A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
IUS framework predicts EUR/USD exchange rate with improved accuracy.
Simple models outperformed sophisticated ones in forecasting Turkish lira exchange rates.
Neural ARFIMA model improves exchange rate forecasting for BRIC economies.
This paper models yearly exchange rates between USD/KZT, EUR/KZT and SGD/KZT, and compares the actual data with developed forecasts using time series analysis over the period from 2006 to 2014. The official yearly data of National Bank of the Republic of Kazakhstan is used for present study. The main goal of this paper…
This paper compares traditional econometric and contemporary machine/deep learning techniques for forecasting foreign exchange rates.
Study improves exchange rate forecasting using machine learning and interpretable methods.
This paper reports empirical evidence that a neural networks model is applicable to the statistically reliable prediction of foreign exchange rates. Time series data and technical indicators such as moving average, are fed to neural nets to capture the underlying "rules" of the movement in currency exchange rates. The …
EXFormer predicts foreign exchange returns with high accuracy using a multi-scale self-attention mechanism and dynamic variable selection.
Any discussion on exchange rate movements and forecasting should include explanatory variables from both the current account and the capital account of the balance of payments. In this paper, we include such factors to forecast the value of the Indian rupee vis a vis the US Dollar. Further, factors reflecting political…
Paper uses machine learning to forecast significant currency exchange rate fluctuations.
The paper models exchange rate risk premium using mean-reverting dynamics.
Study improves forex forecasting accuracy using machine learning models.
RegPred Net forecasts foreign exchange rates with improved accuracy and interpretability.
Fast, global, and sensitively reacting to political, economic and social events of any kind, these are attributes that social media like Twitter share with foreign exchange markets. The leading assumption of this paper is that information which can be distilled from public debates on Twitter has predictive content for …
An expanding literature articulates the view that Taylor rules are helpful in predicting exchange rates. In a changing world however, Taylor rule parameters may be subject to structural instabilities, for example during the Global Financial Crisis. This paper forecasts exchange rates using such Taylor rules with Time V…
Paper adapts ACI for online multi-step time-series forecasting with coverage guarantees.
The team predicts foreign exchange rates using clustering and attention models.
This paper proposes an enhanced approach to modeling and forecasting volatility using high frequency data. Using a forecasting model based on Realized GARCH with multiple time-frequency decomposed realized volatility measures, we study the influence of different timescales on volatility forecasts. The decomposition of …
Proposes a new jackknife method for time series hyperparameter selection.
The Efficient Market Hypothesis (EMH) is widely accepted to hold true under certain assumptions. One of its implications is that the prediction of stock prices at least in the short run cannot outperform the random walk model. Yet, recently many studies stressing the psychological and social dimension of financial beha…
The paper identifies key macroeconomic events affecting exchange rate volatility.
Study improves cryptocurrency volatility forecasting using multiple data sources.
Currency volatility shocks predict lower excess returns, and buying weak transmitters outperforms selling strong ones.
Dynamic functional time-series methods improve forecast accuracy for foreign exchange implied volatility surfaces.
This paper proposes new GARCH models for cryptocurrency volatility, showing skewed distributions improve prediction accuracy.
Temporal mixture ensemble predicts cryptocurrency exchange volumes better than traditional methods.
Adaptive Conformal Inference improves time series forecasting uncertainty.
Recent literature seek to forecast implied volatility derived from equity, index, foreign exchange, and interest rate options using latent factor and parametric frameworks. Motivated by increased public attention borne out of the financialization of futures markets in the early 2000s, we investigate if these extant mod…
Comparative study of neural networks for short-term FOREX forecasting.
New fast estimation methods stemming from control theory lead to a fresh look at time series, which bears some resemblance to "technical analysis". The results are applied to a typical object of financial engineering, namely the forecast of foreign exchange rates, via a "model-free" setting, i.e., via repeated identifi…
Time series analysis and forecasting of stock market prices has been a very active area of research over the last two decades. Availability of extremely fast and parallel architecture of computing and sophisticated algorithms has made it possible to extract, store, process and analyze high volume stock market time seri…
In this paper we develop a Bayesian procedure for estimating multivariate stochastic volatility (MSV) using state space models. A multiplicative model based on inverted Wishart and multivariate singular beta distributions is proposed for the evolution of the volatility, and a flexible sequential volatility updating is …
Develops BPDS for better financial portfolio decisions.
We present a method for conditional time series forecasting based on an adaptation of the recent deep convolutional WaveNet architecture. The proposed network contains stacks of dilated convolutions that allow it to access a broad range of history when forecasting, a ReLU activation function and conditioning is perform…
This paper improves risk control for financial markets by calibrating VaR forecasts using conformal methods.
The purpose of this paper is to propose a time-varying vector autoregressive model (TV-VAR) for forecasting multivariate time series. The model is casted into a state-space form that allows flexible description and analysis. The volatility covariance matrix of the time series is modelled via inverted Wishart and singul…
Deep learning optimizes portfolio Sharpe ratio without forecasting returns.
AEnbMIMOCQR generates robust multi-step ahead prediction intervals for time series data.
Interpretable AI model boosts investment confidence and profitability.
Study finds relevance of exchange and inflation rates to economic factors.
This paper introduces a novel recalibration method for multivariate forecasts.
We first show that there are in fact triangular arbitrage opportunities in the spot foreign exchange markets, analyzing the time dependence of the yen-dollar rate, the dollar-euro rate and the yen-euro rate. Next, we propose a model of foreign exchange rates with an interaction. The model includes effects of triangular…
Transformer-based models overfit financial time series data, leading to increased prediction variance.
We introduce an autoregressive-type model with self-modulation effects for a foreign exchange rate by separating the foreign exchange rate into a moving average rate and an uncorrelated noise. From this model we indicate that traders are mainly using strategies with weighted feedbacks of the past rates in the exchange …
The multi dimensional string objects are introduced as a new alternative for an application of string models for time series forecasting in trading on financial markets. The objects are represented by open string with 2-endpoints and D2-brane, which are continuous enhancement of 1-endpoint open string model. We show ho…
We use techniques from network science to study correlations in the foreign exchange (FX) market over the period 1991--2008. We consider an FX market network in which each node represents an exchange rate and each weighted edge represents a time-dependent correlation between the rates. To provide insights into the clus…
LSTM model predicts stock prices with high accuracy in stable sectors but struggles with volatile ones.
New method recalibrates VaR for option books, reducing forecast errors.