International Journal of Financial Markets and Derivatives (4 papers in press)

Regular Issues

• An empirical analysis of the causal relationship between the Indian stock market and the foreign exchange market  
  by Khushboo Khushboo, Kailash Chandra Pradhan 
  Abstract: This paper empirically explores the dynamic causal relationship between the Indian stock market and foreign exchange market from 2000 to 2024, using daily data of Nifty 50 and three core sectoral indices (Nifty Bank, Nifty FMCG, Nifty IT) alongside the RBI USD/INR reference rate. Adopting time series econometric methods including unit root tests, Johansen cointegration analysis, VECM, variance decomposition and impulse response analysis, the study verify a significant long run unidirectional causal relationship from exchange rates to stock prices, which supports the goods market theory/flow oriented approach. In the short run, a bidirectional causal relationship exists between the two markets, with Nifty 50 and Nifty Bank exhibiting far stronger linkages with exchange rates (explaining 0.19% of exchange rate variation by day 10) than Nifty FMCG (0.06%) and Nifty IT (0.02%). Notably, Nifty IT shows a unique positive response to exchange rate shocks due to its export oriented characteristics, while other indices present negative responses. The study further concludes that exchange rate fluctuations exert a more substantial and persistent influence on stock indices than the reverse, and puts forward targeted policy implications for monetary regulation, sectoral risk management and cross market volatility control.
  Keywords: financial market; exchange rate; Nifty indices; vector error correction model; VECM.
  DOI: 10.1504/IJFMD.2026.10080729
   
  
• Do bondholders care about ESG and sentiments? Influence of ESG and investor sentiment on bond yield spread prediction using machine learning  
  by Sougata Banerjee, Kamran Quddus 
  Abstract: This study examines whether investor sentiment and ESG news sentiment influence corporate bond yield spreads and improve forecasting accuracy. Using U.S. corporate bond transaction data from 2015 to 2024, the study combines bond-market variables with ESG news sentiment and investor sentiment derived from financial news and social media. Long short-term memory (LSTM) and Bayesian ridge regression (BRR) models are used to assess predictive performance and the incremental role of text-derived sentiment signals. The results show that ESG news sentiment and investor sentiment are significantly associated with yield spreads, with environmental sentiment showing the strongest and most consistent economic association among the ESG components. Sentiment-enhanced models improve out-of-sample prediction, with LSTM producing the strongest performance across the evaluated horizons. The study extends ESG and investor sentiment analysis to corporate bond markets and offers practical implications for credit risk management, bond valuation, and non-financial information use in debt-market forecasting.
  Keywords: yield forecasting; investor sentiment; ESG news sentiment; text mining; machine learning; ML; long short-term memory; LSTM.
  DOI: 10.1504/IJFMD.2026.10080559
   
  
• Prediction market efficiency: evidence from Kalshi on pricing accuracy, forecasting, and risk  
  by Tanay Subramanian 
  Abstract: Prediction markets are moving rapidly from academic curiosities to mainstream trading venues. This paper studies Kalshi, the first federally regulated US prediction exchange, using 103,003 resolved event markets across 16 categories as of November 2025. Prices are informative but uneven, since calibration is strong in some domains such as economics, and biased in others, such as entertainment. A simple nonlinear calibration model improves testing accuracy (R² ≈ 0.29; Brier ≈ 0.14), while volume-weighted payoff regressions reveal category- and horizon-specific pricing distortions linked to market frictions. Machine-learning models that treat market prices as inputs, rather than final forecasts, outperform the market baseline using only public features, reducing Brier error by roughly 30% and increasing ROC-AUC from about 0.62 to 0.78. Payoff distributions show negative average returns and positive right-tail skew, driven by outsized returns from a small fraction of contracts, demonstrating lottery-like exposure. Overall, Kalshi resembles a hybrid market combining information aggregation with preference-driven demand for skewed payoffs.
  Keywords: prediction markets; Kalshi; market efficiency; calibration; machine learning; risk premia; tail risk.
  DOI: 10.1504/IJFMD.2026.10080801
   
  
• Dynamic hedging effectiveness in the Taiwan ETF market: evidence from a DCC-GARCH model  
  by Teng-Tsai Tu, Jung-Lieh Hsiao, Chun-Chieh Wang 
  Abstract: This study aims to examine the dynamic hedging effectiveness of Taiwan ETFs market using the DCC-GARCH model. The analysis covers domestic equity ETFs, foreign equity ETFs, and bond and fixed income ETFs in Taiwan. The empirical results indicates that domestic equity ETFs generally exhibit the strongest and most stable hedging effectiveness among all ETF categories. Foreign equity ETFs display more heterogeneous hedging performance. The choice of sample setting influences hedging effectiveness. The same-trading-day setting generally produces stronger hedging performance for domestic and foreign equity ETFs. Hedging effectiveness improves when the hedging horizon is extended. In many cases, the 252-day rolling estimation window generates higher average hedging effectiveness values than the 126-day window. Finally, neither the OLS model nor the DCC-GARCH model consistently dominates across all ETF categories and market conditions. Overall, the results indicate that ETF futures provide substantial risk-reduction benefits in the Taiwan ETF market.
  Keywords: exchange-traded funds; ETFs; hedging effectiveness; optimal hedge ratio; DCC-GARCH model; ETF futures; Taiwan.
  DOI: 10.1504/IJFMD.2026.10080846