Volatility provides a measure of the possible variation or movement in the price of a commodity experienced over a given period of time . In this study, volatility of maize prices for Kenya, Uganda, and Tanzania in comparison with the FAO Global prices for the period of January 2003 to August 2011 is estimated. The study employs measures of dispersion like unconditional standard deviation and the coefficient of variation together with a 20% band around the trend line compared with time series models, the ARMA and EGARCH. Maize prices in Kenya, Tanzania, and FAO Global were found to be time varying and thus estimated using the EGARCH model and the average unconditional standard deviation used as a measure of volatility. Volatility of Uganda maize prices was constant over time and was estimated using ARMA (1,1). The study shows that by both methods, maize prices in Uganda were the most volatile followed by Kenya, Tanzania and then FAO Global implying that volatility in the East African markets is high compared to the global maize market.
The world is experiencing yet another energy- and fuel predicament as oil prices are escalating to new hights. Alternative fuels are being promoted globally as the increasing gasoline prices trigger inflation. Basic food commodities are some of the goods hit by this inflation. This book analyses whether the higher maize and sugar prices are having any effect on the expanding ethanol production. It focuses on the two major crop inputs in ethanol production: maize (in the US) and sugar cane (in Brazil). Econometric tests using cross-sectional data were carried through to find the elasticities of the variables. The crops prices were tested against ethanol output using the log-linear model in several regressions to find a relationship. In addition, the output levels of the crops were tested using the same method. It was found that maize prices and output affects ethanol production. Sugar cane prices do not have any significant impact on ethanol production while sugar cane output has a small, yet significant relationhip with ethanol. Consequently, ethanol''s rise in the fuel market could be a result of increased maize input, rather than sugar.
A new, more accurate take on the classical approach to volatility evaluation Inside Volatility Filtering presents a new approach to volatility estimation, using financial econometrics based on a more accurate estimation of the hidden state. Based on the idea of «filtering», this book lays out a two-step framework involving a Chapman-Kolmogorov prior distribution followed by Bayesian posterior distribution to develop a robust estimation based on all available information. This new second edition includes guidance toward basing estimations on historic option prices instead of stocks, as well as Wiener Chaos Expansions and other spectral approaches. The author's statistical trading strategy has been expanded with more in-depth discussion, and the companion website offers new topical insight, additional models, and extra charts that delve into the profitability of applied model calibration. You'll find a more precise approach to the classical time series and financial econometrics evaluation, with expert advice on turning data into profit. Financial markets do not always behave according to a normal bell curve. Skewness creates uncertainty and surprises, and tarnishes trading performance, but it's not going away. This book shows traders how to work with skewness: how to predict it, estimate its impact, and determine whether the data is presenting a warning to stay away or an opportunity for profit. Base volatility estimations on more accurate data Integrate past observation with Bayesian probability Exploit posterior distribution of the hidden state for optimal estimation Boost trade profitability by utilizing «skewness» opportunities Wall Street is constantly searching for volatility assessment methods that will make their models more accurate, but precise handling of skewness is the key to true accuracy. Inside Volatility Filtering shows you a better way to approach non-normal distributions for more accurate volatility estimation.
One of the most important features of financial assets is the asset price volatility. Understanding volatility and its modelling has been subject matter of great concern for academicians, policy makers and practitioners. The objective of the book is to analyze and capture volatility using ARCH/GARCH classes of models and suggest the best model which explains volatility and its characteristics in a better way. The unique feature of the book is that it uses open source software R to analyse the data set. It implements various functions in R to carry out analysis. The data codes are given in the book. This will help researcher to analyse his/her data set with little bit of modification in the codes. The approach adopted in the book will facilitate any researcher to perform advanced time series data analysis at ease.
This work provides empirical examination of conditionality, degree of persistence of international coffee price volatility and effects of policies in Tanzania. In modelling coffee price volatility, previous studies treated variation in price as unconditional and volatility addressed as constant overtime but recent studies indicated that price volatility exhibits auto-regressive conditional heteroskedastic behavior. Noting that, this study has attempted to model coffee price volatility using the GARCH (1,1) process. It was written out of the time-series data on the international coffee price paid to growers in Tanzania spanning from 1980 through 2008. The study establishes that coffee price had no regular pattern rather a random walk process, non-conditional and its persistence overtime displays memory decay. Changes in trade policies within Tanzania do not have any effect on the world coffee price. The likely explanation is that Tanzania is a price taker, cannot affect settings of international prices. The policy response to this include new marketing approach, ethical trading, Tanzania coffee can be marketed as a brand and revitalization of cooperatives.
Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research using more advanced statistical methods. The statistical requirements of interpreting and publishing in medical journals, together with rapid changes in science and technology, increasingly demands an understanding of more complex and sophisticated analytic procedures. The text explains the application of statistical models to a wide variety of practical medical investigative studies and clinical trials. Regression methods are used to appropriately answer the key design questions posed and in so doing take due account of any effects of potentially influencing co-variables. It begins with a revision of basic statistical concepts, followed by a gentle introduction to the principles of statistical modelling. The various methods of modelling are covered in a non-technical manner so that the principles can be more easily applied in everyday practice. A chapter contrasting regression modelling with a regression tree approach is included. The emphasis is on the understanding and the application of concepts and methods. Data drawn from published studies are used to exemplify statistical concepts throughout. Regression Methods for Medical Research is especially designed for clinicians, public health and environmental health professionals, para-medical research professionals, scientists, laboratory-based researchers and students.
The predictors of survival and readmission in heart failure have not yet been looked at together, especially in a risk modelling approach. To do this a 10 yr cohort from the Royal Adelaide hospital was used that recorded socioeconomic, comorbidity, pharmacological, echo and blood test results (biochemistries) predictors with time to death and every subsequent readmission in separate datasets. This study employs bootstrapping, statistical programming and advanced statistical methods in prognostic risk modelling, survival analysis and recurrent event survival analysis. This study employs these methods to elucidate predictors of survival and readmission in heart failure. This book is useful for cardiologists and other clinicians as well as health service delivery planners. It is also useful as a case study for epidemiologists and biostatisticians and those with a general interest in cardiology and heart failure.
The study establishes the effects of Kenyan 2007 post election violence on maize farming calendar, farm input prices, food prices and infrastructural development in Uasin Gishu District of Kenya. The study reveals post election violence devastating effects on maize farming. When peace was breached; many maize farmers were displaced, maize crop in the field and granaries was burnt and looted, farm implements and machinery were destroyed. Food deficit in the study district was experienced, hence millers had to import maize to sustain local consumer demand. Maize farming inputs prices shot up resulting in a drop in farming activities, delayed timings and reduced acreages coverage which contributed to food crisis in Kenya in General. The study recommends the that Kenyan government and other stakeholders in the maize sub-sector facilitate peace building and reconciliation to boost cohesion, empower the maize farmer by providing farm subsidies, affordable tractor hire services and enhance security for the sake of preventing this vital sub sector in the country from collapsing. The study is appoint of reference for other Nation to learn from.
This work described in this book develops a computationally efficient approach to performing electromagnetic simulations in the presence of statistically defined uncertainties caused by either material inhomogeneities or fabrication and placement tolerances. Comparisons are made with results from Monte Carlo simulations and a sequence of higher order approximation extensions is considered. There are two main statistical techniques used to achieve the overall objective of this book namely: the Direct Solution Technique (DST) and the Unscented Transform (UT) method. The case studies used are developed using the Transmission Line Modelling (TLM) method.
In the early 1990’s, most developing countries implemented reforms in the agricultural sector that affected the volatility of product prices. However, there is limited empirical evidence on the effects of these reforms on the level and volatility of Irish potato prices. This book evaluates the effects of market reform policies on the evolution and volatility of Irish potato prices in Kenya. By using an autoregressive conditional heteroscedasticity in mean model, a monthly time series data set was utilized to identify the effects of the market reforms on the volatility of Irish potato prices. The results of the analysis indicate that the implementation of market reform policies led to higher prices and reduction of price volatility. The book recommends the requisite measures to enable the farmers to realize maximum benefits from the effects of the implementation of market reform policies. Overall, this book sheds more light on the existing body of knowledge on the effects of market reforms and Irish potato marketing and is especially useful to researchers, farmers and policy makers.
This study was motivated by over a quarter of a century of exchange rate volatility, the persistent problem of budget deficit in Kenya, and the importance of the two in the economy, which explains their inclusions in the convergence criteria for the East Africa Community. Exchange rate volatility exposes the participants of international markets to the risk of loss and thus disturbs the optimal allocation of resources. Despite effort made by past studies to explain exchange rate volatility, there exists controversy in their findings, with a counterargument being provided for each assertion. This study attempted to improve on the modelling of volatility in exchange rate in order to best explain its movement. This was done by analyzing the effects of using the financing composition of budget deficit instead of budget deficit in modelling of exchange rate volatility. Thereafter, the model that provided the best fit and forecast were applied to determine the effects of budget deficit financing and macroeconomic fundamentals on exchange rate volatility under different exchange rate regimes.
The skyrocketing prices of oil and food are matters of great concerns for consumers, producers, national and international bodies. This book adresses this aspect in five independent but related essays. The first two essays model the extent of volatility of oil and food price shocks. Third essay examines the cross country mean and volatility spillover effects of food prices while the fourth essay investigates the spillover effects of oil prices on the food prices of selected Asia and Pacific countries. Finally, the fifth essay assesses the economic impacts of oil and food prices in the context of selected Asia and Pacific countries. Overall, the findings show that both oil and food prices have persistence effets on the volatility while the evidence of asymmetry is mixed; there is considerable cross country mean and volatility spillover effects of food prices; oil prices positively spillover to food prices and the impacts of oil and food prices to macroeconomic variables are dependent on the economic characteristics of individual economies.
As technology scaling enters into nanometer geometries, there is a significant increase in performance uncertainty of System-on-chip designs due to parametric variations. Process variability is posing an increasing challenge in timing analysis for designers. The traditional corner-based approach is not effective anymore and it will induce pessimism. Therefore, statistical timing analysis has become very important in overcoming the weakness of the traditional methods. By constructing statistical timing models for variations, chip performance and parametric yield will be more accurately predicted. In this work, variability aware statistical timing models of standard cells and system level interconnects are developed by using SPICE simulations, and statistical timing analysis flow and characterization automation are summarized.
Popular guide to options pricing and position sizing for quant traders In this second edition of this bestselling book, Sinclair offers a quantitative model for measuring volatility in order to gain an edge in everyday option trading endeavors. With an accessible, straightforward approach, he guides traders through the basics of option pricing, volatility measurement, hedging, money management, and trade evaluation. This new edition includes new chapters on the dynamics of realized and implied volatilities, trading the variance premium and using options to trade special situations in equity markets. Filled with volatility models including brand new option trades for quant traders Options trader Euan Sinclair specializes in the design and implementation of quantitative trading strategies Volatility Trading, Second Edition + Website outlines strategies for defining a true edge in the market using options to trade volatility profitably.
The aim of this work is comparing two different models for estimating and forecasting the volatility of financial assets returns, the GARCH and the Stochastic Volatility (SV) model, applying their results to a daily Value at Risk model (VAR). The analysis consists, for each model, in a theoretical discussion and an empirical analysis carried out on a dataset containing S&P500 daily prices. The first part of the research is dedicated to the theoretical comparison and practical estimation of the two volatility models: for the SV model we introduce Bayesian analysis, MCMC methods such as the Gibbs Sampler and Metropolis Hastings algorithm. In the second part of the work we employ the two models variance predictions to build a daily VAR, identifying strengths and weaknesses of each volatility model from a VAR application point of view.