Time Series Analysis and Understanding a Eurobond Fund with Prophet
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The Eurobonds are so popular in Turkey nowadays. The reason for this is that the CDS of Turkey fell for quite a while since the new economic outlook began, according to the economists. We will look into the factors that shape this trend in this article.
First, we build our data set with the components that we think are related to the Turkish Eurobonds.
library(tidyverse) library(tidymodels) library(timetk) library(modeltime) library(modelStudio) #GPA(Eurobond Debt Instruments (FX) Fund of a private Turkish bank) df_gpa <- read_csv("https://raw.githubusercontent.com/mesdi/blog/main/gpa.csv") df_gpa_tidy % janitor::clean_names() %>% mutate(date = parse_date(date, "%m/%d/%Y")) %>% select(date, gpa = price) #CDS(Turkey CDS 5 Years USD) df_cds <- read_csv("https://raw.githubusercontent.com/mesdi/blog/main/cds.csv") df_cds_tidy % janitor::clean_names() %>% mutate(date = parse_date(date, "%m/%d/%Y")) %>% select(date, cds = price) #USDTRY(US Dollar Turkish Lira) df_usdtry <- read_csv("https://raw.githubusercontent.com/mesdi/blog/main/usdtry.csv") df_usdtry_tidy % janitor::clean_names() %>% mutate(date = parse_date(date, "%m/%d/%Y")) %>% select(date, usdtry = price) #Federal Funds Effective Rate df_fedfunds <- read_csv("https://raw.githubusercontent.com/mesdi/blog/main/fedfunds.csv") ##Converting Fed Funds data from monthly to weekly library(padr) df_fedfunds_tidy % janitor::clean_names() %>% thicken("week") %>% pad_by_time(date_week, .by = "week") %>% mutate(across(fedfunds, .fns = (x) ts_impute_vec(x, period = 1))) %>% select(date = date_week, fedfunds) #Transforming the variables to PPP (Purchasing power parities) #to see the changes(%) relative the last point df_tidy % mutate(gpa = (gpa/first(gpa)*100) %>% round(2), cds = (cds/first(cds)*100) %>% round(2), usdtry = (usdtry/first(usdtry)*100) %>% round(2), fedfunds = (fedfunds/first(fedfunds)*100) %>% round(2))
Now that we have created our data set, we can examine the anomalies of the variables.
#Anomaly df_all %>% pivot_longer(cols = -date, names_to = "vars") %>% plot_anomaly_diagnostics(date, value, .interactive = FALSE, .facet_vars = vars, .facet_ncol = 2)
We can see that all variables but cds
have had a lot of anomalies in the last few years. So we will choose the prophet model which is robust to outliers. Before moving on to our modeling, we will analyze the relations between all variables with the clustering method.
#Clustering df_tsfeature % pivot_longer(cols = -date, names_to = "id") %>% group_by(id) %>% tk_tsfeatures( .date_var = date, .value = value, .period = 52, .features = c("frequency", "stl_features", "entropy", "acf_features", "mean"), .scale = TRUE, .prefix = "ts_" ) %>% ungroup() set.seed(123) tibble( cluster = df_tsfeature %>% select(-id) %>% as.matrix() %>% kmeans(centers = 3, nstart = 100) %>% pluck("cluster") ) %>% bind_cols( df_tsfeature ) %>% select(cluster, id) %>% right_join(df_all %>% pivot_longer(cols = -date, names_to = "id") , by = "id") %>% group_by(id) %>% plot_time_series( date, value, .color_var = cluster, .facet_ncol = 2, .interactive = FALSE, .title = "" ) + theme(strip.text = ggtext::element_markdown(face = "bold"))
We can say that the gpa
and usdtry
variables are in the same cluster and look very similar. I wonder if we will see it in our model as well.
#Split into a train and test set splits % timetk::time_series_split(assess = "1 year", cumulative = TRUE) train <- training(splits) test <- testing(splits) #Modeling df_rec <- recipe(gpa ~ ., data = train) df_spec % set_engine(engine = "prophet") set.seed(12345) df_wflow_fit % add_recipe(df_rec) %>% add_model(df_spec) %>% fit(train) #Calibration data df_cal % modeltime_accuracy(metric_set = metric_set(rmse,rsq)) # A tibble: 1 × 5 # .model_id .model_desc .type rmse rsq # #1 1 PROPHET W/ REGRESSORS Test 2.40 0.989
Based on the rsq and rmse values, our model looks very good. Now, we can calculate the variable importance values based on the model with the modeltStudio package. This will pop up the interactive dashboard on your browser, so you will be able to select variables on the right bottom for partial dependence.
#VIP(variable importance) ##Explainer object library(DALEXtra) explainer % select(-gpa), y = train$gpa, label = "Prophet" ) ##Model Studio set.seed(1983) modelStudio::modelStudio(explainer, B = 100, viewer = "browser")
Despite what economists say, the CDS(cds
) seems to have little effect on the fund based on the dashboard. Besides, it looks like the most dominant effect is the USD/TRY(usdtry
) rates’ and it has a strong linear relation to the GPA(gpa
) fund. This confirms the inference we found earlier with clustering.
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