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Kladia Liquidity Deflator (KLD) is an XRPL-based, debt-indexed token whose supply dynamics respond directly to a debt index derived from macroeconomic data sources. The model links indebtedness to deterministic adjustments in issuance, burns, and escrow release caps, creating a rule-based deflationary mechanism that strengthens as debt rises. With a fixed maximum supply of 10 billion KLD, the mechanism is implemented…
We address the problem of asset pricing in a market where there is no risky asset. Previous work developed a theoretical model for a shadow riskless rate (SRR) for such a market in terms of the drift component of the state-price deflator for that asset universe. Assuming asset prices are modeled by correlated geometric Brownian motion, in this work we develop a computational approach to estimate the SRR from empirica…
Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility, allowing financial institutions to price and hedge derivatives, and banks to quantify the risk in their trading books. Additionally, most financial regulators also require a liquidity horizon of several days for institutional investors to exit their risky assets, in order to not materially affect market prices. Howev…
This paper focuses on the horse race of weekly idiosyncratic momentum (IMOM) with respect to various idiosyncratic risk metrics. Using the A-share individual stocks in the Chinese market from January 1997 to December 2017, we first evaluate the performance of the weekly momentum based on raw returns and idiosyncratic returns, respectively. After that the univariate portfolio analysis is conducted to investigate the r…
Although climate and nature related scenario analysis is increasingly important in finance, operational implementations remain limited for translating long horizon environmental scenarios into counterparty credit risk measures used in pricing and regulatory capital. We propose an environmental valuation adjustment framework for CVA with three components: (i) a scenario to credit translation that maps environmental sc…
We introduce Entropy-Guided Multiplicative Updates (EGMU), a convex optimization framework for constructing multi-factor target-exposure portfolios by minimizing Kullback-Leibler divergence from a benchmark under linear factor constraints. We establish feasibility and uniqueness of strictly positive solutions when the benchmark and targets satisfy convex-hull conditions. We derive the dual concave formulation with ex…
A reputation of high volatility accompanies the emergence of Bitcoin as a financial asset. This paper intends to nuance this reputation and clarify our understanding of Bitcoin's volatility. Using daily, weekly, and monthly closing prices and log-returns data going from September 2014 to January 2021, we find that Bitcoin is a prime example of an asset for which the two conceptions of volatility diverge. We show that…
We study markets with no riskless (safe) asset. We derive the corresponding Black-Scholes-Merton option pricing equations for markets where there are only risky assets which have the following price dynamics: (i) continuous diffusions; (ii) jump-diffusions; (iii) diffusions with stochastic volatilities, and; (iv) geometric fractional Brownian and Rosenblatt motions. No arbitrage and market completeness conditions are…
We have studied statistical characteristics of five share price time series. For each stock price, we estimated a best fit quantitative model for the monthly closing price as based on the decomposition into two defining consumer price indices selected from a large set of CPIs. It was found that there are two pairs of similar models (Bank of America/Morgan Stanley and Goldman Sachs/JPMorgan Chase) with a standalone mo…
We develop a new market-making model, from the ground up, which is tailored towards high-frequency trading under a limit order book (LOB), based on the well-known classification of order types in market microstructure. Our flexible framework allows arbitrary order volume, price jump, and bid-ask spread distributions as well as the use of market orders. It also honors the consistency of price movements upon arrivals o…
Markov decision processes (MDPs) are used to model a wide variety of applications ranging from game playing over robotics to finance. Their optimal policy typically maximizes the expected sum of rewards given at each step of the decision process. However, a large class of problems does not fit straightforwardly into this framework: Non-cumulative Markov decision processes (NCMDPs), where instead of the expected sum o…
RLHF fits a reward model to human (or AI) preference comparisons, then optimizes a language model against that reward, usually with a KL penalty back to a reference policy.
A VAE is a probabilistic autoencoder: the encoder outputs a distribution q(z|x), the decoder p(x|z), and training maximizes an ELBO with a KL term that keeps the latent well-behaved.
KL divergence KL(P‖Q) = E_P[log dP/dQ] is the expected extra log-loss from using Q when the truth is P — the loss behind cross-entropy training and many variational methods.
Fade multi-year country or regional ETF winners and buy the laggards — a slow reversal, not a weekly bounce.
KL divergence KL(P‖Q) = E_P[log dP/dQ] is the expected extra log-loss from using Q when the truth is P — the loss behind cross-entropy training and many variational methods.
RLHF fits a reward model to human (or AI) preference comparisons, then optimizes a language model against that reward, usually with a KL penalty back to a reference policy.
Fade multi-year country or regional ETF winners and buy the laggards — a slow reversal, not a weekly bounce.
A VAE is a probabilistic autoencoder: the encoder outputs a distribution q(z|x), the decoder p(x|z), and training maximizes an ELBO with a KL term that keeps the latent well-behaved.