SpotV2Net forecasts intraday spot volatilities using graph attention networks.
problem Forecasting multivariate intraday spot volatilities accurately.
method Graph Attention Network architecture with Fourier estimates of spot and vol-of-vol volatilities.
result SpotV2Net outperforms other models in forecasting accuracy.
Research forecasts electricity spot prices using stochastic volatility models.
problem Forecasting day-ahead electricity prices in a spot market.
method Exploring and enriching a baseline stochastic volatility model with exogenous regressors.
result A better fitting model confirmed by out-of-sample forecasts.
Enhanced volatility forecasting using options data and rough volatility model.
problem Improving realized volatility forecasting accuracy.
method Infer spot volatility from options data using rough stochastic volatility model, accelerate estimation with deep learning, benchmark against traditional models.
result Augmented HAR-RV-RHeston model outperforms traditional models in daily and long-term forecasting.
Study examines asymmetry impacts on Japanese stock market volatility modeling and forecasting.
problem Understanding asymmetry's impact on modeling and forecasting realized volatility in Japanese stock markets.
method Employed heterogeneous autoregressive (HAR) models with three types of asymmetry: positive and negative realized semivariance, asymmetric jumps, and leverage effects.
result Leverage effects significantly influence realized volatility modeling and forecast performance in Japanese stock markets.
Study compares volatility models for Bitcoin, finds GARCH and EGARCH outperform.
problem Evaluating which volatility models best predict Bitcoin spot and option prices.
method Used HIST, EMA ARCH, GARCH, and EGARCH models on Bitcoin spot price series.
result GARCH and EGARCH models outperform other models in both in-sample and out-of-sample forecasts.
Currency volatility shocks predict lower excess returns, and buying weak transmitters outperforms selling strong ones.
problem Predicting currency returns using volatility shocks.
method Constructed a dynamic, directed network of volatility connections using option-implied volatilities.
result Currencies that transmit more volatility shocks earn lower excess returns.
Hybrid models forecast EPEC energy spot prices.
problem Forecasting energy spot prices in EPEC markets.
method Combining Naive, Fourier, ARMA/GARCH, mean-reversion, jump-diffusion, and RNN models.
result Improved accuracy in forecasting compared to individual models.
A new model adds stochastic spot/volatility correlation to Heston model for better exotic pricing.
problem Improving exotic option pricing in foreign exchange markets.
method Developed a Double Heston model with stochastic spot/volatility correlation, an affine model.
result The new model increases prices of out-of-the-money knockout options and one touch options.
TGARCH model shows CSI-300 futures reduce spot price volatility.
problem Impact of CSI-300 futures trading on spot price volatility.
method TGARCH model applied to CSI-300 index data.
result CSI-300 futures trading significantly reduces spot price volatility.
New method for fast volatility estimation robust to change points.
problem Robust high-frequency volatility estimation with change points.
method ℓ1-regularized power variation estimators using LARS for sparse estimation and dynamic programming for change point refinement.
result Minimax rates achieved for volatility estimators, providing accurate and smooth forecasts.
Most models for barrier pricing are designed to let a market maker tune the model-implied covariance between moves in the asset spot price and moves in the implied volatility skew. This is often implemented with a local volatility/stochastic volatility mixture model, where the mixture parameter tunes that covariance. T…
Estimates volatility of volatility and leverage effect using high-frequency options data.
problem Estimating volatility of volatility and leverage effect from high-frequency options data.
method Model-free estimators using characteristic function of price increments and spot volatility.
result Developed feasible inference methods for estimating volatility of volatility and leverage effect.
The paper proves the consistency and efficiency of a volatility estimator in noisy data.
problem Proving the consistency and efficiency of a volatility estimator in the presence of microstructure noise.
method Proves asymptotic normality using Central Limit Theorem for Fourier spot volatility estimator.
result Proves consistency and asymptotic efficiency of the Fourier spot volatility estimator in noisy data.
New method for spot volatility estimation with reduced microstructure noise.
problem Estimating spot volatility from noisy high-frequency data.
method Pre-averaging/kernel estimator to handle microstructure noise.
result Optimal bandwidth selection and kernel functions for minimal variance.
Study Fourier estimator for spot volatility with unbounded coefficients and jumps.
problem Estimating spot volatility with unbounded coefficients and jumps in price process.
method Fourier estimator for spot volatility, convergence analysis for unbounded coefficients and jumps.
result Convergence of trigonometric polynomial to volatility's path, almost sure convergence of reconstructed volatility.
We investigate the joint dynamics of spot and implied volatility from an empirical perspective. We focus on the equity market with the SPX Index our underlying of choice. Using only observable quantities, we extract the instantaneous variance curves implied by the market and study their daily variations jointly with sp…
This paper introduces the class of volatility modulated Lévy-driven Volterra (VMLV) processes and their important subclass of Lévy semistationary (LSS) processes as a new framework for modelling energy spot prices. The main modelling idea consists of four principles: First, deseasonalised spot prices can be modelled di…
This paper develops a Bayesian procedure for estimation and forecasting of the volatility of multivariate time series. The foundation of this work is the matrix-variate dynamic linear model, for the volatility of which we adopt a multiplicative stochastic evolution, using Wishart and singular multivariate beta distribu…
Empirical study finds variance swap rate is affine in spot variance for S&P500 data.
problem Investigating the relationship between variance swap rate and spot variance.
method Empirical analysis using S&P500 data from 2006-2018, testing different models.
result Affine relationship between variance swap rate and spot variance is supported.
Study on estimating volatility of volatility using Fourier methods and provides insights into volatility dynamics.
problem Estimating the volatility of volatility (vol-of-vol) accurately and efficiently.
method Used Fourier methodology to estimate integrated volatility of volatility, bias-corrected and without bias-correction, comparing their asymptotic properties and accuracy.
result The bias-corrected estimator reaches the optimal rate n1/4, while the uncorrected estimator has a slower rate and smaller asymptotic variance. This paper employs machine learning algorithms to forecast German electricity spot market prices. The forecasts utilize in particular bid and ask order book data from the spot market but also fundamental market data like renewable infeed and expected demand. Appropriate feature extraction for the order book data is dev…
It has been recently shown that spot volatilities can be very well modeled by rough stochastic volatility type dynamics. In such models, the log-volatility follows a fractional Brownian motion with Hurst parameter smaller than 1/2. This result has been established using high frequency volatility estimations from histor…
We describe a model for evolving commodity forward prices that incorporates three important dynamics which appear in many commodity markets: mean reversion in spot prices and the resulting Samuelson effect on volatility term structure, decorrelation of moves in different points on the forward curve, and implied volatil…
This paper presents a model based on multilayer feedforward neural network to forecast crude oil spot price direction in the short-term, up to three days ahead. A great deal of attention was paid on finding the optimal ANN model structure. In addition, several methods of data pre-processing were tested. Our approach is…
It is well known that in models with time-homogeneous local volatility functions and constant interest and dividend rates, the European Put prices are transformed into European Call prices by the simultaneous exchanges of the interest and dividend rates and of the strike and spot price of the underlying. This paper inv…
Forecast-to-fill strategy generates durable alpha in gold futures.
problem Generating alpha in gold futures using simple state variables.
method Rolling 10-year training, 6-month testing; fractional Kelly sizing; ATR-based exits.
result 43% annualized return, 37% alpha, Sharpe ratio of 2.88.
The study examines volatility models and finds decoupling of short- and long-term correlation structures.
problem Understanding the dynamic of volatility at different time scales.
method Developed a composite likelihood estimation framework for parametric continuous-time stationary Gaussian processes.
result The short- and long-term correlation structures of stochastic volatility are decoupled.
The paper models intraday power prices using fundamental drivers.
problem Lack of research on drivers for intraday price processes.
method Modelling location, shape, and scale of intraday price distribution using fundamental variables.
result Significant improvements in probabilistic forecasting performance, especially in tails.
We prove that if the Black-Scholes formula holds with the spot volatility for call options with all strikes, then the volatility parameter is constant. The proof relies some result on semimartingales (Theorem 2) of independent interest.
Kernel Estimation is one of the most widely used estimation methods in non-parametric Statistics, having a wide-range of applications, including spot volatility estimation of stochastic processes. The selection of bandwidth and kernel function is of great importance, especially for the finite sample settings commonly e…
In order to study the geometry of interest rates market dynamics, Malliavin, Mancino and Recchioni [A non-parametric calibration of the HJM geometry: an application of Itô calculus to financial statistics, {\it Japanese Journal of Mathematics}, 2, pp.55--77, 2007] introduced a scheme, which is based on the Fourier Seri…
Dual model predicts electricity spot prices using neural networks and wavelets.
problem Forecasting hourly electricity spot prices.
method Dual generalized long memory modelling with k-factor GARMA and G-GARCH models, using LLWNN and PSO for variance prediction.
result The hybrid k-factor GARMA-LLWNN model outperforms other methods in forecasting accuracy.
Paper improves volatility estimation using a Queue-Reactive model.
problem Volatility estimation from high-frequency data is biased by microstructure noise.
method Uses Queue-Reactive model of limit order book to improve volatility estimation.
result Unified and alternation estimators lead to optimal mean squared error for integrated volatility.
Paper develops new spot regression estimators using candlesticks for asset pricing.
problem Estimation of spot betas in asset pricing and risk management.
method Develops a new estimation and inference framework for spot regressions using high-frequency candlesticks.
result The proposed candlestick-based estimators reduce estimation risk and achieve higher power in hypothesis testing.
For a commodity spot price dynamics given by an Ornstein-Uhlenbeck process with Barndorff-Nielsen and Shephard stochastic volatility, we price forwards using a class of pricing measures that simultaneously allow for change of level and speed in the mean reversion of both the price and the volatility. The risk premium i…
We derive a higher-order expansion for rough volatility models.
problem Characterizing and estimating rough volatility models.
method Higher-order asymptotic expansion of characteristic functions.
result Distinct roles of rough and jump dynamics in volatility.
By Gyongy's theorem, a local and stochastic volatility (LSV) model is calibrated to the market prices of all European call options with positive maturities and strikes if its local volatility function is equal to the ratio of the Dupire local volatility function over the root conditional mean square of the stochastic v…
TSFMs outperform traditional models in electricity price forecasting.
problem Accurate electricity price forecasting for effective decision-making.
method Benchmarking several TSFMs against traditional models using real-world data.
result MSTL model consistently outperforms TSFMs across countries and metrics.
Model forecasts global stock market volatility using dynamic graphs and all trading days.
problem Enhance forecasting accuracy and practical utility in global stock market volatility.
method Spatial-temporal graph neural network architecture to capture volatility spillover effect.
result Forecasting performance surpasses baseline models in all scenarios.
Framework improves ETF volatility forecasting by adapting to market conditions.
problem Challenges in volatility forecasting due to shifting market conditions and varying model performance.
method Risk-sensitive specialist routing using online risk-sensitive evaluation and state-dependent gating.
result Reduces forecast loss by 24% and underprediction loss by 22% compared to rolling-best baseline.
We consider a class of stochastic path-dependent volatility models where the stochastic volatility, whose square follows the Cox-Ingersoll-Ross model, is multiplied by a (leverage) function of the spot price, its running maximum, and time. We propose a Monte Carlo simulation scheme which combines a log-Euler scheme for…
The study forecasts portfolio volatility using cointegrated asset dynamics.
problem Forecasting volatility in portfolios with high accuracy.
method Developed HVR/DVR ratios and used Vector Error Correction Model (VECM) to forecast volatility.
result VECM forecasts of portfolio volatility have lower MAPE than covariance-based forecasts.
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 …
New framework improves option pricing models by addressing volatility dynamics.
problem Challenges in standard option pricing models, especially in deriving implied volatility.
method Developed a new framework called Implied Remaining Variance (IRV), identifying minimal conditions for absence of arbitrage.
result Reformulated results of Schweizer and Wissel (2008b) and independently derived El Amrani, Jacquier and Martini (2021) results within IRV framework.
Bayesian model improves asset price forecasting using realized volatility.
problem Improving asset price forecasting accuracy.
method Integrates dynamic gamma process with DLMs for price and realized volatility.
result Significant improvements in asset price forecasting compared to standard models.
New model forecasts stock market volatility better than existing methods.
problem Forecasting volatility in stock markets.
method Combines HAR model with path-dependent volatility models.
result HAR-PD model family outperforms basic HAR model family in volatility forecasting.
Enhanced GARCH model uses autoencoder for volatility forecasting.
problem Selecting optimal realised volatility estimator for forecasting.
method Proposes an autoencoder-enhanced Realised GARCH model combining multiple realised measures.
result The model outperforms traditional linear methods in one-step-ahead rolling volatility forecasting.
In this paper we present a regression based model for day-ahead electricity spot prices. We estimate the considered linear regression model by the lasso estimation method. The lasso approach allows for many possible parameters in the model, but also shrinks and sparsifies the parameters automatically to avoid overfitti…