A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Coronavirus impacts oil prices through volatility and direct effects.
This paper uses SampEn to measure and predict oil price volatility.
Paper uses neural networks to analyze oil price impact on Iranian stock and industry indices.
The paper analyzes gold, oil, and bitcoin futures volatility and basis.
Study exchange option pricing with stochastic volatility and correlation.
We created financial benchmarks for distribution shifts in crude oil prices and volatility.
A new Bachelier model explains oil option volatility during the pandemic.
Oil markets profoundly influence world economies through determination of prices of energy and transports. Using novel methodology devised in frequency domain, we study the information transmission mechanisms in oil-based commodity markets. Taking crude oil as a supply-side benchmark and heating oil and gasoline as dem…
Study reviews Bachelier model for negative oil prices post-COVID.
Study examines impact of oil and gold prices on Tehran Stock Exchange.
We detect and quantify asymmetries in volatility spillovers using the realized semivariances of petroleum commodities: crude oil, gasoline, and heating oil. During the 1987--2014 period we document increasing spillovers from volatility among petroleum commodities that substantially change after the 2008 financial crisi…
Study uses APT and QR to identify risk factors affecting crude oil returns.
QBVAR improves oil price forecasting across quantiles, especially for downside risk.
In order to obtain a reasonable and reliable forecast method for crude oil price volatility, this paper evaluates the forecast performance of single-regime GARCH models (including the standard linear GARCH model and the nonlinear GJR-GARCH and EGARCH models) and the two-regime Markov Regime Switching GARCH (MRS-GARCH) …
Model prices commodity futures and index options.
The paper develops a new model for rough volatility in commodity markets.
The Heston model is validated for option pricing using theoretical derivations and empirical market data.
Oil price data have a complicated multi-scale structure that may vary with time. We use time-frequency analysis to identify the main features of these variations and, in particular, the regime shifts. The analysis is based on a wavelet-based decomposition and analysis of the associated scale spectrum. The joint estimat…
The study examines how global economic policy uncertainty affects crude oil futures volatility.
A new multi-factor model improves commodity pricing accuracy.
It is commonly accepted that Commodities futures and forward prices, in principle, agree under some simplifying assumptions. One of the most relevant assumptions is the absence of counterparty risk. Indeed, due to margining, futures have practically no counterparty risk. Forwards, instead, may bear the full risk of def…
Model predicts volatility and dependencies in EUA and energy prices.
Oil prices affect Russian banks' stability, with negative impacts from decreases.
Deep Q-Learning system for straddle options in volatile markets.
The growing conflicts in and about oil exporting regions and speculations about volatile oil prices during the last decade have renewed the public interest in predictions for the near future oil production and consumption. Unfortunately, studies from only 10 years ago, which tried to forecast the oil production during …
In this article we present a continuous time model for natural gas and crude oil future prices. Its main feature is the possibility to link both energies in the long term and in the short term. For each energy, the future returns are represented as the sum of volatility functions driven by motions. Under the risk neutr…
Study examines value relevance of oil and gas reserve disclosures in London Stock Exchange.
This paper examines the short-run relationships between oil prices and GCC stock markets. Since GCC countries are major world energy market players, their stock markets may be susceptible to oil price shocks. To account for the fact that stock markets may respond nonlinearly to oil price shocks, we have examined both l…
Study examines oil and US stock market interactions during coronavirus crisis.
The dissertation investigates the application of Probabilistic Graphical Models (PGMs) in forecasting the price of Crude Oil. This research is important because crude oil plays a very pivotal role in the global economy hence is a very critical macroeconomic indicator of the industrial growth. Given the vast amount of m…
Methods for detecting structural changes, or change points, in time series data are widely used in many fields of science and engineering. This chapter sketches some basic methods for the analysis of structural changes in time series data. The exposition is confined to retrospective methods for univariate time series. …
We propose a comprehensive treatment of the leverage effect, i.e. the relationship between returns and volatility of a specific asset, focusing on energy commodities futures, namely Brent and WTI crude oils, natural gas and heating oil. After estimating the volatility process without assuming any specific form of its b…
Study shows oil prices but not COVID-19 cases affect US economic policy uncertainty.
Methodology that recently lead us to predict to an amazing accuracy the date (July 11, 2008) of reverse of the oil price up trend is briefly summarized and some further aspects of the related oil price dynamics elaborated. This methodology is based on the concept of discrete scale invariance whose finance-prediction-or…
A phase plot of the oil economy is built using the literature data of world oil production, price, and EROEI (Energy Returned on Energy Invested). An analogy between the oil economy and the Benard convection is proposed; some methods of interpretation and forecast of the system behavior are also shown based on "phase p…
Hybrid approach improves crude oil price forecasting using multi-scale data.
The paper presents a method for detecting jump sizes in crude oil prices.
Crude oil is a major component in most advanced economies of the world. Accurately predicting and understanding the behavior of crude oil prices is important for economists, analysts, forecasters, and traders, to name a few. The price of crude oil has declined in the past decade and is seeing a phase of stability; but …
The study finds a long-term relationship between Dubai crude oil and US natural gas prices.
Investment risk on a regulated market is influenced by gold prices and oil trading.
Study shows adding correlated features doesn't improve LSTM model interpretability for oil stocks.
We analyze total, asymmetric and frequency connectedness between oil and forex markets using high-frequency, intra-day data over the period 2007 -- 2017. By employing variance decompositions and their spectral representation in combination with realized semivariances to account for asymmetric and frequency connectednes…
The paper contributes to the rare literature modeling term structure of crude oil markets. We explain term structure of crude oil prices using dynamic Nelson-Siegel model, and propose to forecast them with the generalized regression framework based on neural networks. The newly proposed framework is empirically tested …
This paper investigates how the conditional quantiles of future returns and volatility of financial assets vary with various measures of ex-post variation in asset prices as well as option-implied volatility. We work in the flexible quantile regression framework and rely on recently developed model-free measures of int…
Research uses SWT and BDLSTM to forecast stock and oil prices amid COVID-19.
In this paper we present a new method to compute the first-order approximation of the price of derivatives on futures in the context of multiscale stochastic volatility of Fouque \textit{et al.} (2011, CUP). It provides an alternative method to the singular perturbation technique presented in Hikspoors and Jaimungal (2…
Study uses BNs to predict cryptocurrency prices, improving accuracy with discretisation.
Wavelet analysis reveals financialization effects on oil-food price correlation.