Research

Publications:

Identifying Risk Factors and Their Premia: A Study on Electricity (with Asger Lunde), forthcoming at Journal of Financial Econometrics

Risk premia are difficult to identify in nonstorable commodities such as electricity. In this paper, we propose a modified Fama-French regression framework and show that when the spot prices do not follow a martingale—a common assumption in the electricity market—model specifications play an important role in detecting time-varying risk premia in the futures market. With this insight, we propose a multi-factor model that captures important dynamics in electricity prices and an estimation method based on particle Markov chain Monte Carlo (PMCMC) to separate risk factors in energy prices. Using spot and futures data in the Germany/Austria electricity market, we demonstrate that our proposed model surpasses alternative models that ignore some of risk factors in forecasting spot prices and in detecting time-varying risk premia. Based on our proposed model, we separately identify risk premia carried by individual risk factors and document large variations in the premia of each factor.

Generic Improvements to Least Squares Monte Carlo Methods with Applications to Optimal Stopping Problems (with Dan Zhu), European Journal of Operational Research, 2022

The least squares Monte Carlo method is a standard tool for solving optimal stopping problems. Nonetheless, its performance is subject to the choice of regressors and is often unsatisfactory in the presence of nonlinearity in high-dimensional settings. These two issues are generally present in optimal stopping problems in practice. This paper provides two generic improvements to the least squares Monte Carlo method to address these issues. The first approach employs model averaging to alleviate the dependence on the choice of approximation model, and the other formulates a single-index regression that preserves nonlinearity in high-dimensional settings. We illustrate the efficacy of the proposed methods compared with existing ones on a wide range of stopping problems. The techniques introduced are generally applicable in any scenario where the least squares Monte Carlo method is viable with a negligible increase in computational cost.

Bagging Weak Predictors (with Eric Hillebrand and Manuel Lukas), International Journal of Forecasting, 2021

Often, relations between economic variables cannot be exploited for forecasting, suggesting that predictors are weak in the sense that the estimation uncertainty is larger than the bias from ignoring the relation. In this paper, we propose a novel bagging estimator designed for such predictors. Based on a test for finite-sample predictive ability, our estimator shrinks the ordinary least squares estimate—not to zero, but towards the null of the test that equates squared bias with estimation variance. We apply bagging to reduce the estimation variance further. We derive the asymptotic distribution and show that our estimator substantially lowers the mean-squared error compared to standard t-test bagging. An asymptotic shrinkage representation for the estimator that simplifies the computation is provided. Monte Carlo simulations showed that the predictor works well with small samples. Empirically, we found that our proposed estimator worked well for inflation forecasting when using unemployment or industrial production as predictors. In an application for predicting equity premiums, the combination of our estimator and a positive constraint on forecasts delivered statistically significant gains relative to the historical average using a wide range of predictors.

The Effect of Human Mobility Restrictions on the COVID-19 Transmission Network in China (with Tatsushi Oka and Dan Zhu), Plos One, 2021

A Generalized Schwartz Model for Energy Spot Prices-Estimation using a Particle MCMC Method (with Asger Lunde), Energy Economics, 72, 2018, pp. 560-582.

We investigate a large set of energy models that account for the stylized properties in energy prices, especially stochastic volatility and spikes. The models under consideration belong to the class of factor models while our full model features a two-factor price process and a two-component stochastic volatility process. The first factor in the price process captures the normal variations; the second accounts for spikes. The two-component volatility allows for a flexible autocorrelation structure. Instead of using various filtering techniques for splitting the factors, as often found in the literature, we estimate the model in one step using the particle MCMC method. We fit the models to both the spot market and the forward market for UK natural gas. We find that the inclusion of stochastic volatility is crucial for the statistical fit of spot prices whereas the spikes are important for explaining forward prices.

The Geometric-VaR Backtesting Method (with Denis Pelletier), Journal of Financial Econometrics, 14 (4), 2016, pp. 725-745.

This article develops a new test to evaluate value-at-risk (VaR) forecasts. VaR is a standard risk measure widely utilized by financial institutions and regulators, yet estimating VaR is a challenging problem, and popular VaR forecast relies on unrealistic assumptions. Hence, assessing the performance of VaR is of great importance. We propose the geometric-VaR test which utilizes the duration between the violations of VaR as well as the value of VaR. We conduct a Monte Carlo study based on desk-level data and we find that our test has high power against various alternatives.

Comments on “Reflections on the Probability Space Induced by Moment Conditions with Implications for Bayesian Inference" (with Asger Lunde), Journal of Financial Econometrics, 14 (2), 2016, pp. 278-283.

Working papers:

Estimating the Effect of an EU-ETS Type Scheme in Australia Using a Synthetic Treatment Approach (with Heather Anderson, Jiti Gao, Guido Turnip and Farshid Vahid)

The 2011 Clean Energy Act sought to align Australia’s carbon pricing to the 2005 European Union Emission Trading Scheme (EU-ETS) by 2015, but this act was repealed in 2014. We estimate the hypothetical impact of Australia adopting an emissions trading policy in 2005, which corresponds with the establishment of the EU-ETS. We use a synthetic treatment approach that constructs a counter- factual measure of Australian carbon emissions that makes use of the time series properties of pre-2005 and post-2005 emissions in European countries. We find that Australian per-capita carbon emissions would have been lower only by about 4.5% as a result of the policy - a result that is robust to several variations of our methodology.

A Stochastic Price Duration Model for Estimating High-Frequency Volatility (with Denis Pelletier).

We propose a class of stochastic price duration models to estimate high-frequency volatility. A price duration measures how long it takes for the asset price to change by a given amount. It is directly linked to volatility from the passage time theory for Brownian motions. Modeling with price durations renders more effecient sampling scheme compared to return-based estimators. Also, our parametric approach allows us to estimate intraday spot volatility and incorporate additional information such as trade durations.