El dashboard

DASHBOARD TERRY MADARIAGA

Financial Risk Analytics involves the systematic use of statistical and mathematical techniques to assess and manage financial risks within various contexts, such as banking, investment management, insurance, and corporate finance. This field is crucial for organizations to understand and mitigate potential financial losses stemming from market fluctuations, credit defaults, operational failures, and other unforeseen events. Overview of Financial Risk Analytics Types of Financial Risks: Market Risk: Arises from changes in market prices, such as stocks, bonds, commodities, and currencies. Credit Risk: Potential losses due to default by borrowers or counterparties. Operational Risk: Risks from internal processes, systems, human errors, and external events. Liquidity Risk: Concerns the ability to quickly convert assets into cash without loss. Importance of Financial Risk Analytics: Risk Measurement: Quantifies risks using models like Value-at-Risk (VaR), stress testing, and scenario analysis. Risk Management: Helps in devising strategies to mitigate risks, allocate capital effectively, and comply with regulatory requirements. Decision Support: Provides insights for investment decisions, hedging strategies, and overall financial planning. Techniques and Models: Statistical Analysis: Utilizes probability distributions, correlation analysis, and regression to model risks. Machine Learning: Applies algorithms to identify patterns, forecast market movements, and detect anomalies. Simulation Methods: Monte Carlo simulation for assessing the impact of uncertain events on portfolios. Optimization Techniques: Mathematical models to optimize asset allocation and risk-adjusted returns. Tools and Software: Risk Management Systems: Integrated platforms for risk assessment, reporting, and compliance. Data Analytics Platforms: Utilize big data frameworks and analytics tools for processing large datasets. Visualization Tools: Dashboards and reporting tools for visual representation of risk metrics. Challenges: Data Quality: Ensuring accuracy and reliability of data inputs for risk models. Model Validation: Assessing the effectiveness and reliability of risk models under various scenarios. Regulatory Compliance: Adhering to regulatory requirements such as Basel III, Solvency II, and IFRS 9. Dynamic Environment: Adapting to changing market conditions and emerging risks. Applications: Financial Institutions: Banks, investment firms, and insurance companies use risk analytics to manage portfolios and assess creditworthiness. Corporate Finance: Helps in managing currency exposures, interest rate risks, and operational risks. Government and Regulatory Bodies: Monitor systemic risks and enforce regulatory standards. In conclusion, Financial Risk Analytics plays a pivotal role in modern finance by providing insights into potential risks, enabling proactive risk management strategies, and supporting informed decision-making in an increasingly complex financial landscape.

<!-- R Commander Markdown Template --> Credit Resic Return ======================= ### DR.Edirdiri Fadol Ibrahim Fadol Scientific Research Center) ### `r as.character(Sys.Date())` ```{r echo=FALSE} # include this code chunk as-is to set options knitr::opts_chunk$set(comment=NA, prompt=TRUE, out.width=750, fig.height=8, fig.width=8) library(Rcmdr) library(car) library(RcmdrMisc) ``` ```{r} # Load necessary libraries library(quantmod) library(ggplot2) ``` ```{r} # Step 1: Fetch historical stock prices using quantmod package ticker <- "AAPL" # Example: Apple Inc. start_date <- "2021-01-01" end_date <- "2021-12-31" ``` ```{r} getSymbols(ticker, from = start_date, to = end_date) ``` ```{r} # Step 2: Extract adjusted closing prices stock_prices <- Ad(get(ticker)) ``` ```{r} # Step 3: Calculate daily returns daily_returns <- diff(log(stock_prices)) ``` ```{r} # Step 4: Calculate key statistics mean_return <- mean(daily_returns, na.rm = TRUE) volatility <- sd(daily_returns, na.rm = TRUE) ``` ```{r} cat("Mean Daily Return:", mean_return, "\n") cat("Volatility (Standard Deviation of Daily Returns):", volatility, "\n") ``` ```{r} # Step 5: Visualize daily returns dates <- index(daily_returns) returns_data <- data.frame(Date = as.Date(dates), Daily_Return = as.numeric(daily_returns)) ``` ```{r} ggplot(returns_data, aes(x = Date, y = Daily_Return)) + geom_line(color = "blue") + labs(title = paste("Daily Returns of", ticker), x = "Date", y = "Daily Returns") + theme_minimal() ``` ```{r} ### Summarize Data Set: returns_data ``` ```{r} summary(returns_data) ``` ### Normality Test: ~Daily_Return ```{r} normalityTest(~Daily_Return, test="shapiro.test", data=returns_data) ``` ### Normality Test: ~Daily_Return ```{r} normalityTest(~Daily_Return, test="shapiro.test", data=returns_data) ``` ```{r} library(abind, pos=23) ``` ```{r} library(e1071, pos=24) ``` ### Numerical Summaries: returns_data ```{r} numSummary(returns_data[,"Daily_Return", drop=FALSE], statistics=c("mean", "sd", "IQR", "quantiles"), quantiles=c(0,.25,.5,.75,1)) ``` ### Numerical Summaries: returns_data ```{r} numSummary(returns_data[,"Daily_Return", drop=FALSE], statistics=c("mean", "sd", "IQR", "quantiles"), quantiles=c(0,.25,.5,.75,1)) ``` ### Single-Sample t-Test: Daily_Return ```{r} with(returns_data, (t.test(Daily_Return, alternative = "two.sided", mu = 0.0, conf.level = .95))) ``` ### Single-Sample t-Test: Daily_Return ```{r} with(returns_data, (t.test(Daily_Return, alternative = "two.sided", mu = 0.0, conf.level = .95))) ```

Este proyecto tiene como objetivo analizar las tasas de homicidios globales utilizando diferentes tipos de indicadores socioeconómicos recopilados de diversas fuentes como el Banco Mundial (World Bank), World Population Review y World Justice Project. Dada la limitación de datos recientes, este análisis se centrará en 66 países seleccionados.

Importing and Cleaning eammi2 data