Risk Management
66 curated documents on risk management from the Gyre Research library, each with a summary. Free to read, no signup required.
2024 Fixed Income Outlook in One Word: Batman!
Loop Capital · Report
January 2024 2024 Fixed Income Outlook in One Word: Batman! At a recent conference, we were asked to summarize the state of the U.S. bond market in a single word. Unlike asset classes promising higher returns and lower volatility, bonds do not have superpowers.
Also filed under Finance, Corporate Finance
2024 Regulations: How They Impact Your Compliance Training Programs
BAI is Bank Administration Institute and BAI Center · Report
"2024 Regulations: How They Impact Your Compliance Training Programs" is a timely and essential guide for financial institutions, offering an in-depth exploration of the regulatory landscape and its influence on compliance training. The book addresses the evolving nature of regulations and provides practical strategies to ensure that institutions meet these challenges head-on, preparing their staff for an increasingly complex compliance environment. Key Highlights: Comprehensive Overview of 2024 Regulatory Changes: This book outlines the significant regulatory changes coming in 2024, breaking down their potential impacts on financial institutions. It explores how new and revised regulations affect key areas like data privacy, anti-money laundering (AML), cybersecurity, and financial reporting. Impact on Compliance Programs: The authors focus on the direct impact of these regulatory changes on compliance training programs. The book explains how institutions need to adapt their training content, delivery methods, and tracking mechanisms to stay compliant with the latest regulations. Adapting Training Programs to New Regulatory Demands: Curriculum Updates: The book highlights the need for compliance programs to be regularly updated to reflect changes in the regulatory environment, ensuring that staff members are well-equipped to navigate new rules. Tailored Training Approaches: Emphasizing the importance of personalized and role-specific training, the authors offer strategies for creating customized learning experiences that meet the needs of different departments within the institution. Technological Solutions for Compliance Training: Given the growing importance of technology in compliance, the book discusses how institutions can leverage learning management systems (LMS), automated compliance tools, and e-learning platforms to efficiently deliver up-to-date training content and monitor employee progress. Risk Management and Mitigation: The book also explores how compliance training can help mitigate risks related to regulatory breaches. By equipping employees with the knowledge of new regulations, institutions can reduce the likelihood of non-compliance and avoid costly penalties. Engagement and Retention in Training Programs: An essential aspect of any training program is ensuring engagement and retention of information. The authors delve into methods for making compliance training more interactive, engaging, and memorable, using real-world scenarios, case studies, and gamification techniques to enhance learning. Tracking and Measuring Effectiveness: The authors emphasize the importance of measuring the effectiveness of compliance training programs. The book provides guidance on how institutions can track employee progress, assess knowledge retention, and adjust training approaches based on data and feedback. Regulatory Trends to Watch Beyond 2024: The book goes beyond 2024, offering insights into the regulatory trends that are expected to shape compliance training in the coming years. This forward-looking perspective helps institutions stay proactive in preparing for future changes.
Also filed under Corporate Finance, Finance
A Neural Network Approach to Understanding Implied Volatility Movements
Jay Cao, Jacky Chen & John Hull · Report
Abstract We employ neural networks to understand volatility surface movements. We first use daily data on options on the S&P 500 index to derive a relationship between the expected change in implied volatility and three variables: the return on the index, the moneyness of the option, and the remaining life of the option. This model provides an improvement of 10.72% compared with a simpler analytic model. We then enhance the model with an additional feature: the level of the VIX index prior to the change being observed.
Also filed under Quant Finance, Machine Learning
A Supervisory Framework for Assessing Nature-Related Financial Risks: Identifying and Navigating Biodiversity Risks
Riccardo Boffo, Hugh Miller, Juan Pavajeau Fuentes, Giulio Mazzone, Geraldine Ang · Report
This OECD report presents a methodological supervisory framework to help central banks and financial supervisors assess biodiversity-related financial risks within their financial sectors. It outlines a four-step approach covering risk identification and prioritization, economic risk assessment, financial risk transmission channels (credit, market, and liquidity risks), and supervisory considerations. The framework translates ecosystem service degradation into quantifiable financial risks while accounting for interconnections with climate change and broader environmental degradation.
Also filed under Finance, Corporate Finance
All about Barrier Options
Author Unknown · Report
Introduction to Barrier Options 2. These options are particularly useful in cases where the buyer is only interested in payoffs under certain conditions. This fea- ture makes barrier options more cost-effective than vanilla options, as certain outcomes are excluded. Barrier options offer a cost-saving alternative to vanilla options.
Also filed under Finance
Applying Factor Models in Pairs Trading
Author Unknown · Notes
Store now The Fama-French model is fully described in the chapter on Factor Models. In the previous chapter, we used the log-returns series of the PEP and KO stocks to create a combined portfolio, applying a Kalman filter to estimate the dynamic relationship between the two returns series. By taking weighted long and short positions in the two stocks, as determined by the βt coefficient estimated in the Kalman model, we were able to eliminate market risk and achieve a returns process that is close to being stationary. However, there is still the question of other risk factors such as size...
Also filed under Quant Finance, Finance
Calculating Beta for unlisted firms
FinShiksha · Slides
Or one that has listed recently? A company coming with an IPO, or a recently listed one, does not have the price history to calculate Beta. So it may be incorrect to take a simple average of betas of sector peers Can we take sector average?
Also filed under Corporate Finance, Quant Finance
Capital Asset Prices With and Without Negative Holdings
William F. Sharpe · Report
William Sharpe's 1990 Nobel Prize lecture summarizing his contributions to the CAPM and extending the framework to portfolios with short positions. Authoritative primary-source treatment of the model that earned Sharpe the prize.
Also filed under Quant Finance, Finance
Carbon Market Principles
JP Morgan Chase & Co · Report
JPMorgan Chase & Co.'s "Carbon Market Principles" outlines the firm's perspective on the voluntary carbon market's role in achieving net-zero emissions. The document highlights key challenges and sets forth principles to guide the firm's engagement in the voluntary carbon market. Key Highlights: Role of Voluntary Carbon Markets: The paper emphasizes the importance of voluntary carbon markets in facilitating emissions reductions beyond regulatory requirements, supporting corporate sustainability goals, and driving innovation in carbon offset projects. Challenges in the Voluntary Carbon Market: It identifies challenges such as ensuring the additionality of carbon credits, maintaining transparency, preventing double-counting, and establishing standardized methodologies for measuring and verifying emissions reductions. JPMorgan Chase & Co.'s Carbon Market Principles: The firm introduces guiding principles to address the identified challenges and align with best practices: Additionality: Ensuring that carbon credits represent genuine emissions reductions that would not have occurred without the project. Transparency: Providing clear and accessible information about carbon credit projects, methodologies, and verification processes. Integrity: Adhering to high environmental and social standards, ensuring that projects deliver real and lasting benefits. Standardization: Supporting the development and adoption of standardized approaches for measuring, reporting, and verifying emissions reductions. Innovation: Encouraging the development of new methodologies and projects that enhance the effectiveness and efficiency of carbon markets. Commitment to Progress: JPMorgan Chase & Co. expresses its dedication to collaborating with stakeholders to advance the voluntary carbon market, aiming to overcome existing challenges and contribute to global climate objectives.
Also filed under Corporate Finance, Finance
Chief Risk Officers Outlook
World Economic Forum · Book
The final section examines the role of the risk function in organizational growth and innovation. The survey featured in this briefing was conducted in May 2024. A volatile mid-year outlook: the global perspective _ __________________________ 7 2. The overall global outlook appears
Also filed under Finance, Corporate Finance
Comprehensive Guide to Volatility Models in Option Pricing
Amit Kumar Jha · Guide
At the heart of option pricing lies the concept of volatility - a measure of the uncertainty or risk associated with the magnitude of changes in an asset’s value. This comprehensive guide delves deep into three primary volatility models used in option pricing: the constant volatility model, the local volatility model, and the stochastic volatility model. Understanding these models is crucial for any practitioner in the field of quantitative finance. Each model offers unique insights and applications, with its own set of strengths and limitations.
Also filed under Quant Finance, Finance
correlation-vs-causation
Author Unknown · Book
Overview of Correlation and Causation 2. Pearson Correlation Coefficient (r) 2. Spearman Rank Correlation Coefficient (or rs) 3. Kendall Tau Rank Correlation Coefficient (or Kendall’s ) 4.
Also filed under Statistics
Crypto Market Roundup
ETC Group · Slides
ETC Group, a leading provider of cryptocurrency exchange-traded products (ETPs), regularly publishes the "Crypto Market Compass," offering in-depth analyses of the cryptocurrency market's performance, investor sentiment, and emerging trends. Key Highlights from Recent Reports: Record Inflows into Crypto ETPs: In late 2024, ETC Group observed unprecedented net inflows into global crypto ETPs, with weekly inflows reaching $2.94 billion, primarily driven by Bitcoin investments. Impact of U.S. Strategic Crypto Reserve Announcement: In March 2025, President Donald Trump's proposal to include major cryptocurrencies like Bitcoin and Ethereum in the U.S. strategic reserve led to an initial surge in cryptocurrency prices. However, these gains were short-lived, with prices stabilizing at lower levels. Market Volatility and Regulatory Developments: The cryptocurrency market has experienced significant fluctuations, influenced by global economic indicators and policy announcements. Notably, the largest-ever cryptocurrency theft occurred recently, further impacting market sentiment. Positive Sentiment Amidst Global Growth Concerns: Despite global growth risks, ETC Group's "Cryptoasset Sentiment Index" indicated a bullish outlook, reaching levels not seen since March 2024, reflecting renewed investor confidence
Also filed under Finance
Crypto Theses 2024
messari.io · Book
"Crypto Theses for 2024" is Messari's annual report that provides an in-depth analysis of the cryptocurrency market, highlighting key trends, investment themes, influential figures, policy developments, and technical advancements expected to shape the crypto landscape in 2024. Key Highlights: Investment Themes: The report identifies several investment themes poised to influence the crypto market in 2024, emphasizing the importance of permissionless innovation over centralized entities. People to Watch: It profiles top individuals who are expected to have a significant impact on the crypto industry in 2024, offering insights into their contributions and potential influence. Policy Developments: The report examines legislative efforts and policy changes affecting the crypto space, including potential regulations and their implications for the industry. Technical Breakthroughs: It discusses anticipated technical innovations and developments within the crypto ecosystem, highlighting projects and protocols to watch in 2024. Overall, "Crypto Theses for 2024" serves as a comprehensive guide for professionals and enthusiasts seeking to understand and navigate the evolving crypto landscape in the coming year.
Also filed under Finance
Cryptoassets & Blockchain
Richard B. Levin, Kevin Tran & Robert Wenner · Guide
Law Business Research is not responsible for any actions (or lack thereof) taken as a result of relying on or in any way using information contained in this report and in no event shall be liable for any damages resulting from reliance on or use of this information.
Also filed under Finance, Corporate Finance
Custom Calculation Data Points
Morningstar. · Report
Morningstar's Custom Calculation Data Points are specialized metrics that enable users to analyze a portfolio's performance by comparing its actual returns to those predicted based on its risk profile, as indicated by its Beta. These calculations provide deeper insights into investment performance beyond standard metrics. Key Features: Alpha (Excess Return): -Measures the difference between a portfolio's actual returns and its expected returns, given its Beta. -A positive Alpha indicates outperformance relative to the expected return based on Beta. -Calculated using the formula: α = (Average Monthly Excess Return of Investment) – (Beta × Average Monthly Excess Return of Benchmark) -Morningstar annualizes the monthly Alpha to present it in annual terms. Alpha (Non-Excess Return): -Assesses the difference between a portfolio's actual returns and its expected returns without considering its Beta. -A positive value suggests the portfolio has performed better than expected, while a negative value indicates underperformance. -Calculated by subtracting Beta-adjusted benchmark returns from the portfolio's raw returns. -Morningstar also annualizes this monthly Alpha for annual representation. Appraisal Ratio: -Evaluates the abnormal excess return per unit of non-systematic risk taken. -Computed by dividing the unannualized Alpha by the standard error of the residual. -A higher ratio indicates more efficient risk-adjusted returns. Custom Calculated Data Points: Morningstar offers custom calculated versions of certain data points, allowing users to access historical values and tailor calculations to specific analytical needs. For instance, custom calculated data points enable users to see past values for metrics like Morningstar Category, providing insights into a fund's classification history. Creating and Utilizing Custom Data Sets: Users can create custom data sets in Morningstar Direct by selecting relevant data points, including custom calculations, to focus on specific analysis criteria. This customization enhances the precision of performance evaluations and benchmarking processes. Integration with Excel: Morningstar's Excel Add-In allows users to retrieve various data points, including custom calculations, directly into Microsoft Excel. This integration facilitates further data manipulation, formatting, and charting, streamlining the analysis process. By leveraging these custom calculation data points, investors and analysts can gain a nuanced understanding of portfolio performance, enabling more informed investment decisions and comprehensive performance assessments.
Also filed under Quant Finance, Finance
Discounted Cash Flow Valuation
Aswath Damodaran · Slides
Proposition 1: For an asset to have value, the expected cash flows have to be positive some time over the life of the asset. Proposition 2: Assets that generate cash flows early in their life will be worth more than assets that generate cash flows later; the latter may however have greater growth and higher cash flows to compensate.
Also filed under Corporate Finance, Quant Finance
Dispersion Trading
Marco Avellaneda · Report
"Dispersion Trading" by Marco Avellaneda offers an in-depth exploration of a unique and strategic approach to trading options, focusing on the concept of dispersion trading, which involves exploiting the differences between the volatility of individual stocks and the volatility of index options. This book is a comprehensive guide for both novice and experienced traders, aiming to provide insights into the techniques and strategies used in dispersion trading. Key Highlights: Concept of Dispersion Trading: The book introduces dispersion trading, which is based on the notion that individual stock volatilities deviate from the volatility of an index. Traders look to capitalize on these differences by buying or selling options on individual stocks while simultaneously trading index options. Mathematical Foundations: The author delves into the mathematical underpinnings of dispersion trading, using concepts from probability theory, stochastic processes, and statistical analysis to explain the behavior of volatility and correlations between individual stocks and index options. Risk Management: A significant portion of the book is dedicated to risk management strategies. It emphasizes how to mitigate potential risks associated with dispersion trades, such as managing exposure to index movements and individual stock price fluctuations. Modeling and Theoretical Framework: The book discusses various models used to predict price movements, including the Black-Scholes model and other options pricing models. It also presents advanced techniques for assessing the effectiveness of dispersion strategies. Practical Applications: Avellaneda emphasizes real-world applications of dispersion trading in different market conditions. The book provides step-by-step examples of how to execute these trades and evaluate their performance in different market environments. Market Efficiency and Arbitrage Opportunities: The author discusses the role of market efficiency in dispersion trading, outlining how arbitrage opportunities arise from mispricings between individual stock options and index options. He examines the dynamics of these opportunities and the conditions under which they can be exploited.
Also filed under Quant Finance, Finance
ESG: From Process to Product
George Serafeim · Report
"ESG: From Process to Product" is a working paper by George Serafeim that examines the evolution of Environmental, Social, and Governance (ESG) practices from internal corporate processes to marketable investment products. Key Insights: Transformation of ESG Practices: Initially, ESG encompassed internal processes such as measurement, analysis, management, and communication within organizations. Over time, the financial industry has transformed ESG into a product, leading to the proliferation of investment funds labeled as ESG-focused. Resulting Confusion: This shift has caused confusion among investors and stakeholders, as the ESG label is applied broadly without a standardized framework, making it challenging to assess the true impact and intentions of ESG-labeled investment products. Proposed Framework for ESG Products: Serafeim proposes a framework to define the objectives and characteristics of ESG investment products, emphasizing: Intentionality: The deliberate allocation of capital to achieve specific financial, environmental, and/or social outcomes. Measurability: The ability to quantify the outcomes to assess the effectiveness of the ESG strategies employed. Materiality: Focusing on ESG factors that are significant to the financial performance and long-term sustainability of the investment. Additionality: Ensuring that ESG investments lead to positive impacts beyond what would have occurred without the investment. By adopting this framework, the paper aims to reduce confusion and enhance the credibility and effectiveness of ESG-labeled investment products, ensuring they deliver on their promised financial, environmental, and social outcomes.
Also filed under Corporate Finance, Finance
Examination Priorities
U.S. Securities And Exchange Commission · Guide
It is not a rule, regulation, or statement of the U.S. Securities and Exchange Commission (SEC or Commission). The Commission has neither approved nor disapproved its content. This statement, like all staff statements, has no legal force or effect: it does not alter or amend applicable law, and it creates no new or additional obligations for any person.
Also filed under Finance
Financial Statement Analysis with Large Language Models
Alex G. Kim, Maximilian Muhn & Valeri V. Nikolaev · Report
Abstract We investigate whether an LLM can successfully perform financial statement analy- sis in a way similar to a professional human analyst. We provide standardized and anonymous financial statements to GPT4 and instruct the model to analyze them to determine the direction of future earnings. Even without any narrative or industry- specific information, the LLM outperforms financial analysts in its ability to predict earnings changes. The LLM exhibits a relative advantage over human analysts in sit- uations when the analysts tend to struggle.
Also filed under Machine Learning, Corporate Finance
FINTECH, DATA & ANALYTICS: Mergers & Acquisitions And Valuation Trends In The Public And Private Markets
D.A Davindson · Slides
"FINTECH, DATA & ANALYTICS: Mergers & Acquisitions and Valuation Trends in the Public and Private Markets" is a December 2021 report by D.A. Davidson & Co., authored by Ken Marlin, Vice Chairman of Tech Investment Banking. Key Insights: Resilience Amidst the Pandemic: The report highlights the surprising resilience of the FinTech, Data, and Analytics sectors during the COVID-19 pandemic, with global M&A activity rebounding to record levels in 2021. Strategic M&A Activity: Companies are actively engaging in M&A to expand customer bases, enhance product offerings, and achieve economies of scale. Buyers, including those with substantial cash reserves, are pursuing opportunities to add value beyond what standalone firms might achieve. Market Sustainability and Rationality: Despite high activity levels, the report suggests that the market operates on rational expectations of future performance and risk, with valuations grounded in sustainable business models and recurring revenue streams. Overall, the report provides a comprehensive analysis of the dynamic M&A landscape in the FinTech, Data, and Analytics sectors, emphasizing strategic growth, resilience, and rational market behaviors.
Also filed under Corporate Finance, Finance
Fixed Income Quantitative Research
Jeroen Kerkhof · Report
overview .......................................................................................................8 3. Euro area......................................................................................................................9 3.2. France........................................................................................................................10 3.3. United Kingdom........................................................................................................11 3.4.
Also filed under Quant Finance, Finance
Foundations of Portfolio Theory (Nobel Lecture)
Harry M. Markowitz · Report
Markowitz's 1990 Nobel Prize lecture presenting the foundations of portfolio theory in his own words — from the 1952 diversification principle through mean-variance optimization and the efficient frontier. Definitive primary-source treatment by the inventor of modern portfolio theory.
Also filed under Quant Finance, Finance
Hedge Fund Research Report 2021
SigTech · Slides
The "Hedge Fund Research Report 2021" by SigTech offers comprehensive insights into the evolving landscape of the hedge fund industry, focusing on quantitative strategies and asset allocation trends. The report is based on a survey of over 100 leading hedge fund managers, collectively overseeing assets under management (AUM) totaling approximately $231 billion across European, Asian, and North American markets. Key Findings: Increased Allocation to Quant Strategies: A significant 80% of hedge fund managers anticipate that institutional investors will boost their allocations to quantitative strategies within the next twelve months. Favorable Economic Conditions: Approximately 73% of respondents believe that the current economic and fiscal environment is conducive to the success of quantitative strategies. Growth in Quant Hedge Funds: An overwhelming 86% of managers expect an increase in the number of quantitative hedge funds over the next five years, indicating a strong trend toward systematic investment approaches. Data and Technology as Alpha Drivers: A substantial 95% of surveyed managers assert that access to high-quality data and cutting-edge technology is crucial for generating alpha, underscoring the industry's shift toward data-driven decision-making. Rise in Digital Asset Trading: About 85% of hedge fund managers anticipate an increase in trading activities involving digital assets, reflecting the growing acceptance and integration of cryptocurrencies and related instruments into investment portfolios. This report highlights a clear industry trend toward the adoption and expansion of quantitative investment strategies, driven by advancements in data accessibility and technological innovation. Hedge fund managers are optimistic about the future, recognizing the importance of leveraging these tools to enhance investment performance and meet the evolving demands of institutional investors.
Also filed under Quant Finance, Finance
How ESG Issues Become Financially Material to Corporations and Their Investors
David Freiberg, Jean Rogers, George Serafeim · Report
"How ESG Issues Become Financially Material to Corporations and Their Investors" by David Freiberg, Jean Rogers, and George Serafeim explores the growing significance of Environmental, Social, and Governance (ESG) factors in corporate financial performance. The book provides a comprehensive framework for understanding how ESG considerations transition from ethical concerns to financially material factors that influence investor decisions and corporate strategy. Key Highlights: Understanding ESG and Financial Materiality: Introduces ESG concepts and explains how they evolve into material financial factors that impact company performance, risk management, and long-term value creation. Framework for ESG Materiality: Discusses how ESG issues gain financial relevance based on industry context, regulatory changes, consumer preferences, and market trends. Empirical Evidence on ESG Impact: Presents research-backed insights on the correlation between strong ESG performance and financial outcomes such as profitability, cost of capital, and stock performance. Investor Perspective on ESG Integration: Explores how institutional investors and asset managers assess ESG risks and opportunities in making investment decisions. Case Studies of ESG Materiality in Action: Provides real-world examples of companies that have successfully integrated ESG factors into their business models, highlighting best practices and lessons learned. Future Trends in ESG Investing: Examines the evolving regulatory landscape, the role of data analytics in ESG measurement, and the increasing adoption of ESG reporting frameworks.
Also filed under Corporate Finance, Finance
Hull White Model for Stochastic Interest Rate Modeling
Mehul Mehta · Slides
• It is a type of short-rate model, which means it focuses on modeling the short-term interest rate, or the instantaneous rate of return on a risk-free investment.
Also filed under Quant Finance, Finance
Implementing Bermudan Swaption using QuantLib and Stochastic Models (Hull-White, Black- Karasinski and G2++) for Calibration in Python
Aaron de la Rosa · Notes
Definition and Features: • A swaption (swap + option) gives the holder the right, but not the obligation, to enter into an interest rate swap at specified terms. • In a Bermudan swaption, the holder has the right to start the swap on any of several predetermined dates within a specific period, known as "exercise dates." These dates are usually aligned with the reset dates of the swap's floating leg. • This flexibility to exercise on multiple dates differentiates Bermudan swaptions from European swaptions (exercisable only on a single date) and American swaptions (exercisable on any date up ...
Also filed under Quant Finance, Finance
Interest Rate and Credit Models
Andrew Lesniewski · Slides
"Interest Rate and Credit Models" is a comprehensive guide to the mathematical and financial theories behind interest rate modeling and credit risk assessment. The book delves into the stochastic processes, pricing methodologies, and risk management techniques used in modern fixed-income and credit markets. It is aimed at quantitative analysts, risk managers, financial engineers, and academics looking for a deep understanding of how interest rate and credit models function in practice. Key Topics Covered: Fundamentals of Interest Rate Models: Introduction to yield curves, discount factors, and the evolution of interest rate dynamics. Short Rate Models: Covers Vasicek, Cox-Ingersoll-Ross (CIR), and Hull-White models, explaining their applications in bond pricing and risk management. Market Models & Libor-Based Approaches: Discusses Libor Market Models (LMM), forward rate agreements, and swap pricing. Affine Term Structure Models: Explores the Heath-Jarrow-Morton (HJM) framework and its role in modeling yield curve movements. Credit Risk & Default Models: Examines structural and reduced-form models for credit spreads, default probabilities, and counterparty risk. Credit Derivatives & Securitization: Discusses credit default swaps (CDS), collateralized debt obligations (CDOs), and risk transfer mechanisms. Calibration & Numerical Methods: Covers Monte Carlo simulations, finite difference methods, and optimization techniques for model calibration. Risk Management & Practical Applications: Provides real-world insights into stress testing, risk-neutral pricing, and hedging strategies. This book serves as an advanced reference for professionals and researchers seeking a rigorous yet practical exploration of interest rate and credit risk modeling, offering a blend of mathematical depth and financial intuition.
Also filed under Quant Finance, Finance
Introduction to Bayesian Statistics
Brendon J. Brewer · Book
Introduction to Bayesian Statistics Brendon J. Brewer This work is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License. 5 1.2 This Version of the Notes . 12 3.1.2 Finding the Likelihood Values .
Also filed under Statistics, Quant Finance
Introduction to Statistical Concepts
William Astle · Slides
to think, understand, and form judgements logically.
Also filed under Statistics, Data Visualization
Large Language Models in Cybersecurity
Andrei Kucharavy, Octave Plancherel, Valentin Mulder, Alain Mermoud & Vincent Lenders · Book
This book is an open access publication. The images or other third party material in this book are included in the book’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the book’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. The use of general descriptive names, registered names, trademarks, service marks, etc.
Also filed under Machine Learning, Computer Science
Learning Algorithms and Market Manipulation
Álvaro Cartea · Slides
A learning algorithm consists of An objective, e.g., maximise profits, minimise costs. Offline with historical data Online as market evolves Both: offline and online What key aspects affect the performance of the learnt strategies?
Also filed under Quant Finance, Machine Learning
Lecture 2: Prediction
Alexandra Chouldechova · Slides
Lecture 2: Prediction Part I: Splines, Additive Models Part II: Model Selection and Validation Prof.
Also filed under Statistics, Machine Learning
Lecture 7: Value At Risk (VAR) Models
Ken Abbott · Book
The book "Lecture 7: Value At Risk (VAR) Models" by Ken Abbott provides an overview of Value at Risk (VAR) models, which are used to assess the potential loss in value of a portfolio over a defined period for a given confidence interval. It covers the theoretical foundations, methodologies, and practical applications of VAR, including historical simulation, variance-covariance, and Monte Carlo simulation approaches. The lecture also discusses the limitations and challenges of VAR models in risk management.
Also filed under Quant Finance, Finance
Low Latency Interest Rate Markets
Nicholas Burgess · Slides
Hospitals, Transport (HS2), Energy & Defence Projects Interest Rate Markets – Why the need for Speed? 4 Cleared Electronic Trading & Auto-Hedging Real-Time, Highly Liquid & High Precision (Bid-Offer 1/10th bps i.e. USD 10 per MM) Trading Horizon: High Frequency Trading (HFT) vs Long-Term Fund Performance
Also filed under Finance
Model Management Guidance
Central Bank of the U.A.E. · Guide
3/70 CBUAE Classification: Public TABLE OF CONTENTS Definitions and interpretations .................................................................................................. 5 1 Context and Objective ...................................................................................................... 11 1.1 Regulatory context .................................................................................................... 11 1.2 Objectives .................................................................................................................
Also filed under Software Engineering, Quant Finance
Modern Portfolio Theory: Some Main Results
Heinz H. Müller · Report
ASTIN Bulletin survey article covering the main results of modern portfolio theory, the Sharpe-Lintner CAPM, and Roll's critique. Accessible treatment of the mathematical foundations.
Also filed under Quant Finance, Finance
Multilevel Monte Carlo Simulation Using Terminal Stratification
Yuquan Li · Notes
Abstract This thesis improves the multilevel Monte Carlo simulation introduced in Giles [2008a] for option pricing. We use stratified sampling on the initial level and thus obtain a further variance reduction. Then we programme the whole procedure of option pricing using this algorithm by C++. Chapter 1 introduces the mathematical background needed for under- standing multilevel Monte Carlo methods.
Also filed under Quant Finance, Statistics
Multivariate GARCH (MGARCH) under Dynamic Conditional Correlation (DCC) specification in Python.
Aaron de la Rosa · Notes
It allows the conditional-on-past-history covariance matrix of the dependent variables to follow a flexible dynamic structure. This means that MGARCH models can capture the time-varying volatility and co-movements in multiple time series. Financial institutions typically use them to estimate the volatility of returns for stocks, bonds, and market indices. DCC (Dynamic Conditional Correlation): DCC is a specific type of MGARCH model.
Also filed under Finance, Data Visualization
Odds & Ends
Jonathan Weisberg · Book
A visual approach to understanding probability concepts, covering how odds relate to likelihood, Bayes' theorem, conditional probability, and common statistical distributions. Uses diagrams and intuitive examples to make abstract probability theory more accessible.
Also filed under Finance
Pairs Trading
Author Unknown · Report
Pairs Trading Pairs trading is a market neutral trading strategy that involves buying and selling two highly correlated financial instruments simultaneously. The idea is to profit from the difference in price movements between the two instruments. Pairs trading is often used in the stock market, but can also be applied to other markets such as futures and options. The strategy is based on the idea that while individual stocks may be affected by company - specific or market - wide events, the relative relationship between two highly correlated stocks will remain relatively stable over time.
Also filed under Quant Finance, Finance
Pricing Commodity Options Using Monte Carlo Simulation with Python
Nikita Lavrentyev · Notes
Introduction to Option Pricing in Commodity Markets As oil prices fluctuate amid uncertainty surrounding the Middle East and U.S. elections, traders are reminded of how unpredictable the energy markets can be. Commodity markets, such as oil, are particularly susceptible to sudden price swings due to a range of external factors, including geopolitical tensions, supply chain disruptions, and shifts in global demand. In such a volatile environment, accurately pricing options is crucial for managing risk and protecting against sudden price shifts.
Also filed under Quant Finance, Python
Principal Component Analysis for IFRS9 Forward-Looking Modeling
Andrija Djurovic · Slides
After reducing the data dimensionality, the selected principal components typically serve as inputs for the regression model. In the context of IFRS9 forward-looking modeling, PCA is a notable approach practitioners employ. PCA addresses a significant challenge in forward-looking modeling exercises: the relatively low ratio between the number of observations and the number of independent variables. Despite its popularity, is PCA always the optimal solution?
Also filed under Corporate Finance, Quant Finance
Production of U.S. Rm-Rf, SMB, and HML in the Fama-French Data Library
Eugene F. Fama & Kenneth R. French · Report
"Production of U.S. Rm-Rf, SMB, and HML in the Fama-French Data Library" is a working paper by Eugene F. Fama and Kenneth R. French that delves into the methodologies and data adjustments involved in constructing key financial factors used in asset pricing models. Key Insights: Factor Definitions: Rm-Rf (Market Excess Return): The return on the value-weighted portfolio of all NYSE, AMEX, and NASDAQ stocks minus the one-month U.S. Treasury bill rate. SMB (Small Minus Big): The return difference between small-cap and large-cap stocks, serving as a proxy for the size effect. HML (High Minus Low): The return difference between stocks with high and low book-to-market ratios, representing the value effect. Data Corrections and Rule Changes: The paper analyzes how various data corrections and rule modifications have impacted the returns of these factors. Understanding these effects is crucial for researchers and practitioners who rely on the Fama-French Data Library for empirical analyses. Methodological Transparency: By detailing the construction processes and the influence of data adjustments, the authors aim to enhance transparency and assist users in accurately interpreting factor returns derived from the library. This paper serves as a valuable resource for those utilizing the Fama-French Data Library, providing clarity on the construction and evolution of widely used financial factors.
Also filed under Finance, Corporate Finance
Quantitative Forecasting Models and Active Diversification for International Bonds
Antti Ilmanen & Rafey Sayood · Report
We review the performance of increas- ingly complex yet quite straightforward and transparent trading strategies. We first use single indicators to predict specific trades. We then pool these indicators into a multipredictor fore- casting model for each trade, and finally diver- sify across several trades. The success of these quantitative trading strategies rests on the twin pillars of the lim- ited forecastability of returns and diversifica- tion across strategies.
Also filed under Quant Finance, Finance
Reinforcement Learning for Corporate Bond Trading: A Sell Side Perspective
Samuel Atkins, Ali Fathi & Sammy Assef · Report
Abstract A corporate bond trader in a typical sell side institution such as a bank provides liquidity to the market participants by buying/selling securities and maintaining an inventory. Upon receiving a request for a buy/sell price quote (RFQ), the trader provides a quote by adding a spread over a prevalent market price. For illiquid bonds, the market price is harder to observe, and traders often resort to available benchmark bond prices (such as MarketAxess, Bloomberg, etc.). In [BG], the concept of Fair Transfer Price for an illiquid corporate bond was introduced which is derived from...
Also filed under Quant Finance, Finance
Risk-Neutral Pricing: An Intuitive Approach
Pablo Marchesi · Notes
Abstract This paper aims to provide a straightforward and intuitive introduction to Risk- Neutral Pricing for derivative securities. We begin by reviewing foundational concepts from Itˆo calculus, including Itˆo processes and martingales, along with the key theorems necessary for deriving the risk-neutral pricing formula. We then proceed to derive the formula, prioritizing intuition over mathematical rigor. We can define arbitrage as a trading strategy that begins with no money, has zero probability of losing money, and has a positive probability of mak- ing money [2].
Also filed under Finance, Quant Finance
Rossmann Sales Prediction Using Supervised Models
Nikita Prasad · Report
No encontrado
Also filed under Machine Learning, Quant Finance
SEC Reporting Obligations Under Section 13 and Section 16 of the Exchange Act
Arthur L. Zwickel & Alicia M. Harrison. · Report
"SEC Reporting Obligations Under Section 13 and Section 16 of the Exchange Act" by Arthur L. Zwickel and Alicia M. Harrison provides a comprehensive overview of the reporting requirements mandated by the Securities Exchange Act of 1934. This legal update is essential for individuals and entities involved in owning, managing, or trading publicly traded or exchange-listed equity securities. Key Highlights: Section 13 Reporting Requirements: Beneficial Ownership Reporting: Entities or individuals that directly or indirectly own more than 5% of a class of voting equity securities registered under Section 12 of the Exchange Act are required to file reports on Schedule 13D or Schedule 13G. Institutional Investment Managers: Managers exercising investment discretion over accounts holding equity securities with an aggregate fair market value of $100 million or more must file quarterly reports on Form 13F. Large Trader Reporting: Persons or entities that trade significant amounts of NMS securities are required to file Form 13H to provide identifying information to the SEC. Section 16 Reporting Requirements: Insider Reporting: Directors, officers, and beneficial owners of more than 10% of any class of equity security registered under Section 12 must file reports disclosing their ownership and transactions. Forms Required: Form 3: Initial statement of beneficial ownership, filed within 10 days of becoming an insider. Form 4: Reports changes in ownership, filed within two business days following the transaction. Form 5: Annual statement of beneficial ownership, filed to report transactions not previously reported. This update serves as a vital resource for understanding the complexities of SEC reporting obligations, ensuring compliance, and avoiding potential penalties associated with non-compliance.
Also filed under Corporate Finance, Finance
Special Note Long/Short US Portfolio
Damien Cleusix · Slides
No encontrado
Also filed under Finance
STAT0029 Statistical Design Of Investigations
Department of Statistical Science University College London · Book
Introduction 4 1.1 Why design? 4 1.3 Experimentation versus sampling . 8 2.1.1 Principles of experimental design . 8 2.1.2 Planning of experiments .
Also filed under Statistics, Software Engineering
Statistical Analysis Handbook
Michael J de Smith · Book
The moral right of the authors has been asserted. Copies of this edition are available in electronic book and web-accessible formats only. Disclaimer: This publication is designed to offer accurate and authoritative information in regard to the subject matter. It is provided on the understanding that it is not supplied as a form of professional or advisory service.
Also filed under Statistics, Data Visualization
Step-by-Step Calibration of the Option Pricing & Rates Models
Amit Kumar Jha · Report
Introduction In this short pdf, I am breaking down the calibration of option pricing models and interest rate models step by step, focusing on clear and practical methods for understanding and implementing these processes. We’ll cover key models such as the Black-Scholes model, LV model, Vasicek model, CIR and HW Model. Data Collection • Market Prices of Options (Cmarket): These are the observed prices of options traded in the market. We collect these because they represent the ”true” value of the options under current market conditions.
Also filed under Quant Finance, Finance
The 2023 State of Corporate ESG
Thomson Reuters Institute · Report
The book "The 2023 State of Corporate ESG" by Thomson Reuters Institute provides an analysis of the current trends, challenges, and opportunities in corporate Environmental, Social, and Governance (ESG) practices. It examines how companies are integrating ESG factors into their strategies, the impact of regulatory changes, and the role of stakeholders in driving ESG initiatives. The book also highlights case studies and best practices from leading organizations to offer insights into effective ESG implementation.
Also filed under Corporate Finance, Finance
The Capital Asset Pricing Model: Some Empirical Tests
Fischer Black, Michael C. Jensen, and Myron Scholes · Report
The classic 1972 empirical test of the CAPM that first documented the low-volatility anomaly — a persistent premium to low-beta stocks that standard CAPM cannot explain. Foundational reading for anyone working with factor models or systematic risk.
Also filed under Quant Finance, Finance
The Capital Asset Pricing Model: Theory and Evidence
Eugene F. Fama and Kenneth R. French · Report
A seminal survey paper reviewing the CAPM from its theoretical foundations through the empirical record. Fama and French document the failures of the Sharpe-Lintner model in explaining cross-sectional returns and motivate the three-factor extension. Essential reading for anyone using beta, alpha, or factor-based attribution.
Also filed under Quant Finance, Finance
The Evolution of Pairs Trading
Author Unknown · Slides
A study of how pairs trading strategies have evolved since their origins at Morgan Stanley in the 1980s. It traces the development from simple correlation-based approaches to modern cointegration and algorithmic methods used in statistical arbitrage.
Also filed under Quant Finance, Finance
Time Series Analysis
Andrew Lesniewski · Slides
We have seen that a stationary time series in the ARMA(p, q) family can be written in the moving average (MA) form: Xt = µ + εt + γ1εt−1 + γ2εt−2 . = µ + γ(L)εt, (1) where L is the lag operator, and where P∞ j=1 |γj| < ∞. Stationary series are rather unusual in finance, and hence the need for developing models that capture the non-stationary nature of financial time series. There are various approaches to model non-stationarity.
Also filed under Data Visualization, Python
Time-weighted volatility
Peter Jackel · Report
An inadvertent consequence to this convention is that each day is assigned the same amount of future daily variance of the underlying financial asset’s future spot realisation. This, alas, does not reflect the real world. Whilst this approach is reasonably well known among praction- ers, it rarely appears in the literature, one exception be- ing [SV00]. For the respective trading desks, the BUS252 volatility day count convention is pragmatic and easy to use, though it has its drawbacks.
Also filed under Quant Finance, Finance
Understanding and Managing Complexity Risk
Eric Bonabeau · Report
about the aircraft’s speed and acceleration. This confused the flight computers, which sent the Boeing 777 on a 3,000-foot roller-coaster ride. With more than five million lines of code, aircraft software programs have become too large and complex to be tested thoroughly and are fielded without any guarantee that they will always work. Comment on this article or contact the author through smrfeedback@mit.edu.
Also filed under Finance, Quant Finance
Validation of Credit Risk Models
Andrija Djurovic · Slides
Validation of Credit Risk Models Does the P-Value Provide Sufficient Insight for Model Validation? The p-value resulting from statistical hypothesis testing is often the sole criterion used in reaching a final conclusion. Relying solely on the p-value raises several questions, such as: Should practitioners adopt a unified approach based on the p-value for all portfolio types? Should practitioners adopt a unified approach based on the p-value for all test types?
Also filed under Quant Finance, Finance
Valuation
Aswath Damodaran · Slides
The only questions are how much and in which direction. • Truth 1.2: The direction and magnitude of the bias in your valuation is directly proportional to who pays you and how much you are paid. The more quantitative a model, the better the valuation • Truth 3.1: One’s understanding of a valuation model is inversely proportional to the number of inputs required for the model. • Truth 3.2: Simpler valuation models do much better than complex ones.
Also filed under Corporate Finance, Quant Finance
Valuation Handbook-International Guide To Cost Of Capital
James P. Harrington, Carla S. Nunes, Anas Aboulamer, Roger J. Grabowski · Book
"Valuation Handbook—International Guide to Cost of Capital," authored by James P. Harrington, Carla S. Nunes, Anas Aboulamer, and Roger J. Grabowski, is a comprehensive resource for estimating the cost of capital across various international markets. This guide is essential for professionals involved in business valuation, investment analysis, and financial decision-making, offering methodologies and data to assess the cost of equity capital globally. Key Highlights: Global Cost of Capital Estimation: The handbook provides methodologies to estimate country-level cost of equity capital for over 180 countries, incorporating factors like country risk premia, equity risk premia, and relative volatility. Data-Driven Insights: It offers access to international data that is often costly and difficult to obtain, assembling it into easy-to-use inputs for cost of capital calculations. Practical Application: The guide translates complex concepts and methodologies into practical applications, supported by exemplifying cases that demonstrate their real-world usage. Updated Content: The 2023 Summary Edition includes updated content and all methodologies previously published, ensuring that readers have access to the most current data and practices in cost of capital estimation. This handbook serves as a vital tool for professionals seeking to understand and apply cost of capital concepts in international contexts, enhancing their ability to make informed financial decisions.
Also filed under Corporate Finance, Finance
Valuation of Cryptoassets: A Guide for Investment Professionals
Urav Soni & Rhodri Preece · Report
"Valuation of Cryptoassets: A Guide for Investment Professionals" by Urav Soni and Rhodri Preece provides a comprehensive framework for understanding and evaluating cryptocurrencies and blockchain-based assets. This guide serves as an essential resource for investment professionals who are looking to navigate the complexities of the rapidly growing crypto market, offering a balanced and methodical approach to assessing the value of cryptoassets. Key Highlights: Introduction to Cryptoassets: The book begins with a clear introduction to the world of cryptoassets, distinguishing between cryptocurrencies (such as Bitcoin and Ethereum) and other types of blockchain-based assets, such as tokenized securities, utility tokens, and non-fungible tokens (NFTs). It outlines the key differences between traditional financial assets and digital assets, helping readers understand the unique characteristics of crypto investments. Valuation Framework: Soni and Preece present a systematic framework for valuing cryptoassets, which integrates traditional financial valuation models with the unique factors that affect digital currencies. The authors emphasize the importance of understanding the underlying blockchain technology, the token's utility or use case, and market demand when assessing the value of cryptoassets. Valuation Methods: The book explores various methods for valuing cryptoassets, including cost-based, income-based, and market-based approaches. These methods are adapted to account for the decentralization, volatility, and speculative nature of digital assets. The authors also discuss the limitations and challenges of applying traditional valuation techniques to the crypto world and highlight alternative metrics like network value and on-chain data analysis. Market Sentiment and Network Effects: A significant portion of the book focuses on understanding the role of market sentiment, network effects, and user adoption in the valuation of cryptoassets. The authors argue that the value of a cryptocurrency is highly influenced by the size of its user base, transaction volume, and the strength of its underlying community, as well as broader market trends and investor sentiment. Risk Factors: Cryptoassets are known for their high volatility and regulatory uncertainty. The book addresses various risk factors that can impact valuations, such as regulatory changes, technological risks, security concerns, and market liquidity. The authors provide practical strategies for mitigating risks and managing cryptoasset investments in a highly unpredictable market environment. Investor Considerations: Soni and Preece provide valuable insights for investment professionals regarding how to approach cryptoassets as part of an investment portfolio. They discuss the potential for diversification, the importance of due diligence, and the role of cryptocurrencies in hedging against traditional asset class risks. The book also explores the evolving regulatory landscape, emphasizing the importance of compliance for institutional investors. Case Studies and Real-World Applications: To further illustrate their valuation concepts, the authors include several case studies of well-known cryptocurrencies and blockchain projects. These case studies help to contextualize the valuation methods in real-world scenarios and demonstrate how the theories discussed in the book can be applied to practical investment decisions.
Also filed under Corporate Finance, Quant Finance
Volatility & Greeks: Nvidia Option
Author Unknown · Notes
Notes on analyzing NVIDIA stock options using the volatility Greeks: Delta, Gamma, Theta, Vega, and Rho. Explains how implied volatility and these sensitivity measures help traders assess risk and price movement expectations for options positions.
Also filed under Quant Finance, Finance
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