126 research outputs found

    Multi-Step Forecast of the Implied Volatility Surface Using Deep Learning

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    Implied volatility is an essential input to price an option. Machine learning architectures have shown strengths in learning option pricing formulas and estimating implied volatility cross-sectionally. However, implied volatility time series forecasting is typically done using the univariate time series and often for short intervals. When a univariate implied volatility series is forecasted, important implied volatility properties such as volatility skew and the term structure are lost. More importantly, short term forecasts can’t take advantage of the long term persistence in the volatility series. The thesis attempts to bridge the gap between machine learning-based implied volatility modeling and multivariate multi-step implied volatility forecasting. The thesis contributes to the literature by modeling the entire implied volatility surface (IVS) using recurrent neural network architectures. I implement Convolutional Long Short Term Memory Neural Network (ConvLSTM) to produce multivariate and multi-step forecasts of the S&P 500 implied volatility surface. The ConvLSTM model is capable of understanding the spatiotemporal relationships between strikes and maturities (term structure), and of modeling volatility surface dynamics non-parametrically. I benchmark the ConvLSTM model against traditional multivariate time series Vector autoregression (VAR), Vector Error Correction (VEC) model, and deep learning-based Long-Short-Term Memory (LSTM) neural network. I find that the ConvLSTM significantly outperforms traditional time series models, as well as the benchmark Long Short Term Memory(LSTM) model in predicting the implied volatility surface for a 1-day, 30-day, and 90-day horizon, for out-of-the-money and at-the-money calls and puts

    The impact of macroeconomic leading indicators on inventory management

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    Forecasting tactical sales is important for long term decisions such as procurement and informing lower level inventory management decisions. Macroeconomic indicators have been shown to improve the forecast accuracy at tactical level, as these indicators can provide early warnings of changing markets while at the same time tactical sales are sufficiently aggregated to facilitate the identification of useful leading indicators. Past research has shown that we can achieve significant gains by incorporating such information. However, at lower levels, that inventory decisions are taken, this is often not feasible due to the level of noise in the data. To take advantage of macroeconomic leading indicators at this level we need to translate the tactical forecasts into operational level ones. In this research we investigate how to best assimilate top level forecasts that incorporate such exogenous information with bottom level (at Stock Keeping Unit level) extrapolative forecasts. The aim is to demonstrate whether incorporating these variables has a positive impact on bottom level planning and eventually inventory levels. We construct appropriate hierarchies of sales and use that structure to reconcile the forecasts, and in turn the different available information, across levels. We are interested both at the point forecast and the prediction intervals, as the latter inform safety stock decisions. Therefore the contribution of this research is twofold. We investigate the usefulness of macroeconomic leading indicators for SKU level forecasts and alternative ways to estimate the variance of hierarchically reconciled forecasts. We provide evidence using a real case study

    Styring av kortsiktig prisusikkerhet i laksespotmarkedet

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    The spot price of Norwegian farmed Atlantic salmon is highly volatile and hard to predict. The uncertainty over the future spot price obscures revenue projections of salmon farmers and cost expectations of processors, exporters and retailers. It also makes business financing expensive as the high uncertainty needs to be compensated by a high return on investment. The market participants acknowledge this to be a substantial quandary. This thesis examines the problem and aims to provide feasible solutions for uncertainty management in the salmon market. An introduction and three research papers address the different aspects of the subject, namely, salmon price volatility, price predictability and hedging the spot price with various financial instruments. A variety of econometric and machine learning techniques are applied to account for seasonal patterns and autoregressive conditional heteroskedasticity in the price series and to deliver forecasts of their conditional means and variances. The first paper “Salmon price volatility: a weightclass-specific multivariate approach” presents a statistical description of the conditional mean and variance of the spot prices of seven different weight classes of salmon. It highlights a considerable increase in the unconditional variance around 2006, which coincides with a change in industry regulations and the introduction of a futures exchange for salmon. The conditional mean and variance patterns are found to be similar across the neighbouring weight classes, and the conditional correlations are nearly perfect since 2007. This allows treating the three most popular weight classes of 3-4 kg, 4-5 kg and 5-6 kg as one and makes hedging with salmon futures relatively attractive. The second paper “Short-term salmon price forecasting” is a comprehensive study of forecasting the spot price one to five weeks ahead. It employs three different classes of forecasting models: (1) time series models broadly based on the ARIMA model; (2) artificial neural networks; and (3) a custom model based on the k-nearest neighbours method. Six measures of forecast accuracy and seven tests of forecast optimality and encompassing are reported. The salmon price appears to have a partly predictable seasonal component; however, statistical significance of predictability cannot be established at the available sample size, and the economic value of forecasts is limited. Also, unpredictability beyond seasonality does not offer evidence against weak form efficiency of the salmon spot market. The third paper “Hedging salmon price risk” examines the hedging performance of salmon futures, live cattle futures, soybean meal and oil futures, and the share price of Marine Harvest on the Oslo Stock Exchange. Considerable attention is paid to defining a relevant objective function for a hedger in the salmon market, and a new measure of hedging effectiveness is proposed. Among the candidate hedging instruments and their combinations, the salmon futures contract offers the highest hedging effectiveness; however, low liquidity may limit its applicability in practice. In conclusion, the high uncertainty in the future spot price of salmon has been a constant predicament to the market participants and asks for a practical response. The research results contained in this thesis indicate that attempts of predicting the spot price might not deliver satisfactory results. However, hedging the price risk with salmon futures offers a substantial reduction in uncertainty and could therefore be promoted, provided that the futures contract attracts enough liquidity to meet the demand for hedging. The data used in the thesis is publicly and freely available, and the models are documented in detail; hence, they may be readily employed by the market participants in their business planning and optimization

    Forecasting Korean LNG import price using ARIMAX, VECM, LSTM and hybrid models

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    Department of Management Technology and Innovation ManagementIn this paper, an optimal forecasting model for the South Korean LNG import price was explored by combining an econometric model, a machine learning model, and a hybrid model. The autoregressive moving average model with extrinsic inputs model (ARIMAX) and VECM were the econometric models, and LSTM was the selected machine learning model. ARIMAX-LSTM and VECM-LSTM were used as hybrid models. Various independent variables, such as the Dubai oil price, European gas price, Australian Newcastle coal price, US natural gas price, Japanese liquified natural gas price and system marginal price in Korea were used for forecasting models. As it was proved that granger causality of each independent variables toward South Korean LNG import price is stronger in the order of the Dubai oil price, European gas price, Australian Newcastle coal price, US natural gas price, Japanese liquified natural gas price and SMP, the variables used for forecasting were added one by one in the order of strong granger causality. Optimal lags were derived from VECM analysis for each variable combination and these were used for VECM and LSTM prediction. As a result of forecasting, 6 LSTM models, 4 VECM-LSTM were ranked in the top 10 forecasting models out of the total 90 models. Single econometric models were not included in the list. The best forecasting model was the LSTM with Dubai oil price, European gas price, Australian Newcastle coal price, US natural gas price, and Japanese liquified natural gas price with lag of 6, and its mean absolute percentage error (MAPE) was 3.5209. In addition, because LNG price forecasting is more important when price fluctuation is high, forecasting models were employed for 11 months with high fluctuation among the test periods. Seven hybrid models, one LSTM models, and two ARIMAX models were ranked in the top 10 forecasting models. VECM-LSTM using Dubai oil price with lag of 5 was derived as the best model with a MAPE of 4.9360. As a result of two forecasting analyses for both the whole and high fluctuation periods, we found that LSTM using Dubai oil price, European gas price, Australian Newcastle coal price, US natural gas price, and Japanese liquified natural gas price with a lag of 6 and VECM-LSTM using Dubai oil price, European gas price with a lag of 5 were ranked within the third best for both tests. Of the two models, the VECM-LSTM is in particular considered as the optimal model in that it has both high forecast accuracy and interpretability.ope

    LINEAR AND NON-LINEAR TECHNIQUES FOR ESTIMATING THE MONEY DEMAND FUNCTION: THE CASE OF SAUDI ARABIA

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    This research is intended to apply linear and non-linear techniques to estimate the money demand function of Saudi Arabia under two alternative approaches using two different measures of monetary aggregates (Divisia and Simple-Sum monetary aggregates). The first approach is the conventional way, which is based on empirical literature where non-oil GDP is used as a measure for income. The second approach is the consumer demand approach to money demand. This approach emphasizes the use of variables that are compatible with consumer demand theory, which emphasizes microeconomic and aggregation theory and deals with monetary assets as durable goods which are directly entered as arguments in the household utility function. The thesis first briefly introduces the topic to in which a concise overview of the recent behavior of Saudi Arabia economy and monetary policy is discussed. Moreover to know the core objective of this research the purpose of the study is mentioned. A hypothesis question is developed as an addition to the purpose of the study. Furthermore, in the first chapter, the economy of Saudi Arabia and recent developments are discussed in details after which the financial system is discussed as it is necessary to get basic knowledge of how the financial system of the country first someone is to find out the money demand behavior of a certain country, and it may also be essential the practices of the relevant commerce institutions that how they are engaged in conducting of the monetary policy, thus for this essential requirement the conduct of the monetary policy in Saudi Arabia is discussed after the discussion on the financial system of Saudi Arabia. After this, in the second chapter, the Divisia monetary aggregate technique is discussed in detail. This chapter presents the process of constructing Divisia Monetary Aggregates for Saudi Arabia and compares Simple-sum and Divisia Monetary Aggregates for Saudi Arabia. The third chapter deals with the literature on the theoretical aspects of demand for money in general with different approaches to the demand for money explained vividly. Part of this chapter discusses the salient aspects of money demand in the context of Saudi Arabia. And in the fourth chapter, any hypothesized assumption, suggestion and recommendations are discussed followed by the methodology exploited in the research of this topic. It is also ensured that the methods used in data collection and the research of this thesis are not merged with the methods which are actually used to create links between the linear and non-linear techniques which are used to predict the money demand in Saudi Arabia. And in the last, in chapter five, the analysis of experimental data is discussed so that detailed statistical information could be presented to support the theory discussed in other parts of the research. The analyses and examinations of the long-run and the short-run of the money demand functions for all alternative measures of monetary aggregates show that Divisia aggregates, when compared to their Simple-Sum counterparts, can serve as a potential target in formulating monetary policy in Saudi Arabia. This is explained by the fact that the Divisia aggregates provide a framework for dealing with the effects of financial innovations and also perform better at a high level of aggregation. Since Saudi Arabia is pursuing a policy of financial deregulation, which certainly will raise the competition and financial innovation in the financial industry, the use of Divisia monetary aggregates as policy instruments is suggested

    Forecasting for Nonlinear and Nonstationary Systems Using Intrinsic Functional Decomposition Models

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    The purpose of this study is to develop nonlinear and nonstationary time series forecasting methods to address modeling and prediction of real-world, complex systems. Particular emphasis has been placed on nonlinear and nonstationary time series forecasting in systems and processes that are of interest to IE researchers. Two new advanced prediction methods are developed using nonlinear decomposition techniques and a battery of advanced statistical methods. The research methodologies include empirical mode decomposition (EMD)-based prediction, structural relationship identification (SRI) methodology, and intrinsic time-scale decomposition (ITD)-based prediction. The advantages of using these prediction methods are local characteristic time scales and the use of an adaptive basis that does not require a parametric functional form (during the decomposition process). The utilization of SRI methodology in ITD-based prediction also provides a relationship identification advantage that can be used to capture the interrelationships of variables in the system for prediction application. The empirical results of using these new prediction methods have shown a significant improvement in the accuracy for customer willingness-to-pay and automobile demand prediction applications.Industrial Engineering & Managemen
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