ObhájenoThis thesis is focused on option pricing in stochastic volatility models using neural networks. First, option prices in the Heston model are generated using the Heston-Lewis formula. A neural network is then trained using these prices to first estimate the parameters of the Heston model and then back-estimate option prices from these parameters. The trained neural network is also used to estimate option prices for real market data
Data-Driven Models: An Alternative Discrete Hedging Strategy
Options hedging is a critical problem in financial risk management. The prevailing approach in financial derivative pricing and hedging has been to first assume a parametric model describing the underlying price dynamics. An option model function is then calibrated to current market option prices and various sensitivities are computed and used to hedge the option risk. It has been recognized that computing hedging position from the sensitivity of the calibrated model option value function is inadequate in minimizing the variance of the option hedging risk, as it fails to capture the model parameter dependence on the underlying price.
We propose several data-driven approaches to directly learn a hedging function from the historical market option and underlying data by minimizing certain measures of the local hedging risk and total hedging risk. This thesis will focus on answering the following questions: 1) Can we efficiently build direct data-driven models for discrete hedging problems that outperform existing state-of-art parametric hedging models based on the market prices? 2) Can we incorporate feature selection and feature extraction into the data-driven models to further improve the performance of the discrete hedging? 3) Can we build efficient models for both the one-step local risk hedging problem and multi-step total risk hedging problem based on the state-of-art learning framework such as deep learning framework and kernel learning framework?
Using the S&P 500 index daily options data for more than a decade ending in August 2015, we first propose a direct data-driven approach based on kernel learning framework and we demonstrate that the proposed method outperforms the parametric minimum variance hedging method, as well as minimum variance hedging corrective techniques based on stochastic volatility or local volatility models. Furthermore, we show that the proposed approach achieves significant gain over the implied Black-Sholes delta hedging for weekly and monthly hedging.
Following the direct data-driven kernel learning approach, we propose a robust encoder-decoder Gated Recurrent Unit (GRU) model, for optimal discrete option hedging. The proposed model utilizes the Black-Scholes model as a pre-trained model and incorporates sequential information and feature selection. Using the S&P 500 index European option market data from January 2, 2004, to August 31, 2015, we demonstrate that the weekly and monthly hedging performance of the proposed model significantly surpasses that of the data-driven minimum variance (MV) method, the regularized kernel data-driven model, and the SABR-Bartlett method.
In addition, the daily hedging performance of the proposed model also surpasses that of MV methods based on parametric models, the kernel method, and the SABR-Bartlett method.
Lastly, we design multi-step data-driven models to hedge the option discretely until the expiry. We utilize the SABR model and Local Volatility Function (LVF) to augment existing market data and thus alleviate the problem of scarcity in market option prices. The augmented market data is used to train a sufficient total risk hedging model
Four essays on quantitative economics applications to volatility analysis in Emerging Markets and renewable energy projects
[ES]Las decisiones financieras se pueden dividir en decisiones de inversión y decisiones de financiación. En lo que respecta a las decisiones de inversión, la incertidumbre acerca de la dinámica futura de las variables económicas y de las financieras tiene un rol fundamental. Eso, se explica porque los retornos esperados por las empresas y por los inversionistas se pueden ver afectados por los movimientos adversos en los mercados financieros y por los altos niveles de volatilidad. Como consecuencia, resulta crucial realizar un adecuado análisis y modelación de la volatilidad para el proceso de toma de decisiones financieras, por parte de las empresas y el diseño de estrategias de inversión y cobertura por parte de los inversionistas. En este sentido, el estudio de la volatilidad se ha convertido en uno de los temas más interesantes de la investigación en finanzas. Lo anterior ha cobrado mayor relevancia en los últimos años, teniendo en cuenta el escenario de alta volatilidad e incertidumbre que afrontan los mercados a nivel global. Este documento tiene como objetivo abordar cuatro cuestiones centrales, las cuales están relacionadas con la volatilidad financiera como campo de investigación. Esas cuestiones son, la transmisión y spillovers de volatilidad en mercados emergentes, la calibración de la superficie de volatilidad para proyectos de energía renovable y el pronóstico de los rendimientos de activos energéticos y spillovers de volatilidad a través de técnicas de machine learning. En el primer capítulo del documento, se examinan los efectos de transmisión de volatilidad entre un índice de energía y un índice financiero para los Mercados Emergentes. En consecuencia, mediante el uso de un modelo DCC, se muestra que los efectos de transmisión de volatilidad entre los índices empleados para la crisis subprime y la crisis del COVID-19 fueron diferentes. Lo anteriormente dicho, considerando que la primera crisis se originó en el sector financiero y luego se extendió al resto de la economía, mientras que la segunda se originó en el sector real y posteriormente afectó al resto de la economía. Teniendo en cuenta que la relación entre la volatilidad de los mercados es cambiante en el tiempo, en el segundo capítulo se llevó a cabo un análisis dinámico de los spillovers de volatilidad entre materias primas, Bitcoin y un índice de Mercados Emergentes. Así, empleando la metodología propuesta por Diebold y Yilmaz (2012), se concluyó que los efectos de los spillovers de volatilidad entre los activos analizados no son constantes en dirección e intensidad a través del tiempo. En particular, para períodos de crisis como el de la pandemia del COVID-19, hay reversiones en la dirección de los spillovers de volatilidad debido al sector en el que se originó la crisis. Además, en este capítulo se explota la naturaleza
dinámica de los spillovers de volatilidad. Por lo tanto, se planteó que el índice de spillovers de volatilidad propuesto por Diebold y Yilmaz puede ser usado como una medida para pronosticar periodos de alta turbulencia. Lo anterior se desarrolló a través de modelos econométricos tradicionales y de técnicas de machine learning. En el tercer capítulo del documento, se propone un modelo que predice los retornos de los
precios del carbono y del petróleo. En este sentido, se desarrolló un modelo híbrido, el cual combina las proyecciones obtenidas a partir de diferentes técnicas de machine learning y modelos econométricos tradicionales, obteniéndose resultados los cuales muestran las ventajas de emplear modelos híbridos que incorporan técnicas de machine learning, exclusivamente, para pronosticar variables financieras. Finalmente, en el capítulo cuatro, se presenta una metodología para la estimación de la volatilidad en la valoración de proyectos de energías renovables mediante opciones reales. En esta metodología, la cual es una extensión del enfoque de volatilidad implícita empleada para las opciones financieras, la volatilidad de un proyecto es la volatilidad implícita obtenida a partir de la superficie de la volatilidad de empresas comparables, según una determinada fecha de valoración y dada la relación deuda-capital de un proyecto de energía renovable. En
este análisis, se utilizó el modelo estocástico 'alfa-beta-rho' para calibrar la superficie de la volatilidad para la valoración mediante opciones reales. Por último, al final del documento se presentan las conclusiones derivadas de los capítulos mencionados, así como algunas recomendaciones para las futuras investigaciones.
[EN]Financial decisions can be divided in investment and financing decisions. Concerning investment decisions, the uncertainty about the future dynamics of financial and economic variables has a central role, considering that the returns expected by firms and investors can be affected by the adverse movements in financial markets and their high volatility. In consequence, the adequate volatility analysis and modeling is crucial for the firm’s financial decision-making process and the design of investing and hedging strategies by investors. In
this regard, the study of volatility has become one of the most interesting topics in finance research. The foregoing has become more relevant in recent years considering the scenario of high volatility and uncertainty faced by markets globally. This document aims to address four central issues related to financial volatility as a research area. These are, volatility transmission and spillovers in Emerging Markets, the calibration of the volatility surface for renewable energy projects and the forecast of energy assets returns and volatility spillovers
through machine learning techniques. In the first chapter of the document, the volatility transmission effects between an energy index and a financial index for Emerging Markets are examined. Then, by using a DCC
model, it is shown that the volatility transmission effects between the employed indices for the subprime crisis and the COVID-19 pandemic were different. This, considering that the former crisis originated in the financial sector and spread to the rest of the economy, while the second originated in the real sector and trasmitted to the rest of the economy posteriorly. Considering that the relationship between markets volatility is time-varying, in the second chapter, a dynamic analysis of volatility spillovers between commodities, Bitcoin and an Emerging Markets index is developed. Employing the methodology proposed by Diebold and Yilmaz (2012), it is concluded that the volatility spillovers effects between the analyzed assets is not constant in direction and intensity over time. In particular, for periods of crisis such as the COVID-19 pandemics, there are reversals in the direction of volatility spillovers due to the sector in which the crises originate. In addition, in this chapter the dynamic nature of volatility spillovers is exploited. Hence, the volatility spillover index proposed by Diebold
and Yilmaz is forecasted to be used as a measure to anticipate high turbulence periods. This, through both traditional econometric models and machine learning techniques. In the third chapter, a model for the prediction of carbon and oil prices is proposed. In this sense, a hybrid model that ensembles the forecasts obtained from different machine learning techniques and traditional econometric models is developed, obtaining results that show the advantages of employing hybrid models which combine machine learning techniques, exclusively, to forecast financial variables. In Chapter four, a methodology for the estimation of volatility in renewable energy projects valuation through real options is presented. In this methodology, which is an extension of the implied volatility approach employed for financial options, the volatility of the project is the implied volatility obtained from the volatility surface of comparable firms for a certain valuation date and given debt-to-equity relation of a renewable energy project. In this analysis, the stochastic ‘alpha-beta-rho’ model is utilized to calibrate the volatility surface for real option valuation purposes. Finally, the conclusions derived from the mentioned chapters are presented at the end of the document as well as some recommendations for future research
American Option Pricing using Self-Attention GRU and Shapley Value Interpretation
Options, serving as a crucial financial instrument, are used by investors to
manage and mitigate their investment risks within the securities market.
Precisely predicting the present price of an option enables investors to make
informed and efficient decisions. In this paper, we propose a machine learning
method for forecasting the prices of SPY (ETF) option based on gated recurrent
unit (GRU) and self-attention mechanism. We first partitioned the raw dataset
into 15 subsets according to moneyness and days to maturity criteria. For each
subset, we matched the corresponding U.S. government bond rates and Implied
Volatility Indices. This segmentation allows for a more insightful exploration
of the impacts of risk-free rates and underlying volatility on option pricing.
Next, we built four different machine learning models, including multilayer
perceptron (MLP), long short-term memory (LSTM), self-attention LSTM, and
self-attention GRU in comparison to the traditional binomial model. The
empirical result shows that self-attention GRU with historical data outperforms
other models due to its ability to capture complex temporal dependencies and
leverage the contextual information embedded in the historical data. Finally,
in order to unveil the "black box" of artificial intelligence, we employed the
SHapley Additive exPlanations (SHAP) method to interpret and analyze the
prediction results of the self-attention GRU model with historical data. This
provides insights into the significance and contributions of different input
features on the pricing of American-style options.Comment: Working pape
Stock Option Valuations and Constraint Enforcement Using Neural Networks
Stock option valuations have long been studied, being inherently non-linear financial derivatives. These instruments have a ubiquitous presence in institutional investment practice, and present many favourable and unique benefits to an investment portfolio. Neural Networks on the other hand have become a more familiar concept in recent times. They are by design set to deal with complex, non-linear classification and prediction tasks. Using Neural Networks to predict stock option prices has been studied at length, by various authors in the last 30 years. These studies have considered their relative performance against closed-form pricing solutions like the infamous Black-Scholes-Merton model, as well as in real-world settings. The collective conclusion that is deduced from past literature presents a clear case for their use in finance, albeit that there are some notable pitfalls, like the lack of interpretability and the ability to explicitly enforce certain constraints. Constraints such as option price bounds (upper and lower) and the Put-Call parity, that a stock option's value should satisfy have not been considered in many prior studies. This dissertation sets out to study stock option valuations using Neural Networks with techniques to enforce constraints. First, a functional and appropriately performing Neural Network configuration is derived that outputs European call and put option prices under one model. Thereafter, enforcement of the lower, upper and relative bounds (Put-Call parity) is incorporated into the model. Finally, the Neural Network application is extended to the real-world setting. The performance of the Neural Network model is assessed by means of mean error, as well as percentiles
Retail Trading in Options and the Rise of the Big Three Wholesalers
We document a rapid increase in retail trading in options in the United States. Facilitated by payment for order flow (PFOF) from wholesalers executing retail orders, retail trading recently reached over 60% of total market volume. Nearly 90% of PFOF comes from three wholesalers. Exploiting new flags in transaction‐level data, we isolate wholesaler trades and build a novel measure of retail options trading. Our measure comoves with equity‐based retail activity proxies and drops significantly during U.S. brokerage platform outages and trading restrictions. Retail investors prefer cheaper, weekly options with average bid‐ask spread of 12.6%, and lose money on average
Corporate Finance
This book comprises 19 papers published in the Special Issue entitled “Corporate Finance”, focused on capital structure (Kedzior et al., 2020; Ntoung et al., 2020; Vintilă et al., 2019), dividend policy (Dragotă and Delcea, 2019; Pinto and Rastogi, 2019) and open-market share repurchase announcements (Ding et al., 2020), risk management (Chen et al., 2020; Nguyen Thanh, 2019; Štefko et al., 2020), financial reporting (Fossung et al., 2020), corporate brand and innovation (Barros et al., 2020; Błach et al., 2020), and corporate governance (Aluchna and Kuszewski, 2020; Dragotă et al.,2020; Gruszczyński, 2020; Kjærland et al., 2020; Koji et al., 2020; Lukason and Camacho-Miñano, 2020; Rashid Khan et al., 2020). It covers a broad range of companies worldwide (Cameroon, China, Estonia, India, Japan, Norway, Poland, Romania, Slovakia, Spain, United States, Vietnam), as well as various industries (heat supply, high-tech, manufacturing)
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