RISK MANAGEMENT WITH TAIL COPULAS FOR EMERGING MARKET PORTFOLIOS

We study the tail dependence of emerging markets in South-East Asia and we show that this tail dependence increased during the financial crisis of 2008-2010. After applying ARMA-GARCH models to individual markets, we fit various copulas to the pairs of market returns and find that in most cases tail copulas such as the t-copula and Symmetrised Joe-Clayton provide the best fit. During the crisis, nonlinear dependence measures (such as rank correlations) and the tail dependence coefficients typically increased by tenfold or even more. We apply our method to portfolio Value-at-Risk estimation and show that the copula-based Value-at-Risk performs remarkably well for South-East Asian market portfolios

Three-factor commodity forward curve model and its joint P and Q dynamics

In this paper, we propose a new framework for modeling commodity forward curves. The proposed model describes the dynamics of fundamental driving factors simultaneously under physical (P) and risk-neutral (Q) probability measures. Our model is an extension of the forward curve model by Borovkova and Geman (2007), into several directions. It is a three-factor model, incorporating the synthetic spot price, based on liquidly traded futures, stochastic level of mean reversion and an analog of the stochastic convenience yield. We develop an innovative calibration mechanism based on the Kalman filtering technique and apply it to a large set of Brent oil futures. Additionally, we investigate properties of the time-dependent market price of risk in oil markets. We apply the proposed modeling framework to derivatives pricing, risk management and counterparty credit risk. Finally, we outline a way of adjusting the proposed model to account for negative oil futures prices observed recently due to coronavirus pandemic

An ensemble of LSTM neural networks for high-frequency stock market classification

We propose an ensemble of long–short-term memory (LSTM) neural networks for intraday stock predictions, using a large variety of technical analysis indicators as network inputs. The proposed ensemble operates in an online way, weighting the individual models proportionally to their recent performance, which allows us to deal with possible nonstationarities in an innovative way. The performance of the models is measured by area under the curve of the receiver operating characteristic. We evaluate the predictive power of our model on several US large-cap stocks and benchmark it against lasso and ridge logistic classifiers. The proposed model is found to perform better than the benchmark models or equally weighted ensembles

Analysis and Modelling of Electricity Futures Prices

We model electricity futures prices using a seasonal forward curve model, quantifying seasonalities by a deterministic seasonal forward premium. Stochastic features of the futures prices are contained in the stochastic forward premium: a quantity analogous to the well-known convenience yield. The model parameters are estimated from the historical data of IPE electricity futures prices and the spark spread, and electricity forward curves are deseasonalized to reveal their underlying stochastic structure. We apply principal component analysis to the deseasonalized forward curves and develop trading strategies using indicators based on these principal components.

Electricity price modeling with stochastic time change

Borovkova S, Schmeck MD. Electricity price modeling with stochastic time change. ENERGY ECONOMICS. 2017;63:51-65.In this paper, we develop a novel approach to electricity price modeling, based on the powerful technique of stochastic time change. This technique allows us to incorporate the characteristic features of electricity prices (such as seasonal volatility, time varying mean reversion and seasonally occurring price spikes) into the model in an elegant and economically justifiable way. The stochastic time change introduces stochastic as well as deterministic (e.g., seasonal) features in the price process' volatility and in the jump component. We specify the base process as a mean reverting jump diffusion and the time change as an absolutely continuous stochastic process with seasonal component. The activity rate of the stochastic time change can be related to the factors that influence supply and demand. Here we use the temperature as a proxy for the demand and hence, as the driving factor of the stochastic time change, and show that this choice leads to realistic price paths. We derive properties of the resulting price process and develop the model calibration procedure. We calibrate the model to the historical EEX power prices and apply it to generating realistic price paths by Monte Carlo simulations. We show that the simulated price process matches the distributional characteristics of the observed electricity prices in periods of both high and low demand. (C) 2017 The Authors. Published by Elsevier B.V

Limit theorems for functionals of mixing processes with applications to U-statistics and dimension estimation

In this paper we develop a general approach for investigating the asymptotic distribution of functional Xn = f((Zn+k)k∈z) of absolutely regular stochastic processes (Zn)n∈z. Such functional occur naturally as orbits of chaotic dynamical systems, and thus our results can be used to study probabilistic aspects of dynamical systems. We first prove some moment inequalities that are analogous to those for mixing sequences. With their help, several limit theorems can be proved in a rather straightforward manner. We illustrate this by re-proving a central limit theorem of Ibragimov and Linnik. Then we apply our techniques to U-statistics Matrix Equation with symmetric kernel h : R × R → R. We prove a law of large numbers, extending results of Aaronson, Burton, Dehling, Gilat, Hill and Weiss for absolutely regular processes. We also prove a central limit theorem under a different set of conditions than the known results of Denker and Keller. As our main application, we establish an invariance principle for U-processes (Un(h))h, indexed by some class of functions. We finally apply these results to study the asymptotic distribution of estimators of the fractal dimension of the attractor of a dynamical system