Chaotic price dynamics of agricultural commodities

Traditionally, commodity prices have been analyzed and modeled in the context of linear generating processes. The purpose of this dissertation is to address the adequacy of this work through examination of the critical assumption of independence in the residual process of linearly specified models. As an alternative, a test procedure is developed and utilized to demonstrate the appropriateness of applying generalized conditional heteroscedastic time series models (GARCH) to agricultural commodity prices. In addition, a distinction is made between testing for independence and testing for chaos in commodity prices. The price series of interest derive from the major international agricultural commodity markets, sampled monthly over the period 1960--1994. The results of the present analysis suggest that for bananas, beef, coffee, soybeans, wool and wheat seasonally adjusted growth rates, ARCH-GARCH models account for some of the non-linear dependence in these commodity price series. As an alternative to the ARCH-GARCH models, several neural network models were estimated and in some cases outperformed the ARCH family of models in terms of forecast ability. This further demonstrated the nonlinearity present in these time series. Although, further examination is needed, all prices were found to be non-linearly dependent. It was determined by use of different statistical measures for testing for deterministic chaos that wheat prices may be an example of such behavior. Therefore, their may be something to be gained in terms of short-run forecast accuracy by using semi-parametric modeling approaches as applied to wheat prices

Financial Applications of Human Perception of Fractal Time Series

The purpose of this thesis is to explore the interaction between people’s financial behaviour and the market’s fractal characteristics. In particular, I have been interested in the Hurst exponent, a measure of a series’ fractal dimension and autocorrelation. In Chapter 2 I show that people exhibit a high level of sensitivity to the Hurst exponent of visually presented graphs representing price series. I explain this sensitivity using two types of cues: the illuminance of the graphs, and the characteristic of the price change series. I further show that people can learn how to identify the Hurst exponents of fractal graphs when feedback about the correct values of the Hurst exponent is given. In Chapter 3 I investigate the relationship between risk perception and Hurst exponent. I show that people assess risk of investment in an asset according to the Hurst exponent of its price graph if it is presented along with its price change series. Analysis reveals that buy/sell decisions also depend on the Hurst exponent of the graphs. In Chapter 4 I study forecasts from financial graphs. I show that to produce forecasts, people imitate perceived noise and signals of data series. People’s forecasts depend on certain personality traits and dispositions. Similar results were obtained for experts. In Chapter 5 I explore the way people integrate visually presented price series with news. I find that people’s financial decisions are influenced by news more than the average trend of the graphs. In the case of positive trend, there is a correlation between financial forecasts and decisions. Finally, in Chapter 6 I show that the way people perceive fractal time series is correlated with the Hurst exponent of the graphs. I use the findings of the thesis to describe a possible mechanism which preserves the fractal nature of price series

Behaviour and performance of key market players in the US futures markets

This study gives an insight into the behaviour and performance of large speculators and large hedgers in 29 US futures markets. Using a trading determinant model and priced risk factors such as net positions and sentiment index, results suggest hedgers (speculators) exhibit significant positive feedback trading in 15 (7) markets. Information variables like the S&P500 index dividend yield, corporate yield spread and the three months treasury bill rate were mostly unimportant in large players’ trading decisions. Hedgers had better market timing abilities than speculators in judging the direction of the market in one month. The poor market timing abilities and poor significance of positive feedback results suggest higher trading frequency intervals for speculators. Hedging pressures, which measure the presence of risk premium in futures markets, were insignificant mostly in agricultural markets. As a robust test of hedging pressures, price pressure tests found risk premium to be still significant for silver, crude oil and live cattle. The positive feedback behaviour and negative market timing abilities suggest hedgers in heating oil and Japanese yen destabilize futures prices, and points to a need to check CFTC’s (Commodity Futures Trading Commission) position limits regulation in these markets. In fact, large hedgers in these two markets are more likely to be leading behaviour, in that they have more absolute net positions than speculators. Alternatively stated, positive feedback hedgers in these two markets are more likely to lead institutions and investors to buy (sell) overpriced (underpriced) contracts, eventually leading to divergence of prices away from fundamentals.Atlhought hedgers in crude oil had significant positive feedback behaviour and negative market timing skills, they would not have much of a destabilizing effect over remaining players because the mean net positions of hedgers and speculators were not far apart. While the results are statistically significant, it is suggested these could be economically significant, in that there have been no regulation on position limits at all for hedgers compared to speculators who are imposed with strict limits from the CFTC. Further, mean equations were regressed against decomposed variables, to see how much of the futures returns are attributed to expected components of variables such as net positions, sentiment and information variables. While the expected components of variables are derived by ensuring there are enough ARMA (autoregressive and moving average) terms to make them statistically and economically reliable, the unexpected components of variables measure the residual on differences of the series from its mean. When decomposing net positions against returns, it was found expected net positions to be negatively related to hedgers’ returns in mostly agricultural markets. Speculators’ expected (unexpected) positions were less (more) significant in explaining actual returns, suggesting hedgers are more prone in setting an expected net position at the start of the trading month to determine actual returns rather than readjusting their net positions frequently all throughout the remaining days of the month. While it important to see how futures returns are determined by expected and unexpected values, it is also essential to see how volatility is affected as well.In an attempt to cover three broad types of volatility measures, idiosyncratic volatility, GARCH based volatility (variance based), and PARCH based volatility (standard deviation) are used. Net positions of hedgers (expected and unexpected) tend to have less effect on idiosyncratic volatility than speculators that tended to add to volatility, reinforcing that hedgers trading activity hardly affect the volatility in their returns. This suggest they are better informed by having a better control over their risk (volatility) measures. The GARCH model showed more reliance of news of volatility from previous month in speculators’ volatility. Hedgers’ and speculators’ volatility had a tendency to decay over time except for hedgers’ volatility in Treasury bonds and coffee, and gold and S&P500 for speculators’ volatility. The PARCH model exhibited more negative components in explaining current volatility. Only in crude oil, heating oil and wheat (Chicago) were idiosyncratic volatility positively related to return, reinforcing the suggestion for stringent regulation in the heating oil market. Expected idiosyncratic volatility was lower (higher) for hedgers (speculators) as expected under portfolio theory. Markets where variance or standard deviation are smaller than those of speculators support the price insurance theory where hedging enables traders to insure against the risk of price fluctuations. Where variance or standard deviation of hedgers is greater than speculators, this suggest the motivation to use futures contracts not primarily to reduce risk, but by institutional characteristics of the futures exchanges like regulation ensuring liquidity.Results were also supportive that there was higher fluctuations in currency and financial markets due to the higher number of contracts traded and players present. Further, the four models (GARCH normal, GARCH t, PARCH normal and PARCH t) showed returns were leptokurtic. The PARCH model, under normal distribution, produced the best forecast of one-month return in ten markets. Standard deviation and variance for both hedgers’ and speculators’ results were mixed, explained by a desire to reduce risk or other institutional characteristics like regulation ensuring liquidity. Moreover, idiosyncratic volatility failed to accurately forecast the risk (standard deviation or variance based) that provided a good forecast of one-month return. This supports not only the superiority of ARCH based models over models that assume equally weighted average of past squared residuals, but also the presence of time varying volatility in futures prices time series. The last section of the study involved a stability and events analysis, using recursive estimation methods. The trading determinant model, mean equation model , return and risk model, trading activity model and volatility models were all found to be stable following the effect of major global economic events of the 1990s. Models with risk being proxied as standard deviation showed more structural breaks than where variance was used. Overall, major macroeconomic events didn’t have any significant effect upon the large hedgers’ and speculators’ behaviour and performance over the last decade

Baltic Dry Index Estimation With NARX Neural Network Model

BDI is a global trade indicator followed by those interested in maritime trade. But it has volatility, seasonality, and uncertain cyclicality. For this reason, in this study, the BDI has been estimated to provide preliminary information to those interested in maritime trade. NARX Neural Network which performs successfully in complex and nonlinear real-life problems is used. In addition, the NARX neural network model has not been found in a previous study used for BDI estimation. Eleven independent variables are used in this study, what increases the predictive power. Independent variables are Bloomberg Commodities Index (BCOM), Twitter-Based Economic Uncertainty Index (TEU), Twitter-Based Market Uncertainty Index (TMU), S&P 500 Index, MSCI World Index, €/$ Parity, VIX (CBOE), US 10-Year Bond Yield (%), Brent Oil (USD/Barrel), Economic Uncertainty Index and World Trade Volume (USD Billion). The Twitter-Based Economic Uncertainty Index (TEU) and Twitter-Based Market Uncertainty Index (TMU), which were not used before in BDI estimation studies, were included in the analysis and contributed to the literature. The data set contains daily data for the period 9.07.2012–31.08.2020. 11-day estimate values covering 1.09.2020–15.09.2020 are calculated. MAPE, MAE and RMSE performance criteria were calculated for the estimation values. Value of MAPE (2.96%), value of MAE (36.6%) and value of RMSE (46.68) were obtained. As a result, the estimate values were compared with the actual values

Using seasonal climate forecasting in agriculture: a participatory decision-making approach

Farm Management,