Per Bjarte Solibakke

SCIENTIFIC STOCHASTIC VOLATILITY MODELS FOR THE SALMON FORWARD MARKET: FORECASTING (UN-)CONDITIONAL MOMENTS

This article applies the General Scientific Model methodology of Gallant and McCulloch implementing MCMC simulation methodologies to build a multifactor stochastic volatility model for the mean and latent volatility for the Fish Pool front month salmon market. Stochastic volatility is the main way time-varying volatility is modeled in financial markets. Our main objective is therefore to structure a scientific model specifying volatility as having its own stochastic process. Appropriate model descriptions broaden the applications into derivative pricing purposes, risk assessment and asset allocation. The article reports risk and portfolio measures, conditional one-step-ahead moments, particle filtering for one-step-ahead conditional volatility, conditional variance functions for evaluation of shocks, analysis of multi-step-ahead dynamics, and conditional persistence. The analysis adds market insight and enables forecasts to be made, thus building up methodologies for developing valid scientific models for commodity market applications.

Modelling day ahead Nord Pool forward price volatility: Realized volatility versus GARCH models

Traditionally, and still within electricity futures/forward markets, daily data has been utilized as the unit of analyses when modelling and making predictions of volatility. However, over the recent past it is argued that better volatility estimates can be obtained by using standard time series techniques on non-parametric volatility measures constructed from high-frequency intradaily returns. Liquidity in financial electricity markets has increased rapidly over the recent years, which make it possible to apply these relatively new methods for measuring market volatility. In this paper high-frequency data and the concept of realized volatility is utilized to make day ahead predictions of Nord Pool forward price volatility. Such short term volatility predictions are especially important for operators and other participants in the electricity sector. We compare the results obtained from standard time-series techniques with the more traditional GARCH-framework which utilizes daily returns. Additionally, we examine whether different approaches of decomposing the total variation into a continuous — and jump measure improves the model fit or not. The paper provides new insights to how the financial electricity market at Nord Pool works, and how we efficiently can model and make predictions of the price movements in this market.

Calculating abnormal returns in event studies: controlling for non‐synchronous trading and volatility clustering in thinly traded markets

Reviews previous research based on event study methodology, pointing out that events can influence returns in many ways, and applies the method to a sample of mergers and acquisitions in the thinly traded Norwegian market 1983‐1994. Explains how the classic market model can be adjusted to control for non‐synchronous trading and changing/asymmetric volatility; and how the event and non‐event periods can be combined into a single model. Applies two different models to the data, compares the results and finds the ARMA‐GARCH approach superior to the OLS. Discusses the implications of this for researchers.

A stochastic volatility model specification with diagnostics for thinly traded equity markets

Abstract The majority of world equity markets exhibit non-synchronous- and non-trading for some quoted asset series. This investigation sets out to determine the complexity of illiquid markets applying versions of stochastic differential equation (SDE) specifications. Efficient method of moments (EMM) is used to estimate and evaluate the diffusion models. EMM estimation on SNP scores reveals that a standard SDE with first and second order drift, non-synchronous trading and constant diffusion are rejected. However, a simple two-factor stochastic volatility specification with non-synchronous trading and conditional heteroscedasticity, reports success for illiquid equity markets. Tracking portfolios may therefore not track derivative products perfectly and importantly; the stochastic volatility model seems to be the preferred volatility specification.

The Nordic/Baltic Spot Electric Power System Price: Univariate Nonlinear Impulse-Response Analysis

This paper revisits the conditional mean and volatility density characteristics of the system price settled by the Nordic/Baltic spot electric power market (1993–2017). The main aim of this paper is an analysis of the nonlinear impulse-response features (shocks) in the nonstorable commodity market. Initially, we extract all deterministic seasonality and nonstationary trend and scale features from the series. A strictly stationary model reports serial correlation for the mean, and clustering, asymmetry and level effects for the volatility. For the mean, the impulse-response analysis reports linear and symmetric mean reversion for any price movements. For the volatility, small price movements show symmetric and decreasing volatility. In contrast, for larger absolute price movements, the volatility shows a nonlinear increase as well as fast-growing negative asymmetries. The impulse persistence is therefore relatively short. With the entrance of renewables into the energy market, the subperiod 2008–17 reports major systematic changes in the mean, volatility, asymmetry and persistence. In fact, the renewables era has changed the fundamental features of the Nordic/Baltic electricity market.

Derivation of econometric estimable functions of intra-trade industry: the case of the Norwegian intra-continental import trade pattern

This paper evaluates the item-based Norwegian intra-continental trade pattern. The paper derives the best linear unbiased estimator (BLUE) of estimable functions of the two-stage non-full rank hierarchical linear econometric model for the analysis of the intra-continental variations of the yearly Norwegian import expenditures for the period 1988-2014. The result confirms that the intra-Europe, item-based trade pattern of the Norwegian import trade is characterised as very stable, standardised and predictable. However, most of the items of import from the other continents show lack of stability and predictability. The result implies that efforts by governments (or firms) for trade stability have little effects.

Intercontinental variations of the import trade pattern of Norway: applications to best linear unbiased estimable functions of hierarchical econometric model

This papers main purpose is an analysis of the intercontinental variations of Norwegian import expenditures based on yearly import data from 1988 to 2014. We apply the best linear unbiased estimable (BLUE) functions of the two-stage non-full rank hierarchical linear econometric model. The results confirm that the top three import-items across continents (in descending order) are machinery and transport equipment, manufactured goods classified mainly by material, and miscellaneous manufactured articles. These three import-items cover more than 60% of the Norwegian imports. Furthermore, the model predicts that Europe is the leading continent of these three important items of Norwegian imports. The European continent is therefore influential for the Norwegian trade pattern, while other continents show lack of stability and predictability. The results imply that any governmental (or private) trade stability programs have only marginal effects.

Stochastic Volatility Models for the Brent Oil Futures Market: Forecasting and Extracting Conditional Moments

This paper builds and implements a multifactor stochastic volatility model for the latent (and observable) volatility from the front month future contracts at the Intercontinental Commodity Exchange (ICE), London, applying Bayesian Markov chain Monte Carlo simulation methodologies for estimation, inference and model adequacy assessment. Stochastic volatility is the main way time�?varying volatility is modelled in financial markets. An appropriate scientific model description, specifying volatility as having its own stochastic process, broadens the applications into derivative pricing purposes, risk assessment and asset allocation and portfolio management. From an estimated optimal and appropriate stochastic volatility model, the paper reports risk and portfolio measures, extracts conditional one�?step�?ahead moments (smoothing), forecasts one�?step�?ahead conditional volatility (filtering), evaluates shocks from conditional variance functions, analyses multistep�?ahead dynamics and calculates conditional persistence measures. (Exotic) option prices can be calculated using the re�?projected conditional volatility. Observed market prices and implied volatilities establish market risk premiums. The analysis adds insight and enables forecasts to be made, building up the methodology for developing valid scientific commodity market models.

Econometric Modelling of the Variations of Norway’s Export Trade across Continents and over Time: The Two-Stage Non-Full Rank Hierarchical Linear Econometric Model Approach

This paper applies the two-stage hierarchical non-full rank linear econometric model to make a deep analysis based on revenue generated from key Norwegian export items over the world’s continents. The model’s ability to analyse the variation of Norway’s export trade gives us the following interesting details: (1) for each continent intra- and intervariation of export items, (2) access to deep knowledge about the characteristics of the Norway’s export items revenue, (3) quantifying the economic importance and sustainability of export items within continents; and finally (4) comparing a given export item economic importance across continents. The results suggest the following important policy implications for Norway. First, Europe is the most important trade partner for Norway. In fact, 81.5% of Norwegian export items are transported to Europe. Second, there is a structural shift in Norwegian exports from North and Central America to Asia and Oceania. Third, the new importance of Asia and Oceania is also emphasized by the 85% increase in export revenues over the period 1988–2012. The trade pattern has changed and trade policy must change accordingly. The analysis has shown that in 2012 there are two important export continents for Norway: Europe and Asia and Oceania.

Covariance estimation using high-frequency data: Analysis of Nord Pool electricity forward data

Volatility and correlation modelling is important in order to calculate hedge ratios, value at risk estimates, CAPM betas, derivate pricing and for risk management in general. Historically, these measures have usually been obtained by analyzing daily data. Recently access to intra-daily high-frequency data for two of the most liquid contracts at the Nord Pool exchange (quarterly and yearly forward contracts), makes it possible to apply new and promising methods for analyzing volatility and correlation. We apply the concept of realized volatility and realized correlation, and as the first study statistically describe the distribution (both distributional properties and temporal dependencies) of electricity forward data from 2005 to 2009. The overall main findings show that the logarithmic realized volatility are approximately normal distributed, while realized correlation seems not. Further, realized volatility and realized correlation has a long memory feature, and there seem to be a high correlation between realized correlation and volatilities. These results are to a large extent consistent with earlier stylized facts studies of other financial and commodity markets.