An Empirical Analysis of Dynamic Multiscale Hedging using Wavelet Decomposition

This paper investigates the hedging effectiveness of a dynamic moving window OLS hedging model, formed using wavelet decomposed time-series. The wavelet transform is applied to calculate the appropriate dynamic minimum-variance hedge ratio for various hedging horizons for a number of assets. The effectiveness of the dynamic multiscale hedging strategy is then tested, both in- and out-of-sample, using standard variance reduction and expanded to include a downside risk metric, the time horizon dependent Value-at-Risk. Measured using variance reduction, the effectiveness converges to one at longer scales, while a measure of VaR reduction indicates a portion of residual risk remains at all scales. Analysis of the hedge portfolio distributions indicate that this unhedged tail risk is related to excess portfolio kurtosis found at all scales.Comment: To Appear: Journal of Futures Market

An Empirical Analysis of Dynamic Multiscale Hedging using Wavelet Decomposition

This paper investigates the hedging effectiveness of a dynamic moving window OLS hedging model, formed using wavelet decomposed time-series. The wavelet transform is applied to calculate the appropriate dynamic minimum-variance hedge ratio for various hedging horizons for a number of assets. The effectiveness of the dynamic multiscale hedging strategy is then tested, both in- and out-of-sample, using standard variance reduction and expanded to include a downside risk metric, the time horizon dependent Value-at-Risk. Measured using variance reduction, the effectiveness converges to one at longer scales, while a measure of VaR reduction indicates a portion of residual risk remains at all scales. Analysis of the hedge portfolio distributions indicate that this unhedged tail risk is related to excess portfolio kurtosis found at all scales.

Wavelet multiresolution analysis of financial time series

fi=vertaisarvioitu|en=peerReviewed

Eurolanguages-2012: Innovations and Development

Збірник наукових студентських робіт призначено для широкого кола читачів, які цікавляться проблемами вивчення іноземних мов та перекладу в Україні

Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias

Large language models (LLMs) have been recently leveraged as training data generators for various natural language processing (NLP) tasks. While previous research has explored different approaches to training models using generated data, they generally rely on simple class-conditional prompts, which may limit the diversity of the generated data and inherit systematic biases of LLM. Thus, we investigate training data generation with diversely attributed prompts (e.g., specifying attributes like length and style), which have the potential to yield diverse and attributed generated data. Our investigation focuses on datasets with high cardinality and diverse domains, wherein we demonstrate that attributed prompts outperform simple class-conditional prompts in terms of the resulting model's performance. Additionally, we present a comprehensive empirical study on data generation encompassing vital aspects like bias, diversity, and efficiency, and highlight three key observations: firstly, synthetic datasets generated by simple prompts exhibit significant biases, such as regional bias; secondly, attribute diversity plays a pivotal role in enhancing model performance; lastly, attributed prompts achieve the performance of simple class-conditional prompts while utilizing only 5\% of the querying cost of ChatGPT associated with the latter. We release the generated dataset and used prompts to facilitate future research. The data and code will be available on \url{https://github.com/yueyu1030/AttrPrompt}.Comment: Work in progress. A shorter version is accepted to the ICML DMLR worksho

Applications of Airborne and Portable LiDAR in the Structural Determination, Management, and Conservation of Southeastern U.S. Pine Forests

Active remote sensing techniques, such as Light Detection and Ranging (LiDAR), have transformed the field of forestry and natural resource management in the last decade. Intensive assessments of forest resources and detailed structural assessments can now be accomplished faster and at multiple landscape scales. The ecological applications of having this valuable information at-hand are still only being developed. This work explores the use of two active remote sensing techniques, airborne and portable LiDAR for forestry applications in a rapidly changing landscape, Southeastern Coastal Pine woodlands. Understanding the strengths and weaknesses of airborne and portable LiDAR, the tools used to extract structural information, and how to apply these to managing fire regimes are key to conserving unique upland pine ecosystems. Measuring habitat structure remotely and predicting habitat suitability through modeling will allow for the management of specific species of interest, such as threatened and endangered species. Chapter one focuses on the estimation of canopy cover and height measures across a variety of conditions of secondary upland pine and hardwood forests at Tall Timbers Research Station, FL. This study is unique since it uses two independent high resolution small-footprint LiDAR datasets (years 2002 and 2008) and extensive field plot and transect sampling for validation. Chapter One explores different tools available for metric derivation and tree extraction from discrete return airborne LiDAR data, highlighting strengths and weaknesses of each. Field and LiDAR datasets yielded better correlations for stand level comparisons, especially in canopy cover and mean height data extracted. Individual tree crown extraction from airborne LiDAR data significantly under-reported the total number of trees reported in the field datasets using either Fusion/LVD and LiDAR Analyst (Overwatch). Chapter two evaluates stand structure at the site of one of the longest running fire ecology studies in the US, located at Tall Timbers Research Station (TTRS) in the southeastern U.S. Small footprint high resolution discrete return LiDAR was used to provide an understanding of the impact of multiple disturbance regimes on forest structure, especially on the 3-dimensional spatial arrangement of multiple structural elements and structural diversity indices. LiDAR data provided sensitive detection of structural metrics, diversity, and fine-scale vertical changes in the understory and mid-canopy structure. Canopy cover and diversity indices were shown to be statistically higher in fire suppressed and less frequently burned plots than in 1- and 2-year fire interval treated plots, which is in general agreement with the increase from 2- to 3-year fire return interval being considered an ecological threshold for these systems (Masters et al. 2005). The results from this study highlight the value of the use of LiDAR in evaluating disturbance impacts on the three-dimensional structure of pine forest systems, particularly over large landscapes. Chapter three uses an affordable portable LiDAR system, first presented by Parker et al. (2004) and further modified for extra portability, to provide an understanding of structural differences between old-growth and secondary-growth forests in the Red Hills area of southwestern Georgia and North Florida. It also provides insight into the strengths and weaknesses in structural determination of ground-based portable systems in contrast to airborne LiDAR systems. Structural plot metrics obtained from airborne and portable LiDAR systems presented some similarities (i.e. canopy cover), but distinct differences appeared when measuring canopy heights (maximum and mean heights) using these different methods. Both the airborne and portable systems were able to provide gap detection and canopy cover estimation at the plot level. The portable system, when compared to the airborne LiDAR sensor, provides an underestimation of canopy cover in open forest systems ([less than]50% canopy cover), but is more sensitive in detection of cover in hardwood woodland plots ([greater than]60% canopy cover). The strength of the portable LiDAR system lies in the detection of 3-dimensional fine structural changes (i.e. recruitment, encroachment) and with higher sensitivity in detecting lower canopy levels, often missed by airborne systems. Chapter four addresses a very promising application for fine-scale airborne LiDAR data, the creation of habitat suitability models for species of management and conservation concerns. This Chapter uses fine scale LiDAR metrics, such as canopy cover at various height strata, canopy height information, and a measure of horizontal vegetation distribution (clumped versus dispersed) to model the preferences of 10 songbirds of interest in southeast US woodlands. The results from this study highlight the rapidly changing nature of habitat conditions and how these impact songbird occurrence. Furthermore, Chapter four provides insight into the use of airborne LiDAR to provide specific management guidance to enhance the suitable habitat for 10 songbird species. The collection of studies presented here provides applied tools for the use of airborne and portable LiDAR for rapid assessment and responsive management in southeastern pine woodlands. The advantages of detecting small changes in three-dimensional vegetation structure and how these can impact habitat functionality and suitability for species of interest are explored throughout the next four chapters. The research presented here provides an original and important contribution in the application of airborne and portable LiDAR datasets in forest management and ecological studies

Dynamic block encryption with self-authenticating key exchange

One of the greatest challenges facing cryptographers is the mechanism used for key exchange. When secret data is transmitted, the chances are that there may be an attacker who will try to intercept and decrypt the message. Having done so, he/she might just gain advantage over the information obtained, or attempt to tamper with the message, and thus, misguiding the recipient. Both cases are equally fatal and may cause great harm as a consequence. In cryptography, there are two commonly used methods of exchanging secret keys between parties. In the first method, symmetric cryptography, the key is sent in advance, over some secure channel, which only the intended recipient can read. The second method of key sharing is by using a public key exchange method, where each party has a private and public key, a public key is shared and a private key is kept locally. In both cases, keys are exchanged between two parties. In this thesis, we propose a method whereby the risk of exchanging keys is minimised. The key is embedded in the encrypted text using a process that we call `chirp coding', and recovered by the recipient using a process that is based on correlation. The `chirp coding parameters' are exchanged between users by employing a USB flash memory retained by each user. If the keys are compromised they are still not usable because an attacker can only have access to part of the key. Alternatively, the software can be configured to operate in a one time parameter mode, in this mode, the parameters are agreed upon in advance. There is no parameter exchange during file transmission, except, of course, the key embedded in ciphertext. The thesis also introduces a method of encryption which utilises dynamic blocks, where the block size is different for each block. Prime numbers are used to drive two random number generators: a Linear Congruential Generator (LCG) which takes in the seed and initialises the system and a Blum-Blum Shum (BBS) generator which is used to generate random streams to encrypt messages, images or video clips for example. In each case, the key created is text dependent and therefore will change as each message is sent. The scheme presented in this research is composed of five basic modules. The first module is the key generation module, where the key to be generated is message dependent. The second module, encryption module, performs data encryption. The third module, key exchange module, embeds the key into the encrypted text. Once this is done, the message is transmitted and the recipient uses the key extraction module to retrieve the key and finally the decryption module is executed to decrypt the message and authenticate it. In addition, the message may be compressed before encryption and decompressed by the recipient after decryption using standard compression tools